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10 Data Warehouse Best Practices to Save Colossal Extra Costs

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Storing large data sets in a data warehouse can become expensive over a period of time. However, data warehouse best practices save organizations colossal cloud storage costs and optimize them.


Contents

1. The High Cost of Low-efficiency Data Warehousing
2. Data Warehouse Best Practices: A Blueprint to Savings
2.1 Effective Data Organization
2.2 Automation
2.3 Storage Optimization
2.4 Data Quality Assurance
2.5 Security Measures
2.6 Metadata Management
2.7 Logging
2.8 Data Flow Diagram
2.9 Change Data Capture (CDC) Policy
2.10 Agile Data Warehouse Methodology
3. The Future is Frugal: Tapping Cost-effective Data Warehousing

Inefficient data warehousing can be a silent drain on an organization's resources, necessitating the implementation of stringent data warehousing best practices. It's like a leaky faucet, slowly siphoning off valuable time and money, often going unnoticed until the damage is done. The financial implications are far-reaching, from increased storage costs to wasted resources and even the potential for costly errors.

 

1. The High Cost of Low-efficiency Data Warehousing

  • Increased Storage Costs: Inefficient data warehousing can lead to unnecessary data duplication and overlap, resulting in high storage costs.
  • Wasted Resources: Poorly managed data warehouses often consume up to 90% of the available compute capacity and 70% of the required storage space.
  • Potential for Costly Errors: Manual errors and missed updates can lead to corrupt or obsolete data, affecting data-driven decision-making and causing inaccurate data analysis.

Efficiency in data management is not just about cutting costs; it's about unlocking the full potential of the existing data. It is crucial to understand the best practices for data warehousing to save costs and aim to turn data warehouses from a cost center into a value generator.

 

2. Data Warehouse Best Practices: A Blueprint to Savings

best-prctices-infographics

 

Data warehousing is an essential aspect of business intelligence which often presents operational challenges. The tasks can be daunting, from managing vast amounts of data to ensuring data quality and security. However, by adopting best practices, these challenges can be turned into opportunities for significant cost savings. Data warehouse cost optimization drives the success of a data warehouse, mitigating the challenge of reducing data warehouse costs in the long run.

 

2.1 Effective Data Organization

  • Structured Data Modeling and Design: A well-thought-out data model organizes data effectively, enabling efficient data retrieval and supporting analytics needs.
  • Metadata Classification: By categorizing data based on metadata, organizations can significantly enhance data retrieval and organization.
  • Data Governance: Implementing a data governance framework helps define the relationships between people, processes, and technologies.
  • Data Warehouse Schema Design: A well-designed schema optimizes data retrieval and analysis and ensures that the data warehouse aligns with the business’s analytical and reporting needs.
  • Data Flow Management: Efficient management of data flow from various sources into the data warehouse is crucial for maintaining data integrity and consistency.

Effective data organization involves structuring data in a way that facilitates efficient retrieval and analysis. It requires a well-thought-out data model, effective metadata classification, robust data governance, appropriate schema design, and efficient data flow management.

 

2.2 Automation

  • ETL Automation: Automating ETL processes decreases the human labor required to build and deploy warehouses.
  • Data Integration Automation: Automating data integration ensures smooth data flow into a warehouse.
  • Data Quality Checks Automation: Implementing automated data quality checks minimizes the risk of erroneous data analysis.
  • Data Warehouse Design Automation: Modern data warehouse design tools can execute within hours, compared to months, at a fraction of the cost of manual programming.
  • Data Management Automation: Automation in data management can drastically reduce manual labor and error rates.

Data warehouse automation replaces standard methods for building data warehouses with the right data warehousing software tools. It automates the planning, modeling, and integration steps, keeping pace with an ever-increasing amount of data and sources. A data warehouse software buyer’s guide comes in handy to select the appropriate tool for data center operations.

 

2.3 Storage Optimization

  • Efficient Data Analysis: Supports complex data queries and analytics, enabling deeper insights and more effective reporting.
  • Scalability and Flexibility: It adapts easily to changing data volumes and evolving business needs.
  • Data Compression: Data compression techniques can be used to reduce the storage space required.
  • Data Partitioning: Data partitioning can improve query performance and the manageability of data.
  • Data Indexing: Proper indexing can significantly speed up data retrieval times.

Storage management and optimization in data warehousing involve techniques that improve performance and reduce storage costs.

 

2.4 Data Quality Assurance

  • Data Cleansing: This involves identifying and fixing errors, duplicates, inconsistencies, and other issues.
  • Data Validation: This ensures the accuracy, consistency, and reliability of the data stored in a warehouse.
  • Data Profiling: It entails understanding the quality of data to uncover any gaps.
  • Data Standardization: The process ensures that the data conforms to common formats and standards.
  • Continuous Monitoring: Regular monitoring of data quality is necessary to maintain high standards.

Data quality assurance involves identifying and fixing errors, duplicates, inconsistencies, and other issues. It ensures the accuracy, consistency, and reliability of the data stored in a company’s warehouse.

 

2.5 Security Measures

  • User Access Controls: This is for ensuring strict user access controls so that employees only have access to the data they need to conduct their tasks.
  • Data Encryption: This is done using highly secure encryption techniques to protect data.
  • Network Security: It takes precautions to safeguard networks where data is stored.
  • Data Migration Security: Moving data with care and consideration for the security implications of any data migration process comes under data migration security.
  • Regular Security Audits: This implies conducting regular security audits to identify potential vulnerabilities.

Security measures in data warehousing involve using a multiplicity of methods to protect assets. These include intelligent user access controls, proper categorization of information, highly secure encryption techniques, and ensuring strict access controls.

 

2.6 Metadata Management

  • Data Cataloging: This is all about maintaining a comprehensive catalog of all data assets to facilitate easy retrieval and usage.
  • Data Lineage: Data lineage allows you to trace the origin and transformation of data over its lifecycle.
  • Data Dictionary: A data dictionary is used to define the meaning, relationships, and business relevance of data elements.
  • Metadata Integration: This is essential for seamless integration of metadata across various platforms and tools.
  • Regular Metadata Updates: Regularly updating metadata is done to reflect changes in data sources and business requirements.

Metadata management in data warehousing involves the systematic organization and control of data assets. This includes maintaining a comprehensive data catalog, tracking data lineage, creating a data dictionary, and ensuring seamless metadata integration.

 

2.7 Logging

  • Activity Tracking: The activity implies monitoring user activities and transactions to maintain a record of data interactions.
  • Error Logging: Capturing and recording errors facilitates troubleshooting and improves system reliability.
  • Audit Trails: Maintaining audit trails ensures accountability and traceability of actions.
  • Log Analysis: Regularly analyzing log data helps in the identification of patterns, anomalies, and potential security threats.
  • Log Retention: Storing logs for a defined period assists in meeting compliance requirements and supports incident investigation.

Logging in data warehousing involves keeping a detailed record of activities, errors, and transactions. This includes monitoring user activities, capturing errors, maintaining audit trails, analyzing log data, and storing logs as per compliance requirements.

 

2.8 Data Flow Diagram

  • Data Sources: Data sources involve identifying and documenting the sources from which data is collected.
  • Data Transformation: The task entails mapping out the processes that modify or transform data as it moves through the system.
  • Data Storage: Data storage involves detailing where data is stored at various stages of the data lifecycle.
  • Data Usage: This illustrates how and where data is used in business processes.
  • Data Archiving: The process shows how data is archived or retired when no longer in active use.

A data flow diagram in data warehousing provides a visual representation of how data moves, transforms, and is used within the system. It includes identifying data sources, mapping data transformations, detailing data storage, illustrating data usage, and showing data archiving processes.

 

2.9 Change Data Capture (CDC) Policy

  • Understanding Data Needs: One begins the incorporation of CDC by understanding the data integration requirements.
  • Choosing the Right CDC Method: One chooses a CDC method that resonates with the requirements and specific use cases.
  • Incorporating Monitoring and Logging Processes: The process involves the implementation of proper recording and monitoring mechanisms to evaluate the quality and efficacy of the CDC tools.
  • Ensuring Real-Time Synchronization: Change data capture helps to synchronize data in a source database with a destination system as soon as a change happens.
  • Choosing the Right CDC Implementation Pattern: Depending on specific needs, one can choose from query-based CDC, trigger-based CDC, or binary log-based CDC.

