With the rise of big data, connected devices, and the app-based economy, enterprises are collecting and having to manage, process, and analyze larger amounts and varieties of data faster than ever before. ETL integration tools help IT teams or data integration specialists design, execute, and monitor BI ETL processes—essential for transforming all of that raw data into actionable insight.
Even with a great ETL solution, however, some IT teams are finding it difficult to provide business users with the accurate, complete, and up-to-date data they need for tactical decision-making. In the face of exponential data growth, cumbersome ETL integration processes have become a critical bottleneck in the enterprise data warehouse, necessitating new approaches to data integration.
In order to derive meaningful information and insights from data, businesses must extract data from various sources, transform this data into a format or structure usable by other systems and applications, and load the transformed data into a data warehouse for reporting and analytics. These processes which are referred to as ETL or ETL integration can be implemented with hand-coded scripts in an ETL tool.
Modern ETL integration tools allow IT teams or specialists to set up, execute, and manage ETL processes via an easy-to-use point-and click UI. By providing a graphical overview of data flows and components, these tools make mapping data elements and modifying and troubleshooting ETL integration workflows more manageable in the long-term.
Where many of today's ETL integration tools fall short is in the face of large volumes of unstructured data flowing in from multiple sources combined with the demand for real-time business insight. As batch windows shrink, IT teams are struggling to process and deliver fresh data to business users—and accommodate changing business requirements—with their existing tools and infrastructure.
One way firms can accelerate data warehouse processing is through ETL offload, or offloading resource-intensive ETL integration workloads to Hadoop—a cost-effective and highly scalable platform leveraging clusters of servers for parallel processing of complex batch jobs.
With Qlik Compose (formerly Attunity Compose) (and integrated Qlik Replicate (formerly Attunity Replicate)), you can design, create, load, and manage data warehouses and data marts without having to do any manual ETL coding. With Qlik Visibility (formerly Attunity Visibility), our data usage analytics platform, you can identify ETL-intensive workloads in your data warehouse and carry out an impact analysis to assess the possible effects of an offload on performance. Qlik Replicate (formerly Attunity Replicate) then enables you to migrate data from your data warehouse to a Hadoop data lake, boosting the performance of your warehouse and reducing analytics latency.
Another way firms can reduce time-to-insight is through the use of real-time ETL or real-time data integration. Employing our CDC technology, Qlik (Attunity) Replicate detects, captures, and delivers only committed change data to your database or data warehouse, minimizing the need for bulk transfers and enabling real-time updates.