Analytical data 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 accelerator.

10 PB
of managed data
1 million
events / second
10 years
of experience

Get to insights faster

Before realising business value with analytics, companies have to build a platform and fill it with data. This investment is necessary to consistently generate insights at scale. We use our accelerator and years of experience to quickly build the analytics platform and implement data pipelines, reducing the cost and risk of the initial investment. That way, our clients can get to value 10x faster and focus on what’s important for business: business intelligence, data science, and data driven decisions with machine learning.

Reduce cost and increase scalability

Traditional on-premise EDW software, such as Teradata, Netezza, or mainframe-based DB2, is getting prohibitively expensive and can’t efficiently scale to the new analytics use cases. By migrating data pipelines and reporting to the cloud, you can reduce total cost of ownership, take advantage of onboarding new data sources, implement real time streaming data pipelines, and dynamically scale to increase efficiency of data scientists.

Accelerate innovation

Very few companies should be in the business of managing on-premise data lakes. High cost of maintenance, stability issues, coupled with lack of scalability and limited technology stack options for DataOps and MLOps, they slow down data analysts. Migrating on-premise data processing to the cloud based solution reduces total cost of ownership, increases data quality and accessibility, and re-focuses company resources on building differentiating value.

Increase data quality and accessibility

Basic data lake is no longer sufficient to implement effective analytics at scale. Too many companies fill in in the lakes only to realize that the data is difficult to use. To unlock the value of data, upgrade your data lake with data governance, data quality, catalog and lineage, access layer, implement stream processing, and deploy an AI platform.

Become data driven organization

Getting value from data is hard without needed skills, culture, process, and tools. Similar to how DevOps streamlines application delivery processes, DataOps and MLOps can increase the quality of data pipelines and help data scientists consistently and repeatedly turn data into insights.

Analytical data platform accelerator 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

We provide flexible engagement options to design and build analytical data platforms and AI use cases at scale. Clients take advantage of our accelerators to increase their speed to insights and reduce the risk. Contact us today to start with a workshop, discovery, or PoC.


We offer free half-day workshops with our top experts in big data and analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices.


If you have already identified a specific use case for big data or fast data, we usually can start with a 4–8-week proof-of-concept project to deliver tangible results for your company.


If you are in the stage of requirements analysis and data strategy development, we can start with a 2–3-week discovery phase to perform gap analysis, design your solution, and build an implementation roadmap.