These practices to implement a CDC policy help boost the efficiency of data warehousing operations, leading to significant cost savings.

 

2.10 Agile Data Warehouse Methodology

  • Model Just-in-Time (JIT): One begins the incorporation of Agile Data Warehouse Methodology by modeling details in a Just-in-Time (JIT) manner.
  • Prove the Architecture Early: The architecture is tested using code early in the process to confirm that it works.
  • Focus on Usage: One prioritizes the needs of the end-users and ensures that the data warehouse or business intelligence solution meets their actual needs.
  • Don’t Get Hung Up on “The One Truth”: One validates and reconciles different versions of the truth within an organization.
  • Organize Work by Requirements: One organizes the development work based on the requirements of the stakeholders.
  • Active Stakeholder Participation: One ensures active participation from all stakeholders. This helps in understanding their needs and expectations better.
  • Strong Collaboration: One reassures that business users and stakeholders work together effectively, as well as that automation, evolutionary modeling, and continuous integration are implemented correctly.

Agile data warehousing practices contribute to the efficiency and effectiveness of data warehousing operations, leading to significant cost savings.

Each of these best practices contributes to cost savings by reducing data management procedures and increasing overall efficiency. In the next section, learn about cost-effective data warehousing recommendations for the future. Understand how to optimize data warehousing operations further for maximum savings!

 

3. The Future is Frugal: Tapping Cost-effective Data Warehousing

Data warehousing is a crucial component of any data-driven organization. However, the cost of managing and storing vast amounts of data can be a significant pain point. But what if a company could turn this challenge into an opportunity for innovation and sustainability?

Frugality, the practice of being economical with resources, is driving significant advancements in data warehousing. Here are some key trends:

  • Cloud Dominance: The shift towards cloud-based data warehousing solutions is accelerating. These platforms offer remarkable scalability, flexibility, and cost-effectiveness.
  • Cost-effective Data Storage: Strategies like data compression, data archival, and resource management are being employed to reduce the overall cost of storing and managing data.
  • Efficient ETL Processes: Optimized ETL processes and seamless data integration ensure smooth data flow into a warehouse, reducing operational costs.

Looking ahead, it's clear that frugality will continue to shape the future of data warehousing. So, how can a company tap into these trends for a better future in data warehousing?

Firstly, organizations should consider transitioning their data warehouses to the cloud if they haven't already. The cost savings, scalability, and flexibility offered by cloud-based solutions are too significant to ignore. Secondly, they should implement cost-effective data storage strategies such as data compression and archival. Lastly, they should optimize their ETL processes for efficient data integration.

By embracing frugality, organizations are not just cutting costs; they are driving innovation and sustainability in their data warehousing operations. The future is indeed frugal!

 

Spotlight

Switchfly, Inc

Switchfly is a SaaS travel commerce & loyalty engagement platform enabling enterprises to rapidly deploy state-of-the-art online travel services to customer. Leading airlines, hotels, online travel agencies and financial service providers depend on Switchfly to power their omni-channel travel experiences because it uniquely combines a highly scalable and secure architecture with deep product and content inventory.

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Leading Data Warehouse Software Vendors That Enhance Analytics

