Analytics Platform

Increase speed to insights. Manage enterprise data assets, migrate from on-premise EDW to cloud, improve data accessibility and quality, and increase the return on investment with our solution and starter kit.

Our clients

Manufacturing & CPG
Analytics platform Starter Kit provides a set of pre-integrated capabilities covering end-to-end data lifecycle from ingestion to machine learning including batch processing and streaming data ingestion, data processing, data transformation, data management, catalog and lineage, data pipeline orchestration, data preparation, data warehouses, reporting, as well as AI platform. It is built on the best of breed combination of open source software, saas platforms, as well as cloud-based services. The solution has been battle tested in company-wide numerous deployments in Fortune-5000 companies and technology startups, satisfying the strictest performance and security requirements. analytics data platform - data lake, Grid Dynamics
petabytes of data
events / second
data sources
managed data pipelines
quality and accessibility

Technology stack

Capability Open-source 3rd party AWS GCP Azure
Data lake
Access layer N/A
Data catalog
Data quality
Application platform
AI platform
Capability Data lake Messaging EDW Access layer Orchestration Data catalog Data quality Application platform AI platform
Open-source N/A
3rd party

Analytics platform industries

We helped Fortune-1000 companies unlock the full potential of data.

Read more about analytics platform

From data lake to analytics platform
In this article we review the industry journey from basic data lakes to modern analytics platforms. We also explore what capabilities data driven enterprises need to increase the productivity of their teams.
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5 technology enablers for DataOps
This article describes how to consistently generate actionable insights with DataOps and MLOps and what tools are required to support it.
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Why you need data quality automation to make data-driven decisions with confidence
With growing numbers of data pipelines, data quality is important now more than ever. In this article we describe how to add it to your toolbox.
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