December 2018 Summaries

The ongoing separation of compute and storage in data analytics is transforming how enterprises manage their data, allowing for more flexible and cost-effective solutions by utilizing cloud storage services like AWS, Microsoft Azure, and Google Cloud alongside distributed SQL query engines such as Presto, Hive, and Spark. This trend, initially pioneered by Google, enables businesses to independently scale compute and storage resources, facilitating the use of multiple analytics engines for diverse use cases without data duplication or resource competition. Companies like Facebook, Netflix, and Airbnb benefit from this abstraction, which is not confined to cloud environments but is increasingly supported in on-premises setups through object storage solutions. Analysts predict the continued adoption of this architecture as enterprises seek to leverage both on-premises and cloud-based data sources, integrating relational and NoSQL databases for enhanced data discovery, processing, and analytics capabilities.