Article | August 14, 2023

From amplified scalability to data transparency, top data warehouse software vendors boost businesses’ analysis capabilities. Learn how companies gain data agility with cloud data warehouse software. Contents 1. Understanding the Fundamentals of a Cloud Data Warehouse 2. Key Advantages of Data Warehousing for Businesses 3. Data Warehouse Strategies to Integrate and Analyze Data 4. Top Data Warehouse Software Vendors Offering Analytics Prowess 4.1 Dremio 4.2 Starburst 4.3 Firebolt 4.4 Lyftrondata 4.5 CData 4.6 Zap 4.7 Druid 4.8 TimeXtender 4.9 dbt Labs 4.10 Redwood Software 5. Final Thoughts 1. Understanding the Fundamentals of a Cloud Data Warehouse Representing a robust solution for handling and storing vast datasets, a cloud data warehouse offers businesses high convenience, quick data access, and remarkable flexibility. The data warehouse is essentially hosted in a cloud computing environment that empowers organizations to modify storage and processing as and when required without needing hardware upgrades. The growing prominence of the advanced setup today can notably be ascribed to its unparalleled accessibility, which allows companies to manage their data from any part of the world via internet access. Likewise, the warehouse’s core components include a centralized repository with distinct computing abilities, which enable scalable data retention and optimized resource allocation. Moreover, the data warehouse software boasts phenomenal data integration and handling tools, allowing businesses to link to diverse data sources, build datasets, set permissions, and run queries. It further boosts companies’ performances through parallel query processing, system backups, data encryption, etc. Moreover, supporting innovative analytics and insights, the cloud environment enables businesses to make data-backed decisions, augmenting their analytics prowess and driving high ROIs. 2. Key Advantages of Data Warehousing for Businesses From enhanced financial efficiencies to unmatched data access, data warehousing practices offer multiple benefits to businesses that allow sound decision-making and operational success. The chief merits of a data warehouse software include: Scalable Infrastructure for Unhindered Productivity With a cloud data warehouse, businesses can effortlessly adjust resources to handle diverse data processing requirements and scale up or down without bearing upgrade costs ascribed to traditional on-premises systems. By simply modifying payment schemes, companies can increase or decrease their computing power or storage, paying only for what they use. The unparalleled scalability further enables businesses to ensure undisturbed operations, faster innovations, and speed-to-market, benefiting from high agility in operations and augmenting business productivity. Quick Data Access Cloud data warehouses enhance data availability exponentially. They unify diverse data into a centralized repository, enabling users to access data quickly from anywhere in the world via web access. This flexible access, in turn, encourages remote work and collaboration endeavors, promoting real-time decision-making and augmenting operational responsiveness. Enhanced Visual Data Analysis With leading-edge business intelligence tools, advanced data warehouses support swift data processing, empowering businesses to extract crucial insights and analyze data in real time. The boosted analytics and data visualization abilities help companies make sound decisions and stay ahead of the curve. Cost-effectiveness A cloud data warehouse is a more economical substitute for on-premises systems. It works on a pay-as-you-go model, eliminating the need for physical hardware. Therefore, companies pay only for the computing resources and storage they use. Most data warehouse providers offer flexible pricing models, enabling businesses to maintain cost-efficient systems for data warehousing. Robust Security and Disaster Recovery A significant advantage of the cloud data warehouse is that it implements notable security measures like access controls, data encryption, and periodic security audits to protect sensitive business data and information from unauthorized breaches. It offers all-inclusive disaster recovery solutions, including scheduled backups and data replication across various geographic locations. Such features secure unhindered functions and data resilience in times of unanticipated interruptions. Performance Excellence Boasting features like distributed computing and parallel processing, cloud data warehouses guarantee elevated business performance as they quicken data analysis and query execution. From optimizing operations through automated tasks to eliminating the need for costly hardware investments, data warehouse setups on the cloud promise boosted productivity and efficiency. Impressive Integrations Cloud data platforms offer impressive integration capabilities, helping businesses connect with various data origins and third-party applications. Such incorporations expedite effective data reporting and analysis and augment businesses’ ability to extract valuable insights, showcasing the advantage of data warehouse integration and leading to better performance. Reliable Support and Ease-of-Use Established cloud data warehouse providers render round-the-clock support to businesses, ensuring uninterrupted operations and prompt problem resolutions. The warehouse, which comprises intuitive user interfaces, simplifies data access for non-technical teams, reducing dependency on IT staff and enabling the autonomous performance of data tasks, such as data reading, editing, and writing, through low-code or codeless platforms. 3. Data Warehouse Strategies to Integrate and Analyze Data Businesses employ various data warehousing techniques and strategies, like ETL or incremental loading, to maintain excellent quality in business processes and secure optimized performance. Here’s a breakdown of the prominent data warehouse strategies that companies leverage to derive crucial insights and gain remarkable scalability: Effective Metadata Categorization With strong metadata categorization, businesses can enhance data extraction and organization, simplifying data queries and augmenting accessibility. The centralized metadata management further documents data usage, lineage, and definitions, facilitating efficient data governance. The strategy helps stakeholders easily find and understand the required data, ensures compliance with data use regulations, and promotes data transparency and availability within the company. ETL Technique for Data Integration Through the ETL processes, i.e., extract, transform, load, data can be retrieved from diverse sources, transformed to match the warehouse schema, and loaded into the target system or data warehouse. This strategy is pivotal for incorporating data into the setup. It effectively streamlines data flows and allows businesses to conduct impactful data analysis. Data Segmentation and Indexing Also called ‘partitioning’, data segmentation involves dividing vast tables into easy-to-manage segments, maximizing data extraction, storage, and management. Each of these segments or ‘partitions’, therefore, holds a subset of the large table’s data, divided according to established criteria, like hash keys or value ranges. Indexing, likewise, allows speedy data retrieval based on specific criteria, as it creates systematized lists of the table’s fundamental values. Collectively, these promising strategies optimize query processing and database management, ensuring responsiveness with increasing data volumes. Data Modeling Strategy Data can be organized effectively for optimum querying by leveraging techniques like star or snowflake schemas. For instance, measurable data like sales can be stored in fact tables, while attributes like time or location are stored in dimension tables. Hence, the data modeling strategy makes it simpler to ask intricate questions about data, boosting performance and enabling easy-to-understand data analysis and exploration. Incremental Loading for Quicker Data Processing Another prominent data warehousing strategy is incremental loading. This strategy involves speeding up information handling processes by only making changes since the last data update. While the previous data remains unchanged, only new data gets added since the last load. Therefore, the approach reduces the time and resources needed for updating large datasets, promptly making the latest data available. It further strengthens real-time data analytics, where information changes regularly, allowing analysts to access the most recent information. Data Protection Implementing strong data security and governance is a critical data warehousing strategy. This strategy includes measures like role-based access control (RBAC), which limits who views the data. Such measures ensure high data integrity and safeguard it against unauthorized breaches or access. Furthermore, compliance with data laws and rules ensures that sensitive information is protected and best practices are adhered to. Query Enhancement The query optimization technique includes effectively organizing queries or questions so that maximum benefits may be derived from partitions, indexes, and materialized views that store precomputed results. The strategy benefits businesses by expediting query processing, allowing users to promptly get their answers, enhancing user experience, and facilitating complex analytical processes. Data Quality Assurance A prominent data warehousing strategy, data quality assurance relates to maintaining data accuracy, reliability, and conciseness. This involves various crucial measures, such as data examination, validation, and cleaning, which aim to mitigate errors that can influence analysis and decision-making. Such measures not only promise dependable findings but also result in optimal decisions. Data Presentation and Reporting A cloud data warehouse helps users make data-backed decisions and build their own dashboards and reports by converting complex data into valuable insights. Therefore, data presentation and reporting as a strategy help companies leverage business intelligence to ensure sound decisions and goal attainment. 4. Top Data Warehouse Software Vendors Offering Analytics Prowess Empowering businesses with spectacular analytics capabilities, the leading data warehouse software vendors ensure smarter decisions for companies. Here’s a list of the leading data warehouse software vendors promising robust analytics capabilities to businesses: 4.1 Dremio Dremio is an open, self-service SQL analytics platform provider that expedites time to insight, incorporating data lake adaptability with data warehouse performance. Relied on by leading organizations, the company empowers businesses by rendering seamless BI capabilities and allowing unified analytics across varied environments, like on-premises, hybrid, and cloud, for reduced costs. Furthermore, Dremio significantly boosts data management and ensures optimized data functions by streamlining data integration processes. The company facilitates the transformation of Hadoop workloads for sub-second queries by offering a Unified Lakehouse Platform. Dremio’s impressive features include its support for Apache Iceberg, which enables effective data handling with version control. It also can establish distributed data architectures, securing relevant data delivery across hybrid and cloud environments. From reducing ETL complexity to supporting AI endeavors with speedier data access, the company’s striking platform promises simplified data management and supports frictionless self-service analytics directly on data lakes via SQL Query Engine. 4.2 Starburst Starburst is a pioneering data warehouse software vendor that offers fast and adjustable data access solutions, leveraging the power of Trino, a premier SQL analytics engine. Renowned for its enterprise-grade reliability and promising a high-performance data lakehouse solution, the company aims to help customers overcome expensive, rigid, and slow data access constraints. The company’s platform effectively overcomes data silos problems, supports near real-time analytics, and adds to Trino’s capabilities by including tools to connect with diverse data sources, providing 24/7 support, and ensuring robust security. Starburst optimizes data management across traditional (on-premises) and cloud environments through its comprehensive products and features. For instance, the Starburst Galaxy multi-cloud platform allows users to leverage data warehouse-like performance and flexibility. Providing high-efficiency SQL queries directly on the data lake and built-in components for cluster management, data governance, etc., it allows businesses to optimize data analytics processes. Moreover, the Starburst Enterprise platform empowers organizations to link to any data source, employing advanced analytics tools for effective data analysis without data movement. The company notably supports AI workloads at vast scales and provides resilient query processing across multiple data sources, enhancing functional productivity. 4.3 Firebolt Founded in 2019, Firebolt has emerged as a global leader in the cloud data warehouse field and aims to broaden access to robust data analysis tools for more users. Promoting a culture of transparency, responsibility, and customer obsession, the company delivers a leading-edge cloud-based data warehouse platform that incorporates the swiftness of a query accelerator and the ability to handle large volumes of information like a traditional data warehouse. Imperatively, the company strives to empower businesses by streamlining data engineering, amplifying performance, and reducing costs. The company renders spectacular features, transforming cloud data warehousing with low-latency analytics for high concurrency and more agile query responses. With components like distributed multi-threading, data processing in arrays or batches, and an innovative optimizer for more effective execution plans, Firebolt facilitates agile and accurate data operations. The company further ensures ACID compliance, securing data integrity and reliability in database transactions. Additionally, it allows scaling both horizontally and vertically, enabling businesses to handle varying data volumes. Furthermore, the company’s platform encourages active collaborations and remains prepared for continuous integration and continuous deployment practices, supporting automated testing and frictionless updates to data pipelines. 4.4 Lyftrondata Providing a leading-edge data fabric platform, Lyftrondata empowers companies to convert data silos into valuable insights with promptness. Its self-service, agile data-delivery platform includes prominent offerings, such as Data Virtualization, a Managed Warehouse supported by Snowflake, a Data Pipeline, an API Data Hub, a Data Catalog, etc. The company facilitates effortless data integration across varied sources to expedite business insights, leveraging more than 300 connectors and further ensuring HIPAA compliance. Lyftrondata provides a unified environment, allowing users of different technical abilities to manage ETL processes seamlessly. From automating data retrieval to transforming complex data into actionable insights, the company’s forward-thinking platform eliminates manual coding. It employs flexible technologies, such as Kafka streaming and Apache Spark, to ensure easy automation for data integration and processing. The company boasts one of the best data warehouse solutions, empowering users to carry out large-scale analytics. It further leverages data virtualization to speed up live analytics and optimizes data governance, securing data privacy and adhering to enterprise-level security standards. 4.5 CData Standing at the forefront of data access and connectivity solutions, CData aims to simplify data connection processes for organizations and applications in today’s business world, where data is spread out in multiple locations. With real-time connectivity solutions, the company, hence, breaks down and converts data silos, allowing easy access, integration, and sharing of data. The company’s platform boosts businesses’ efficiency by standardizing interactions for operational consistency and optimizes performance at the socket level. It further supports two modes of integration, replicated and live data, and renders real-time data access to users across varied enterprise applications like ERP or CRM systems. CData’s remarkable offerings include CData Virtuality, which enables companies to get immediate access to over 200 data sources and leverage enterprise data virtualization through a consolidated interface to utilize data from varied origins. It further optimizes query performance with caching mechanisms. Another prominent tool, CData Sync, simplifies the creation and implementation of ETL or ELT data pipelines, facilitating the multi-environment deployment of data integration techniques and strategies, offering a cost-effective approach to data replication with its connector-based pricing model, and supporting a broad selection of data origins. Through secure, centralized data management, CData reduces data breaches, backs extensive integrations, and offers self-service connectivity, enhancing decision-making. 4.6 Zap Streamlining data handling for businesses, Zap efficiently integrates data from diverse sources, enriches it, and ensures that it is prepared for analysis. With its innovative Power BI analytic tool or patented Data Hub Analytics, Zap empowers companies to handle their functional and financial analytics requirements effortlessly. The company further leverages pre-defined data models enhanced with specialized knowledge to expedite the data preparation processes and attain actionable insights. The platform’s users can choose between Power BI or the proprietary Zap Data Hub Analytics for all-inclusive business and financial analysis, while Zap augments their capabilities through extensive training. Among the chief features of the company’s platform remain the advanced data modeling options, which include pre-built solutions for ERP systems and a codeless UI with SQL abilities. From boosting analytics with self-detected measures through semantic layer modeling to offering user-friendly dashboards and printable reports, Zap ensures exponential improvement in data and decision-making processes for businesses. 4.7 Druid Catalyzing powerful digital transformation, Druid is an ingenious technology company that specializes in data and analytics. The company employs advanced technologies like artificial intelligence, machine learning, and IoT to help businesses streamline and monitor their operational processes. The company’s services include IoT analytics, serverless frameworks, digital acceleration, and complex integrations. In addition to providing thorough data analysis, Druid further facilitates cloud migration for businesses, amplifying their agility, resource management, and operational prowess. It effectively supports incorporating and managing IoT solutions in the cloud and boasts commendable expertise in designing big data architecture. The company’s promising platform renders intelligent insights to businesses by integrating AI and ML technologies, ensuring boosted productivity and strategic growth. Importantly, Druid’s platform promises agile solutions for businesses, integrating via APIs, optimizing IT project lifecycles, and emphasizing digital advancement. 4.8 TimeXtender TimeXtender embraces innovation and sustainability to streamline data integration techniques and strategies for businesses. Leveraging metadata-driven solutions, it empowers companies to make strategically sound decisions, establishes benchmarks, and keeps a focus on both social and environmental impact. With its powerful and responsive data infrastructure, the company optimizes AI and analytics endeavors of businesses, enabling them to maximize the utmost potential of data. The core functionalities offered by TimeXtender include Master Data Management, which automates data correction, creates hierarchical relationships among critical data entities, and handles reference data, augmenting data governance capabilities. Its platform further ensures excellent data integration, supporting more than 250 pre-configured connectors, entailing incremental loading, and reducing processing times. Furthermore, with its premier Unified Metadata Framework, it facilitates automated code generation for consistent data workflows and supports a shared semantic layer for congruous understanding and usage of data across different departments. Not to mention, the company amplifies data accuracy with its striking data quality management tools for continual evaluation and easy troubleshooting. 4.9 dbt Labs A leader in the data development field, dbt Labs empowers data analysts to amplify data product delivery, ensuring preciseness and reliability in results. Trusted by industry leaders, the company strives to limit the use of dated tools to elevate data confidence and faster operations. From encouraging self-serve data analysis to superlative data oversight, the company stays resolute in its commitment to limit costs and optimize data models. With its robust end-to-end platform, dbt Cloud, it adopts an SQL-first workflow and empowers teams to handle intricate data tasks effortlessly, following best practices in data development and securing high-quality data delivery. From enabling the creation of modular analytics code to automated code testing, the advanced platform ensures result accuracy. It further boasts impressive documentation capabilities, promotes uniform metrics with dbt Semantic Layer, incorporates Git-enabled version control for uniformity, and provides robust security with advanced features. Moreover, offering dbt Explorer, it lets users visualize data transformation lineage, fostering trust, transparency, and reliability in data-backed decisions. 4.10 Redwood Software Redwood Software is a renowned automation platform provider that empowers businesses to automate their processes securely across on-premises and cloud environments. Focused on augmenting enterprise productivity, the company provides impressive cloud-native solutions, allowing integrations for ERP systems and incorporating REST web services via user-friendly API wizards. Serving as a cost-effective choice for businesses’ automation requirements, it provides a consumption-based or pay-as-you-go pricing model to mitigate financial risks and support budget management. The company’s leading-edge platform, RunMyJobs, offers comprehensive features for data warehouse management, allowing businesses to extract valuable insights from big data across departments. 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Article | August 1, 2023

