Data Warehouse Augmentation

Traditional approaches for data processing in the data warehouse can’t keep pace with big data. According to Gartner, nearly 70% of all data warehouses face challenges related to performance and capacity. Data lakes built on top of scale-out architectures (e.g. Hadoop, AWS S3) can store raw data in any format at a fraction of the cost of the data warehouse. A Zaloni-managed data lake makes enterprises more agile by offloading extract, transform, load (ETL) functions to an open source platform – delivering faster processing and saving millions of dollars.


Maximize Available Data for Analysis

By taking a modern approach to your data warehouse infrastructure, you will not only improve its performance and capacity, but you will also unleash the availability and efficiency of data for analysis. Your most valuable business insights can be discovered from analyzing multiple data points that can now be combined thanks to the offload capabilities and scalability that a modern data lake architecture can provide.

Improve Performance and Capacity While Reducing Costs

“One of the greatest challenges with managing massive amounts of data, located across multiple sources, is managing cost. With Zaloni’s Data Lake Management Platform we will realize $33M in savings, and those savings accompanied a scalable, highly available and reliable platform.”
– Pradeep Varadan, Associate Director, Verizon Enterprise Solutions

Data Warehouse Augmentation: Enhanced Analytics From the Data Lake

Data Warehouse Augmentation with the Zaloni Data Platform (ZDP)


Zaloni works closely with enterprises to design the architecture for data warehouse augmentation and implements the industry’s only fully integrated data lake platform to not only accelerate deployment, but to significantly improve visibility into the data.

Featured Resources

Why Smart Companies are Complementing Their Data Warehouses with Data Lakes
Why Your Data Warehouse Needs a Data Lake
Operationalize Your Data Lake to Accelerate Business Insights

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