Data Warehouse Modernization

What is the Data Warehouse Modernization big data use case?

Data Warehouse Modernization (formerly known as Data Warehouse Augmentation) is about building on an existing data warehouse infrastructure, leveraging big data technologies to ‘augment’ its capabilities. There are three key types of Data Warehouse Modernizations:

  • Pre-Processing - using big data capabilities as a “landing zone” before determining what data should be moved to the data warehouse
  • Offloading - moving infrequently accessed data from data warehouses into enterprise-grade Hadoop
  • Exploration - using big data capabilities to explore and discover new high value data from massive amounts of raw data and free up the data warehouse for more structured, deep analytics.

Ask yourself:

checkmark Are you integrating big data and data warehouse capabilities to increase operational efficiency?

checkmark Have you taken steps to migrate rarely used data to new technologies like Hadoop to optimize storage, maintenance and licensing costs?

checkmark Are you using stream computing to filter and reduce storage costs?

checkmark Are you leveraging structured, unstructured, and streaming data sources required for deep analysis?

checkmark Do you have a lot of cold, or low-touch data that is driving up costs or slowing performance?

If you answered yes to any of the above questions, the Data Warehouse Modernization use case is the best starting point for your big data journey.

With Data Warehouse Modernization, organizations can:

  • Big Data Use Case - Data Warehouse Augmentation

    Watch how the new big data technologies can provide valuable new capabilities that extend and augment your existing Data Warehouse to deliver broader analytics, more value and improved ROI.

  • Extending the Value of Your Data Warehouse: Big Data Use Case

    Learn how data warehouse augmentation can help lower costs, provide the ability to perform analysis on more and new types of data.

  • Case Study

    Learn how Constant Contact improved analysis performance by over 40 times, reduced wait time from hours to seconds, and increased client campaign effectiveness by almost 25%

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