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Article | August 8, 2023

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Top 15 Cloud Security Certifications to Grow Your Career in Cloud Security

Article | August 14, 2023

From amplified scalability to data transparency, top data warehouse software vendors boost businesses’ analysis capabilities. Learn how companies gain data agility with cloud data warehouse software. Contents 1. Understanding the Fundamentals of a Cloud Data Warehouse 2. Key Advantages of Data Warehousing for Businesses 3. Data Warehouse Strategies to Integrate and Analyze Data 4. Top Data Warehouse Software Vendors Offering Analytics Prowess 4.1 Dremio 4.2 Starburst 4.3 Firebolt 4.4 Lyftrondata 4.5 CData 4.6 Zap 4.7 Druid 4.8 TimeXtender 4.9 dbt Labs 4.10 Redwood Software 5. Final Thoughts 1. Understanding the Fundamentals of a Cloud Data Warehouse Representing a robust solution for handling and storing vast datasets, a cloud data warehouse offers businesses high convenience, quick data access, and remarkable flexibility. The data warehouse is essentially hosted in a cloud computing environment that empowers organizations to modify storage and processing as and when required without needing hardware upgrades. The growing prominence of the advanced setup today can notably be ascribed to its unparalleled accessibility, which allows companies to manage their data from any part of the world via internet access. Likewise, the warehouse’s core components include a centralized repository with distinct computing abilities, which enable scalable data retention and optimized resource allocation. Moreover, the data warehouse software boasts phenomenal data integration and handling tools, allowing businesses to link to diverse data sources, build datasets, set permissions, and run queries. It further boosts companies’ performances through parallel query processing, system backups, data encryption, etc. Moreover, supporting innovative analytics and insights, the cloud environment enables businesses to make data-backed decisions, augmenting their analytics prowess and driving high ROIs. 2. Key Advantages of Data Warehousing for Businesses From enhanced financial efficiencies to unmatched data access, data warehousing practices offer multiple benefits to businesses that allow sound decision-making and operational success. The chief merits of a data warehouse software include: Scalable Infrastructure for Unhindered Productivity With a cloud data warehouse, businesses can effortlessly adjust resources to handle diverse data processing requirements and scale up or down without bearing upgrade costs ascribed to traditional on-premises systems. By simply modifying payment schemes, companies can increase or decrease their computing power or storage, paying only for what they use. The unparalleled scalability further enables businesses to ensure undisturbed operations, faster innovations, and speed-to-market, benefiting from high agility in operations and augmenting business productivity. Quick Data Access Cloud data warehouses enhance data availability exponentially. They unify diverse data into a centralized repository, enabling users to access data quickly from anywhere in the world via web access. This flexible access, in turn, encourages remote work and collaboration endeavors, promoting real-time decision-making and augmenting operational responsiveness. Enhanced Visual Data Analysis With leading-edge business intelligence tools, advanced data warehouses support swift data processing, empowering businesses to extract crucial insights and analyze data in real time. The boosted analytics and data visualization abilities help companies make sound decisions and stay ahead of the curve. Cost-effectiveness A cloud data warehouse is a more economical substitute for on-premises systems. It works on a pay-as-you-go model, eliminating the need for physical hardware. Therefore, companies pay only for the computing resources and storage they use. Most data warehouse providers offer flexible pricing models, enabling businesses to maintain cost-efficient systems for data warehousing. Robust Security and Disaster Recovery A significant advantage of the cloud data warehouse is that it implements notable security measures like access controls, data encryption, and periodic security audits to protect sensitive business data and information from unauthorized breaches. It offers all-inclusive disaster recovery solutions, including scheduled backups and data replication across various geographic locations. Such features secure unhindered functions and data resilience in times of unanticipated interruptions. Performance Excellence Boasting features like distributed computing and parallel processing, cloud data warehouses guarantee elevated business performance as they quicken data analysis and query execution. From optimizing operations through automated tasks to eliminating the need for costly hardware investments, data warehouse setups on the cloud promise boosted productivity and efficiency. Impressive Integrations Cloud data platforms offer impressive integration capabilities, helping businesses connect with various data origins and third-party applications. Such incorporations expedite effective data reporting and analysis and augment businesses’ ability to extract valuable insights, showcasing the advantage of data warehouse integration and leading to better performance. Reliable Support and Ease-of-Use Established cloud data warehouse providers render round-the-clock support to businesses, ensuring uninterrupted operations and prompt problem resolutions. The warehouse, which comprises intuitive user interfaces, simplifies data access for non-technical teams, reducing dependency on IT staff and enabling the autonomous performance of data tasks, such as data reading, editing, and writing, through low-code or codeless platforms. 3. Data Warehouse Strategies to Integrate and Analyze Data Businesses employ various data warehousing techniques and strategies, like ETL or incremental loading, to maintain excellent quality in business processes and secure optimized performance. Here’s a breakdown of the prominent data warehouse strategies that companies leverage to derive crucial insights and gain remarkable scalability: Effective Metadata Categorization With strong metadata categorization, businesses can enhance data extraction and organization, simplifying data queries and augmenting accessibility. The centralized metadata management further documents data usage, lineage, and definitions, facilitating efficient data governance. The strategy helps stakeholders easily find and understand the required data, ensures compliance with data use regulations, and promotes data transparency and availability within the company. ETL Technique for Data Integration Through the ETL processes, i.e., extract, transform, load, data can be retrieved from diverse sources, transformed to match the warehouse schema, and loaded into the target system or data warehouse. This strategy is pivotal for incorporating data into the setup. It effectively streamlines data flows and allows businesses to conduct impactful data analysis. Data Segmentation and Indexing Also called ‘partitioning’, data segmentation involves dividing vast tables into easy-to-manage segments, maximizing data extraction, storage, and management. Each of these segments or ‘partitions’, therefore, holds a subset of the large table’s data, divided according to established criteria, like hash keys or value ranges. Indexing, likewise, allows speedy data retrieval based on specific criteria, as it creates systematized lists of the table’s fundamental values. Collectively, these promising strategies optimize query processing and database management, ensuring responsiveness with increasing data volumes. Data Modeling Strategy Data can be organized effectively for optimum querying by leveraging techniques like star or snowflake schemas. For instance, measurable data like sales can be stored in fact tables, while attributes like time or location are stored in dimension tables. Hence, the data modeling strategy makes it simpler to ask intricate questions about data, boosting performance and enabling easy-to-understand data analysis and exploration. Incremental Loading for Quicker Data Processing Another prominent data warehousing strategy is incremental loading. This strategy involves speeding up information handling processes by only making changes since the last data update. While the previous data remains unchanged, only new data gets added since the last load. Therefore, the approach reduces the time and resources needed for updating large datasets, promptly making the latest data available. It further strengthens real-time data analytics, where information changes regularly, allowing analysts to access the most recent information. Data Protection Implementing strong data security and governance is a critical data warehousing strategy. This strategy includes measures like role-based access control (RBAC), which limits who views the data. Such measures ensure high data integrity and safeguard it against unauthorized breaches or access. Furthermore, compliance with data laws and rules ensures that sensitive information is protected and best practices are adhered to. Query Enhancement The query optimization technique includes effectively organizing queries or questions so that maximum benefits may be derived from partitions, indexes, and materialized views that store precomputed results. The strategy benefits businesses by expediting query processing, allowing users to promptly get their answers, enhancing user experience, and facilitating complex analytical processes. Data Quality Assurance A prominent data warehousing strategy, data quality assurance relates to maintaining data accuracy, reliability, and conciseness. This involves various crucial measures, such as data examination, validation, and cleaning, which aim to mitigate errors that can influence analysis and decision-making. Such measures not only promise dependable findings but also result in optimal decisions. Data Presentation and Reporting A cloud data warehouse helps users make data-backed decisions and build their own dashboards and reports by converting complex data into valuable insights. Therefore, data presentation and reporting as a strategy help companies leverage business intelligence to ensure sound decisions and goal attainment. 4. Top Data Warehouse Software Vendors Offering Analytics Prowess Empowering businesses with spectacular analytics capabilities, the leading data warehouse software vendors ensure smarter decisions for companies. Here’s a list of the leading data warehouse software vendors promising robust analytics capabilities to businesses: 4.1 Dremio Dremio is an open, self-service SQL analytics platform provider that expedites time to insight, incorporating data lake adaptability with data warehouse performance. Relied on by leading organizations, the company empowers businesses by rendering seamless BI capabilities and allowing unified analytics across varied environments, like on-premises, hybrid, and cloud, for reduced costs. Furthermore, Dremio significantly boosts data management and ensures optimized data functions by streamlining data integration processes. The company facilitates the transformation of Hadoop workloads for sub-second queries by offering a Unified Lakehouse Platform. Dremio’s impressive features include its support for Apache Iceberg, which enables effective data handling with version control. It also can establish distributed data architectures, securing relevant data delivery across hybrid and cloud environments. From reducing ETL complexity to supporting AI endeavors with speedier data access, the company’s striking platform promises simplified data management and supports frictionless self-service analytics directly on data lakes via SQL Query Engine. 