ETL can improve the productivity of data professionals by coding and reusing processes that move data without requiring technical skills to write code or scripts.

ETL has evolved to meet emerging integration requirements for things like streamed data.

Organizations need ETL and ELT to gather data, maintain accuracy, and provide the auditing facility often required in data warehousing, reporting, and analytics.

Today’s fast-moving data (streamed data) can be instantly captured and analyzed through streaming analytics. This approach presents the opportunity to act immediately, based on what is happening at a particular time. But the historical view that ETL makes possible puts the data in context. In turn, organizations achieve a full understanding of business over time. The two approaches need to work together.

Data integration has been with us for years, but it continues to play a critical role in data capture, processing, and movement. Follow these tips from TDWI to guide your data integration modernization efforts.

This energy supplier company stored customer data in different systems and formats. Using SASĀ® Data Management software, the company purged and integrated records, thereby reducing total records by 25% and increasing record integrity by 30%.

Rather than fading away, old technologies often end up coexisting with new ones. Today, data integration is changing to keep up with different data sources, formats, and technologies. This document shows how to keep your approach to data integration relevant.

Gartner has positioned SAS as a Leader in the Gartner Magic Quadrant for Data Integration Tools 2017.

SAS data integration software distributes integration tasks across any platform and connects to virtually any source or destination data warehouse.

The core ETL and ELT tools work in parallel with other data integration tools, and with other different aspects of data management – such as data quality, data governance, virtualization, and metadata. Its current popular uses include:

ETL is a proven method that many organizations turn to every day – such as retailers who need to see sales data regularly or healthcare providers looking for an accurate description of requests. ETL can combine and surface transaction data from one data warehouse to another so that it is ready for business people to view in a format they can understand.

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