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ETL for the future - a cluster computing approach

Modern Data Warehouse

Build the hub for all your data structured, unstructured, or streaming to drive transformative solutions like BI and reporting, advanced analytics and real-time analytics.

High-performance and highly flexible data analytics foundation enables timely insights to support data-driven decision making.

The Hybrid Data Warehouse: Fluid, Flexible, and Formidable. It offers full customization and real time stream processing, built-in security, scalability, and easy deployment.

Take advantage of the performance, flexibility, and security of fully managed KOCKPIT services

Hybrid Data Warehouse(Spark + Hadoop)

Spark’s framework is built on top of the Hadoop Distributed File System (HDFS), providing every aspect of Hadoop environment for your customized platform.

Here's What You Can Do with Kockpit create Your Autonomous Database in a Few Steps:

  • Provision a database instance
  • Connect SQL Server to the new database instance
  • Load your data in highly compressed format

Power BI is a business analytics service that delivers insights to enable fast, informed decisions.

  • Traditional data warehouse functions for BI reporting and modeling
  • VisuallyData analytics for structured data, machine logs
  • On-demand self-service data access that encourages collaboration
  • Support for diverse groups of users with varying levels of analytical skills

Main Features


Open Source

The whole cluster environment is totally open source which can be customized as per business requirements.


Distributed Processing

Data will be stored in distributed manner across cluster which will be processed in parallel on cluster of nodes.


Fault Tolerant

Data will be totally fault tolerant, which means in case of node failure or task failure they will be recovered automatically by the framework.


Data Reliability

Due to replication of data in the cluster, data is reliably stored on the cluster of machine despite machine failures. If your machine goes down, then also your data will be stored reliably.


High Availability

Data is highly available and accessible despite hardware failure, due to multiple copies of data. If a machine or few hardware crashes, then data will be accessed from other path.



It starts with the same concept of being able to run jobs except that it first places the data into RDDs (Resilient Distributed Datasets) so that this data is now stored in memory so it’s more quickly accessible i.e. the same jobs can run much faster because the data is accessed in memory.


Real time stream processing

Every year the real time data being collected from various sources keeps shooting up exponentially. This is where processing and manipulating real time data can help us. Spark helps us to analyse real time data as and when it is collected.


Data Locality

Kockpit Datawarehouse works on data locality principle which states that move computation to data instead of data to computation. When client submits the algorithm, this algorithm is moved to data in the cluster rather than bringing data to the location where algorithm is submitted and then processing it.


High-growth brands trust Kockpit with their optimization efforts

Start your work with Kockpit today