4.2 Starburst Starburst is a pioneering data warehouse software vendor that offers fast and adjustable data access solutions, leveraging the power of Trino, a premier SQL analytics engine. Renowned for its enterprise-grade reliability and promising a high-performance data lakehouse solution, the company aims to help customers overcome expensive, rigid, and slow data access constraints. The company’s platform effectively overcomes data silos problems, supports near real-time analytics, and adds to Trino’s capabilities by including tools to connect with diverse data sources, providing 24/7 support, and ensuring robust security. Starburst optimizes data management across traditional (on-premises) and cloud environments through its comprehensive products and features. For instance, the Starburst Galaxy multi-cloud platform allows users to leverage data warehouse-like performance and flexibility. Providing high-efficiency SQL queries directly on the data lake and built-in components for cluster management, data governance, etc., it allows businesses to optimize data analytics processes. Moreover, the Starburst Enterprise platform empowers organizations to link to any data source, employing advanced analytics tools for effective data analysis without data movement. The company notably supports AI workloads at vast scales and provides resilient query processing across multiple data sources, enhancing functional productivity. 4.3 Firebolt Founded in 2019, Firebolt has emerged as a global leader in the cloud data warehouse field and aims to broaden access to robust data analysis tools for more users. Promoting a culture of transparency, responsibility, and customer obsession, the company delivers a leading-edge cloud-based data warehouse platform that incorporates the swiftness of a query accelerator and the ability to handle large volumes of information like a traditional data warehouse. Imperatively, the company strives to empower businesses by streamlining data engineering, amplifying performance, and reducing costs. The company renders spectacular features, transforming cloud data warehousing with low-latency analytics for high concurrency and more agile query responses. With components like distributed multi-threading, data processing in arrays or batches, and an innovative optimizer for more effective execution plans, Firebolt facilitates agile and accurate data operations. The company further ensures ACID compliance, securing data integrity and reliability in database transactions. Additionally, it allows scaling both horizontally and vertically, enabling businesses to handle varying data volumes. Furthermore, the company’s platform encourages active collaborations and remains prepared for continuous integration and continuous deployment practices, supporting automated testing and frictionless updates to data pipelines. 4.4 Lyftrondata Providing a leading-edge data fabric platform, Lyftrondata empowers companies to convert data silos into valuable insights with promptness. Its self-service, agile data-delivery platform includes prominent offerings, such as Data Virtualization, a Managed Warehouse supported by Snowflake, a Data Pipeline, an API Data Hub, a Data Catalog, etc. The company facilitates effortless data integration across varied sources to expedite business insights, leveraging more than 300 connectors and further ensuring HIPAA compliance. Lyftrondata provides a unified environment, allowing users of different technical abilities to manage ETL processes seamlessly. From automating data retrieval to transforming complex data into actionable insights, the company’s forward-thinking platform eliminates manual coding. It employs flexible technologies, such as Kafka streaming and Apache Spark, to ensure easy automation for data integration and processing. The company boasts one of the best data warehouse solutions, empowering users to carry out large-scale analytics. It further leverages data virtualization to speed up live analytics and optimizes data governance, securing data privacy and adhering to enterprise-level security standards. 4.5 CData Standing at the forefront of data access and connectivity solutions, CData aims to simplify data connection processes for organizations and applications in today’s business world, where data is spread out in multiple locations. With real-time connectivity solutions, the company, hence, breaks down and converts data silos, allowing easy access, integration, and sharing of data. The company’s platform boosts businesses’ efficiency by standardizing interactions for operational consistency and optimizes performance at the socket level. It further supports two modes of integration, replicated and live data, and renders real-time data access to users across varied enterprise applications like ERP or CRM systems. CData’s remarkable offerings include CData Virtuality, which enables companies to get immediate access to over 200 data sources and leverage enterprise data virtualization through a consolidated interface to utilize data from varied origins. It further optimizes query performance with caching mechanisms. Another prominent tool, CData Sync, simplifies the creation and implementation of ETL or ELT data pipelines, facilitating the multi-environment deployment of data integration techniques and strategies, offering a cost-effective approach to data replication with its connector-based pricing model, and supporting a broad selection of data origins. Through secure, centralized data management, CData reduces data breaches, backs extensive integrations, and offers self-service connectivity, enhancing decision-making. 4.6 Zap Streamlining data handling for businesses, Zap efficiently integrates data from diverse sources, enriches it, and ensures that it is prepared for analysis. With its innovative Power BI analytic tool or patented Data Hub Analytics, Zap empowers companies to handle their functional and financial analytics requirements effortlessly. The company further leverages pre-defined data models enhanced with specialized knowledge to expedite the data preparation processes and attain actionable insights. The platform’s users can choose between Power BI or the proprietary Zap Data Hub Analytics for all-inclusive business and financial analysis, while Zap augments their capabilities through extensive training. Among the chief features of the company’s platform remain the advanced data modeling options, which include pre-built solutions for ERP systems and a codeless UI with SQL abilities. From boosting analytics with self-detected measures through semantic layer modeling to offering user-friendly dashboards and printable reports, Zap ensures exponential improvement in data and decision-making processes for businesses. 4.7 Druid Catalyzing powerful digital transformation, Druid is an ingenious technology company that specializes in data and analytics. The company employs advanced technologies like artificial intelligence, machine learning, and IoT to help businesses streamline and monitor their operational processes. The company’s services include IoT analytics, serverless frameworks, digital acceleration, and complex integrations. In addition to providing thorough data analysis, Druid further facilitates cloud migration for businesses, amplifying their agility, resource management, and operational prowess. It effectively supports incorporating and managing IoT solutions in the cloud and boasts commendable expertise in designing big data architecture. The company’s promising platform renders intelligent insights to businesses by integrating AI and ML technologies, ensuring boosted productivity and strategic growth. Importantly, Druid’s platform promises agile solutions for businesses, integrating via APIs, optimizing IT project lifecycles, and emphasizing digital advancement. 4.8 TimeXtender TimeXtender embraces innovation and sustainability to streamline data integration techniques and strategies for businesses. Leveraging metadata-driven solutions, it empowers companies to make strategically sound decisions, establishes benchmarks, and keeps a focus on both social and environmental impact. With its powerful and responsive data infrastructure, the company optimizes AI and analytics endeavors of businesses, enabling them to maximize the utmost potential of data. The core functionalities offered by TimeXtender include Master Data Management, which automates data correction, creates hierarchical relationships among critical data entities, and handles reference data, augmenting data governance capabilities. Its platform further ensures excellent data integration, supporting more than 250 pre-configured connectors, entailing incremental loading, and reducing processing times. Furthermore, with its premier Unified Metadata Framework, it facilitates automated code generation for consistent data workflows and supports a shared semantic layer for congruous understanding and usage of data across different departments. Not to mention, the company amplifies data accuracy with its striking data quality management tools for continual evaluation and easy troubleshooting. 4.9 dbt Labs A leader in the data development field, dbt Labs empowers data analysts to amplify data product delivery, ensuring preciseness and reliability in results. Trusted by industry leaders, the company strives to limit the use of dated tools to elevate data confidence and faster operations. From encouraging self-serve data analysis to superlative data oversight, the company stays resolute in its commitment to limit costs and optimize data models. With its robust end-to-end platform, dbt Cloud, it adopts an SQL-first workflow and empowers teams to handle intricate data tasks effortlessly, following best practices in data development and securing high-quality data delivery. From enabling the creation of modular analytics code to automated code testing, the advanced platform ensures result accuracy. It further boasts impressive documentation capabilities, promotes uniform metrics with dbt Semantic Layer, incorporates Git-enabled version control for uniformity, and provides robust security with advanced features. Moreover, offering dbt Explorer, it lets users visualize data transformation lineage, fostering trust, transparency, and reliability in data-backed decisions. 4.10 Redwood Software Redwood Software is a renowned automation platform provider that empowers businesses to automate their processes securely across on-premises and cloud environments. Focused on augmenting enterprise productivity, the company provides impressive cloud-native solutions, allowing integrations for ERP systems and incorporating REST web services via user-friendly API wizards. Serving as a cost-effective choice for businesses’ automation requirements, it provides a consumption-based or pay-as-you-go pricing model to mitigate financial risks and support budget management. The company’s leading-edge platform, RunMyJobs, offers comprehensive features for data warehouse management, allowing businesses to extract valuable insights from big data across departments. The platform ensures proactive data management by enabling automated data extraction from any database and enhancing live data pipeline monitoring via a unified interface. The platform further emphasizes security and optimizes resource management with dynamic load balancing. Such an approach allows strong data governance and promises precision and agility in data processes. 5. Final Thoughts Cloud data warehouses have transformed data processes, strengthening businesses with prompt data access, spectacular analytics capabilities, cost-effectiveness, and unmatched scalability. By partnering with the leading data warehouse software providers, companies today leverage advanced tools to attain crucial insights, drive intelligent decision-making, and achieve operational excellence. With the continual adoption of these advanced technologies, the future of cloud data warehouses is poised to witness continued incorporation of machine learning and artificial intelligence in cloud data warehousing processes. This would effectively automate data-handling tasks and augment predictive analytics for business implementation. The advancement would further witness an inclination toward containerized implementation, exhaustive data governance features, and deployment of microservices architecture. Such changes would equip companies with leading-edge tools to retrieve deeper data insights, enhance innovation, and maximize profitability.

