ETL Offload

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.

Eliminating the Data Processing Bottleneck in the Data Warehouse with ETL Offload

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.

Accelerating ETL Offload and Optimizing Your Data Warehouse with Qlik (Attunity)

In order to capitalize on the ETL offload opportunity, your teams must first identify the appropriate data and workloads to offload to Hadoop. Qlik Visibility (formerly Attunity Visibility) is a unique data usage analytics platform that helps you identify the most ETL-intensive workloads, measuring for example the percent of CPU cycles they consume, and predict the utilization and performance benefits of their potential offload.

Qlik Visibility (formerly Attunity Visibility) offers a detailed, comprehensive view into your data and resource usage patterns, allowing you to identify repetitive or resource-intensive workloads and run impact analysis to predict the performance impact of ETL offload. Our solution can guide you through the entire ETL offload process—beginning with the initial assessment and finishing with data migration using Qlik Replicate (formerly Attunity Replicate), our data replication tool. With Qlik Replicate (formerly Attunity Replicate), you can accelerate ETL offload by migrating data from your data warehouse to Hadoop via our easy-to-use graphical interface.