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Data architectures for reporting and analytics continue to evolve as organizations transition from on-premises systems to the cloud. Organizations look to invest in high-performance, flexible, scalable, and future-proof data architecture. Instead of considering the data warehouse and the data lake as independent “data islands” that coexist in a cloud platform, it is time to reconsider the fundamental ways that information is accumulated, managed, and then provisioned to the different downstream data consumers and analytics tools.

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.

As the volume, variety, and velocity of data increases, the cloud is the most efficient and cost-effective option for machine learning and advanced analytics. Organizations looking to scale their big data projects can do so with greater ease with a cloud-native data platform. Qubole provides a single platform for data engineers, analysts, and scientists that supports multiple use cases -- from machine learning to predictive analytics. The platform saves organizations up to 50 percent in data processing costs by leveraging multiple engines like Apache Spark, Presto, and Hive, and automatically provisions, manages, and optimizes cloud resources.