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Cloud Security, Cloud App Management, Cloud Infrastructure Management

Top 10 Cloud Security Management Tools for 2023

Article | August 1, 2023

Continuous intelligence (CI) relies on the real-time analysis of streaming data to produce actionable insights in milliseconds to seconds. Such capabilities have applications throughout a business. In today’s dynamic marketplace, new CI applications that use data from various sources at any given time might be needed on very short notice.The challenge is how to have the flexibility to rapidly develop and deploy new CI applications to meet fast-changing business requirements. A common approach employed today is to use a dynamic architecture that delivers access to data, processing power, and analytics capabilities on demand. In the future, solutions also will likely incorporate artificial intelligence applications to complement the benefits of traditional analytics. Increasingly, cloud-native is the architecture of choice to build and deploy AI-embedded CI applications. A cloud-native approach offers benefits to both the business and developers. Cloud-native applications or services are loosely coupled with explicitly described dependencies.

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Cloud Security, Cloud App Management, Cloud Infrastructure Management

10 New Cloud Security Trends for Better Technology Management

Article | August 8, 2023

Microsoft currently features old-school solutions that are growing relatively slowly (Office and Windows) and new cloud solutions that are growing tremendously (Dynamics 365 and Azure). If the company stays in its current form, Microsoft stock will keep steadily advancing. But because the company’s total top and bottom lines are never going to increase much more than 30% or 35% per year, the shares are never going to deliver truly huge returns. But that would change if the company was to spin off its rapidly growing cloud businesses. In such a scenario, the current owners of Microsoft stock would receive shares in a cutting edge cloud services company (let’s call it Azure), and shares in a company focused on providing old, mostly PC-based software to businesses and consumers.

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Intelligence Giant Upgrading its Cloud Technology

Article | February 10, 2020

With the huge amounts of data in all fields, a future in the cloud is imperative to help deal with this explosion of data, especially in the field of intelligence technology. This is the reason why the US Central Intelligence Agency is updating its cloud technology. The agency has recently released a draft request for proposal for its Commercial Cloud Enterprise contract.The C2E tens of billions contract will be a multi-award commercial cloud computing contract with a five-year base period and two five-year options for a period of performance of up to 15 years, according to nextgov.com.In a March 2019 presentation by the Directorate of Digital Innovation, a division of the CIA, the department outlined its vision for C2E. It would be broad and include infrastructure, platform and software cloud services supporting a broad range of users, with a variety of security clearances and a worldwide presence, as reported by techcrunch.com. The principal C2E Program objective is to acquire cloud computing services directly from commercial cloud service providers with established records for innovation and operational excellence in cloud service delivery for a large customer base,the department stated in the presentation.Apparently, the agency prefers to avoid all the attention that the Pentagon’s JEDI cloud procurement process got, and quietly go about its business.

