Think Inside the Box

Maximize your MPP investment with 100% pushdown architecture from Diyotta. Achieve the highest level of performance with native execution and eliminate intermediate data integration servers.

Massively Parallel Processing Integration

Diyotta is purpose built for MPP-based data warehouses such as Teradata, IBM Pure Data for Analytics (Netezza) and Greenplum. Diyotta offers a unique, agile and reusable design process to create optimized code that provides the highest level of performance for executing data transformations.

Powerful

Utilize MPP’s full
processing potential

Fast

Move data point-to-point
with no intermediate platforms

Simple

Intuitive graphical interface
for ease of use

HIGHLY OPTIMIZED IN-DATABASE PROCESSING FOR ELT WORKLOADS

Diyotta uses the power of MPP platforms by automatically generating native code from its graphical user interface and native functional libraries. Diyotta orchestrates point-to-point movement and utilizes target platforms for processing without the need for an intergration server.

MODERNIZING TRADITIONAL DATA WAREHOUSE WITH HADOOP

Diyotta’s provides organizations the ability to leverage existing MPP investments in data infrastructure while adding new technologies such as Hadoop. Once deployed, Diyotta uses Hadoop as an enterprise data hub to reduce traditional enterprise data warehouse costs and improve performance.

MAXIMIZE EXISTING INVESTMENTS & FUTURE-PROOF ARCHITECTURES

Technology is continuously evolving. Architectures need to adapt quickly to new platforms for changing business needs. Diyotta automatically generates native code based on target processing platforms and can easily change the native code to new target platforms.

Maximize Your MPP Investment With Diyotta

Eliminate the need for intermediate integration servers that sit between source and target systems. Develop an ability to move data at network speed: point-to-point. Take advantage of powerful processing capabilities provided by MPP platforms for in-database processing. Maximize performance with the use of efficient set-based operations and reduce data latency from hours to minutes.