With containerized Kognitio you get huge SQL query throughput and high concurrency to serve all your users, directly on your data.
Kognitio takes full advantage of the scalabilty of your containerized infrastucture to provide ultra-fast analytics.
As much resource as you want for as long as you need it.
Kognito is optimized for ad-hoc query patterns over large data sets. Gives ultra-fast response times whether you are doing simple aggregation and filtering or running complex forecasts using embedded Python scripts.
Out-of-the-box integration with your data lake / object stores, such as S3, HDFS and MapRFS, means your data is ready to be analysed with Kognitio.
No need to modify your ETL, just image and query.
Kognitio gives your end users access to advanced analytics via their standard BI tools.
Common languages such as Python 3 come ready installed in the standard Docker container image and more are easy to add.
Allows large complex queries to benefit from massive parallelization while providing high levels of concurrency for simpler BI queries.
Eliminate the need for workload-specific tuning and run mixed workloads.
With Kognitio your users won’t be disappointed by missing capabilities.
We support ANSI standard SQL and are compatible with all of the most popular SQL clients and BI tools.
Before you begin with Kognitio in Containers read our blog articles:
Part 1 is an overview of why we have chosen to containerize Kognitio and covers the thinking behind how we designed our Docker container.
Part 2 gives the technical details of the Kognitio Docker container image including how we set-up and utilize persistent storage.
Part 3 provides a full walkthrough for creating a cluster using 4 containers in a vanilla Docker environment
Part 4 outlines how to use Kubernetes to orchestrate a multi-container Kognitio cluster based on the Docker container image
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