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The Manufacturing Sector Experiences More Attacks in the Cloud than Any Other Industry

PR Newswire: | January 19, 2024

Netwrix, a cybersecurity vendor that makes data security easy, today revealed additional findings for the manufacturing sector from its survey of 1,610 IT and security professionals across more than 100 countries. According to the survey, 64% of companies in the manufacturing sector suffered a cyberattack during the preceding 12 months, which is similar to the finding among organizations overall (68%). However, it turned out that the manufacturing sector experiences more cloud infrastructure attacks than any other industry surveyed. Among manufacturing companies that detected an attack, 85% spotted phishing in the cloud compared to only 58% across all verticals; 43% faced user account compromise in the cloud as opposed to 27% among all industries; and 25% dealt with data theft by hackers in the cloud compared to 15% for organizations overall. "The manufacturing sector relies heavily on the cloud to work with their supply chain in real time. This makes their cloud infrastructure a lucrative target for attackers — infiltrating it enables them to move laterally and potentially compromise other linked organizations, as happened to one the world's top meat processing companies. Credential compromise or malware deployed via a phishing email is just the beginning of the attack," says Dirk Schrader, VP of Security Research at Netwrix. "The attack surface in the cloud is always expanding, so it's critical for manufacturing companies to adopt a defense-in-depth approach," adds Ilia Sotnikov, Security Strategist at Netwrix. "First, they must rigorously enforce the principle of least privilege to limit access to sensitive data, which ideally includes just-in-time access to eliminate unnecessary entry points for adversaries. They also need to gain deep visibility into when and how critical data in the cloud is being used so that IT teams can promptly spot potential threats. Finally, they need to be prepared to minimize the damage from incidents by having a comprehensive response strategy that is regularly exercised and updated." To learn more about security trends, check out the complete 2023 Hybrid Security Trends Report. About Netwrix Netwrix makes data security easy. Since 2006, Netwrix solutions have been simplifying the lives of security professionals by enabling them to identify and protect sensitive data to reduce the risk of a breach, and to detect, respond to and recover from attacks, limiting their impact. More than 13,500 organizations worldwide rely on Netwrix solutions to strengthen their security and compliance posture across all three primary attack vectors: data, identity and infrastructure.

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TRG Screen Announces Acquisition of Xpansion for Reference Data Usage Management

PR Newswire: | January 25, 2024

TRG Screen, the leading provider of enterprise subscription spend and usage management software, today announced it has acquired Xpansion, the leading provider of cloud-based solutions for reference data usage monitoring in the financial services industry. The acquisition of Xpansion will further solidify TRG Screen's position as a global market leader in market data management solutions. Xpansion – established in 2013 – is focused on empowering data operations teams to proactively manage their usage, control costs and optimize data workflows. Xpansion's offerings include Xmon, Xprocess and Xplore, and provide real-time analytics, giving clients unprecedented transparency, visibility and control into their reference data usage. This deal consolidates TRG Screen's unique position as the only provider of enterprise subscription management capabilities spanning the whole spectrum of market data optimization, from spend and inventory tracking, through to usage and enquiry management, exchange reporting and compliance. "Xpansion and TRG Screen have been partners for many years. Bringing Xpansion into the TRG Screen family is a very logical next step for both companies, given our strong relationship and shared view that the industry demand for integrated usage management solutions is going to continue to grow," said TRG Screen CEO Leigh Walters. "Xpansion is an established firm with excellent customer satisfaction and retention, and highly experienced and industry respected leadership. We are very excited at the opportunities this acquisition brings." "We are thrilled to be joining TRG Screen," said Xpansion co-founder and CEO Amjad Zoghbi. "Reference data usage is one of the most complex aspects of market data management, and managing it correctly is essential to maintaining contractual compliance and ensuring clients are right-sizing their usage based on actual consumption and business need. I'm very pleased that Xpansion's customers, and team, will now be part of the best-of-breed solution with the industry's leading provider of market data management solutions." The acquisition demonstrates TRG Screen's ongoing commitment to servicing the needs of market data consumers, vendors and exchanges. Financial terms of the transaction were not disclosed. About TRG Screen TRG Screen is the leading provider of enterprise subscription management solutions. Founded in 1998, TRG Screen is uniquely differentiated by its ability to monitor both spend and usage of data and information services including market data, research, software licenses, consulting and other corporate expenses. TRG Screen's solutions provide its customers with full transparency into their vendor relationships and their subscription spend and usage, enabling them to optimize their enterprise subscriptions. TRG acquired Priory Solutions in 2016, Screen Group in 2018, Axon Financial Systems in 2019, Market Data Insights in 2020, and Jordan & Jordan's Market Data Reporting (MDR) business in 2021 and with these acquisitions is now positioned as the global market leader in the financial, legal, and professional services markets. TRG Screen's product portfolio includes subscription spend, usage, enquiry and compliance solutions. For more information visit trgscreen.com. Follow TRG Screen on LinkedIn, @TRG Screen, and on Twitter, @trgscreen. About Xpansion Xpansion delivers next-generation reference data solutions that empower financial institutions to streamline their reference data operations, reduce costs, enhance data quality, and improve data discovery. With a focus on customer satisfaction, continuous innovation and quick time to value, Xpansion is a trusted partner for financial institutions in the buy- and sell-side as well as solution providers in the industry.

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Cloud App Management

DriveNets and Acacia Announce Joint Network Cloud 400G ZR/ZR+ Solution

PR Newswire | January 16, 2024

DriveNets – a leader in innovative networking solutions – and Acacia today announced the completion of integrating multiple Acacia 400G ZR/ZR+ optical modules with DriveNets' Network Cloud platform. The combined DriveNets-Acacia solution will ensure quick adoption of this innovative disaggregated networking solution and accelerate large-scale network rollouts. DriveNets and Acacia have joint Tier-1 operator customers who will deploy the joint solution. Last September, DriveNets announced that Network Cloud was the first Disaggregated Distributed Chassis/Backbone Router (DDC/DDBR) to support ZR/ZR+ optics as native transceivers that can be inserted into any Network Cloud-supported white boxes. The combined Acacia-DriveNets solution announced today adds the initial collaboration between the companies, offering several benefits: The joint solution will deliver significant simplicity and cost savings by collapsing Layer-1 to Layer-3 communications into a single platform. The use of 400ZR/ZR+ eliminates the need for standalone optical transponders, lowering the number of boxes in the solution, and reducing operational-overhead, floor-space, and power. DriveNets and Acacia worked together to ensure that the DriveNets NOS (DNOS) supports the 400ZR/ZR+ modules beyond simply plugging them into the box. The collaboration ensures the 400ZR/ZR+ modules can be tunable, configurable, and manageable by DriveNets Network Cloud software. This integration also goes beyond interoperability validation. DriveNets Network Cloud offers full software support for the Acacia modules, including configuration (channel and power), monitoring, and troubleshooting for Acacia Bright 400ZR+ transceivers with transmit power greater than +1dBm. "Today's announcement is further proof of the growth of disaggregated networking solutions and demonstrates that more operators are looking for open solutions that will allow them to mix elements from multiple vendors and avoid being locked to a specific end-to-end vendor solution," said Nir Gasko, Vice President, Global Strategic Alliances for DriveNets. "By collaborating with Acacia, we enable our joint customers to quickly adopt cutting-edge technologies and evolve their networks faster." "Partnering with DriveNets on this joint solution will allow network operators to deploy Acacia's high-volume standard-based coherent pluggable portfolio in open disaggregated networks with less effort," said Fenghai Liu, Senior Director of Product Line Management for Acacia. "Through this collaboration customers can achieve significant capex and opex savings with router-based coherent optics." DriveNets Network Cloud is being adopted by more Tier-1 operators around the world. By partnering with world-class providers like Acacia, the company continues to expand its ecosystem to support its customers' desire to mix-and-match hardware and software from multiple vendors. Learn more about DriveNets here. About DriveNets DriveNets is a leader in high-scale disaggregated networking solutions. Founded in 2015, DriveNets modernizes the way service providers, cloud providers and hyperscalers build networks, streamlining network operations, increasing network performance at scale, and improving their economic model. DriveNets' solutions – Network Cloud and Network Cloud-AI – adapt the architectural model of hyperscale cloud to telco-grade networking and support any network use case – from core-to-edge to AI networking – over a shared physical infrastructure of standard white-boxes, radically simplifying the network's operations and offering telco-scale performance and reliability with hyperscale elasticity. DriveNets' solutions are currently deployed in the world's largest networks.

