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Escaping Hadoop Purgatory with a Streaming Data Warehouse

The world of Hadoop has grown overly complex. As data analytics has evolved, the need for add-on components has grown. The initial Apache definition of “Big Data” did not include streaming data, a cloud data warehouse, graph analytics, location intelligence, and machine learning, to name just a few examples. 

In an era where more and more data is moving to the cloud, the object stores from the cloud platform players are increasingly replacing the Hadoop Distributed File System (HDFS). There is an opportunity to consolidate and simplify the data ecosystem. Owning a “platform” is everyone’s target – from traditional BI and analytics, data integration, DBMS, and machine learning vendors, to cloud platform providers.

The Kinetica Streaming Data Warehouse is well-situated to simplify the messy architectures of the Hadoop world. Kinetica provides the streaming, location, graph, and machine learning tools required for modern analytical applications in a single solution. The simplified Kinetica architecture cuts out the pieces that result in slow data movement and armies of troubleshooters, while enhancing stability and making it straightforward to evolve to fit future use cases. 

Many components of the Hadoop ecosystem are being stretched beyond their original purpose. This is driven by an increase in demand for real-time results, and an increase in the complexity of desired queries, on a Hadoop ecosystem created for a more batch-oriented world. In contrast, Kinetica was built from the ground up to deliver real-time analysis on data at speed and scale. It’s in our DNA to solve complex, high-velocity, high-scale use cases. 

Interested in learning more about how Kinetica can consolidate your Hadoop stack? Our founders, Amit Vij and Nima Negahban, are hosting a round-table discussion on November 18th to discuss this topic. Register for their talk here now.

Andrew Wooler is global marketing manager at Kinetica.


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