Big Savings and Faster Time to Market Using A Data Lake

Like many financial institutions, our customer was challenged with handling huge volumes of data. The Capital Markets team realized that to scale beyond today’s data requirements, their current approach (enterprise data warehousing) was not sustainable.

Our data specialists set up a Hadoop-based data lake solution. This approach had proved itself a faster and lower-cost alternative to the existing data warehouse. After deployment, the time it took to add attributes dropped by 60 percent, and data storage costs fell by 97 percent.

The switch to a data lake solution also made it easier for business users to collect, process, and query large data sets.

Client: A major financial institution
Business Need:

Implement Data Lake solution for faster time to market and provide self- service analytics

Challenges:

• Existing EDW wouldn’t scale up to retain historical data needs.
• Unstructured data like chats/emails were not being captured.
• Multiple silos of data for every SOR.

Solution:

• Syntelli team worked closely with IT to deploy the Hadoop Data Lake to start storing historical data as well as unstructured data.
• Hadoop Developers and analysts were able to work with Business users to create an EDW for reporting purposes in Hadoop.
• Dashboards were created to enable self-service to business users.

Platform: Hadoop, Apache Spark, Apache Drill, Spotfire, Teradata

Value Added:

• Data lake reduced storage costs by up to 80 percent and reduced development time.
• Provided self-service platform to business users.
Faster deployment times reduced the time-to-market on new product offerings by 60 percent.


 

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