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Cloud Infrastructure Management

The Manufacturing Sector Experiences More Attacks in the Cloud than Any Other Industry

PR Newswire: | January 19, 2024

Netwrix, a cybersecurity vendor that makes data security easy, today revealed additional findings for the manufacturing sector from its survey of 1,610 IT and security professionals across more than 100 countries. According to the survey, 64% of companies in the manufacturing sector suffered a cyberattack during the preceding 12 months, which is similar to the finding among organizations overall (68%). However, it turned out that the manufacturing sector experiences more cloud infrastructure attacks than any other industry surveyed. Among manufacturing companies that detected an attack, 85% spotted phishing in the cloud compared to only 58% across all verticals; 43% faced user account compromise in the cloud as opposed to 27% among all industries; and 25% dealt with data theft by hackers in the cloud compared to 15% for organizations overall. "The manufacturing sector relies heavily on the cloud to work with their supply chain in real time. This makes their cloud infrastructure a lucrative target for attackers — infiltrating it enables them to move laterally and potentially compromise other linked organizations, as happened to one the world's top meat processing companies. Credential compromise or malware deployed via a phishing email is just the beginning of the attack," says Dirk Schrader, VP of Security Research at Netwrix. "The attack surface in the cloud is always expanding, so it's critical for manufacturing companies to adopt a defense-in-depth approach," adds Ilia Sotnikov, Security Strategist at Netwrix. "First, they must rigorously enforce the principle of least privilege to limit access to sensitive data, which ideally includes just-in-time access to eliminate unnecessary entry points for adversaries. They also need to gain deep visibility into when and how critical data in the cloud is being used so that IT teams can promptly spot potential threats. Finally, they need to be prepared to minimize the damage from incidents by having a comprehensive response strategy that is regularly exercised and updated." To learn more about security trends, check out the complete 2023 Hybrid Security Trends Report. About Netwrix Netwrix makes data security easy. Since 2006, Netwrix solutions have been simplifying the lives of security professionals by enabling them to identify and protect sensitive data to reduce the risk of a breach, and to detect, respond to and recover from attacks, limiting their impact. More than 13,500 organizations worldwide rely on Netwrix solutions to strengthen their security and compliance posture across all three primary attack vectors: data, identity and infrastructure.

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TRG Screen Announces Acquisition of Xpansion for Reference Data Usage Management

PR Newswire: | January 25, 2024

TRG Screen, the leading provider of enterprise subscription spend and usage management software, today announced it has acquired Xpansion, the leading provider of cloud-based solutions for reference data usage monitoring in the financial services industry. The acquisition of Xpansion will further solidify TRG Screen's position as a global market leader in market data management solutions. Xpansion – established in 2013 – is focused on empowering data operations teams to proactively manage their usage, control costs and optimize data workflows. Xpansion's offerings include Xmon, Xprocess and Xplore, and provide real-time analytics, giving clients unprecedented transparency, visibility and control into their reference data usage. This deal consolidates TRG Screen's unique position as the only provider of enterprise subscription management capabilities spanning the whole spectrum of market data optimization, from spend and inventory tracking, through to usage and enquiry management, exchange reporting and compliance. "Xpansion and TRG Screen have been partners for many years. Bringing Xpansion into the TRG Screen family is a very logical next step for both companies, given our strong relationship and shared view that the industry demand for integrated usage management solutions is going to continue to grow," said TRG Screen CEO Leigh Walters. "Xpansion is an established firm with excellent customer satisfaction and retention, and highly experienced and industry respected leadership. We are very excited at the opportunities this acquisition brings." "We are thrilled to be joining TRG Screen," said Xpansion co-founder and CEO Amjad Zoghbi. "Reference data usage is one of the most complex aspects of market data management, and managing it correctly is essential to maintaining contractual compliance and ensuring clients are right-sizing their usage based on actual consumption and business need. I'm very pleased that Xpansion's customers, and team, will now be part of the best-of-breed solution with the industry's leading provider of market data management solutions." The acquisition demonstrates TRG Screen's ongoing commitment to servicing the needs of market data consumers, vendors and exchanges. Financial terms of the transaction were not disclosed. About TRG Screen TRG Screen is the leading provider of enterprise subscription management solutions. Founded in 1998, TRG Screen is uniquely differentiated by its ability to monitor both spend and usage of data and information services including market data, research, software licenses, consulting and other corporate expenses. TRG Screen's solutions provide its customers with full transparency into their vendor relationships and their subscription spend and usage, enabling them to optimize their enterprise subscriptions. TRG acquired Priory Solutions in 2016, Screen Group in 2018, Axon Financial Systems in 2019, Market Data Insights in 2020, and Jordan & Jordan's Market Data Reporting (MDR) business in 2021 and with these acquisitions is now positioned as the global market leader in the financial, legal, and professional services markets. TRG Screen's product portfolio includes subscription spend, usage, enquiry and compliance solutions. For more information visit trgscreen.com. Follow TRG Screen on LinkedIn, @TRG Screen, and on Twitter, @trgscreen. About Xpansion Xpansion delivers next-generation reference data solutions that empower financial institutions to streamline their reference data operations, reduce costs, enhance data quality, and improve data discovery. With a focus on customer satisfaction, continuous innovation and quick time to value, Xpansion is a trusted partner for financial institutions in the buy- and sell-side as well as solution providers in the industry.

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DriveNets and Acacia Announce Joint Network Cloud 400G ZR/ZR+ Solution

PR Newswire | January 16, 2024

DriveNets – a leader in innovative networking solutions – and Acacia today announced the completion of integrating multiple Acacia 400G ZR/ZR+ optical modules with DriveNets' Network Cloud platform. The combined DriveNets-Acacia solution will ensure quick adoption of this innovative disaggregated networking solution and accelerate large-scale network rollouts. DriveNets and Acacia have joint Tier-1 operator customers who will deploy the joint solution. Last September, DriveNets announced that Network Cloud was the first Disaggregated Distributed Chassis/Backbone Router (DDC/DDBR) to support ZR/ZR+ optics as native transceivers that can be inserted into any Network Cloud-supported white boxes. The combined Acacia-DriveNets solution announced today adds the initial collaboration between the companies, offering several benefits: The joint solution will deliver significant simplicity and cost savings by collapsing Layer-1 to Layer-3 communications into a single platform. The use of 400ZR/ZR+ eliminates the need for standalone optical transponders, lowering the number of boxes in the solution, and reducing operational-overhead, floor-space, and power. DriveNets and Acacia worked together to ensure that the DriveNets NOS (DNOS) supports the 400ZR/ZR+ modules beyond simply plugging them into the box. The collaboration ensures the 400ZR/ZR+ modules can be tunable, configurable, and manageable by DriveNets Network Cloud software. This integration also goes beyond interoperability validation. DriveNets Network Cloud offers full software support for the Acacia modules, including configuration (channel and power), monitoring, and troubleshooting for Acacia Bright 400ZR+ transceivers with transmit power greater than +1dBm. "Today's announcement is further proof of the growth of disaggregated networking solutions and demonstrates that more operators are looking for open solutions that will allow them to mix elements from multiple vendors and avoid being locked to a specific end-to-end vendor solution," said Nir Gasko, Vice President, Global Strategic Alliances for DriveNets. "By collaborating with Acacia, we enable our joint customers to quickly adopt cutting-edge technologies and evolve their networks faster." "Partnering with DriveNets on this joint solution will allow network operators to deploy Acacia's high-volume standard-based coherent pluggable portfolio in open disaggregated networks with less effort," said Fenghai Liu, Senior Director of Product Line Management for Acacia. "Through this collaboration customers can achieve significant capex and opex savings with router-based coherent optics." DriveNets Network Cloud is being adopted by more Tier-1 operators around the world. By partnering with world-class providers like Acacia, the company continues to expand its ecosystem to support its customers' desire to mix-and-match hardware and software from multiple vendors. Learn more about DriveNets here. About DriveNets DriveNets is a leader in high-scale disaggregated networking solutions. Founded in 2015, DriveNets modernizes the way service providers, cloud providers and hyperscalers build networks, streamlining network operations, increasing network performance at scale, and improving their economic model. DriveNets' solutions – Network Cloud and Network Cloud-AI – adapt the architectural model of hyperscale cloud to telco-grade networking and support any network use case – from core-to-edge to AI networking – over a shared physical infrastructure of standard white-boxes, radically simplifying the network's operations and offering telco-scale performance and reliability with hyperscale elasticity. DriveNets' solutions are currently deployed in the world's largest networks.

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