Due to the exponential growth of data stores and demand for the freshest data possible from business users, IT teams are under pressure to efficiently ingest, process, analyze, and distribute data. Meanwhile, enterprise data warehouses are buckling under the strain of having to run extract, transform, and load (ETL) processes on larger and larger data sets in smaller batch windows. As a result, companies are turning to Hadoop, a cost-effective and highly scalable platform, to address the problems of data processing bottlenecks, insufficient processing capacity, and rising data warehousing costs.
Firms can implement ETL offload—the migration of compute-intensive ETL integration jobs from an enterprise data warehouse to an economic big data platform like Hadoop—to accelerate BI ETL workloads, better allocate and leverage IT resources, and keep up with the velocity of modern data flows.
To enable BI reporting and analytics, large firms depend on their data warehouse, BI tools, and ETL solutions. Today many firms utilize an ETL tool to simplify and streamline ETL development, execution and management tasks. While ETL automation tools have made it easier for IT teams to design, monitor, and adjust data processing workflows, these tools often prove inadequate to contend with multiplying data sources, ballooning data stores, and demands for continuous updates. In fact, many IT managers are now finding that they cannot meet SLAs with their existing infrastructure due to shrinking batch windows and ETL processing bottlenecks—a far cry from real-time ETL.
ETL offload is a necessity for firms struggling with data processing delays and unsustainable data warehousing costs. Running on a cluster of commodity servers, Hadoop is designed to ingest and process large volumes of data efficiently using a divide-and-conquer approach. By distributing data across multiple compute nodes, Hadoop can process more data in smaller batch windows, making it a perfect fit for ETL offload. By migrating resource-intensive ETL workloads to Hadoop, firms can cut data warehousing costs, free up data warehouse CPU cycles for BI projects, and thereby reduce time-to-insight.