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Big data

Effectively store and process all business insights

Solutions

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 accelerator.

Stream processing

Achieve real time insights with stream analytics. Increase decision making speed by migrating from batch processing to streaming for customer intelligence, IoT, and fraud detection use cases. Transform big data into fast data with our stream processing solutions.

Data quality

Start trusting your data and insights. Automatically detect data corruption and prevent it from spreading with production data quality monitoring.

Machine learning Ops

Consistently deliver actionable insights. Adopt DevOps ideas to machine learning and achieve greater business impact with automated machine learning operations.

Data governance

Manage your data platforms with data governance

ML platform

Get to insights faster and achieve greater business impact. Efficiently scale machine learning efforts in the enterprise while adopting MLOps and increasing the quality of insights. Deploy in the cloud, in the datacenter, or at the edge.

Our clients

Retail
Hi-tech
Manufacturing
Finance
Healthcare

Turn data Into insights faster with Grid Dynamics Analytics platform Starter Kit on AWS Cloud

In the world of data business struggles to get insights faster. Analytics platform Starter Kit helps turning data into insights.

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From data lake to analytical data platform
Analytical data platform starter kit and how it can help building modern data platform coming with orchestrated batch and streaming data pipelines, data quality, data governance and integration with ML platform.
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5 steps to Implementing a successful DataOps practice
In the world of data, the industry invented the term DataOps, which takes Agile and DevOps principles and applies them to data engineering, science, and analytics. Post will focus on the technology enablers that facilitate DataOps implementation.
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Why you need data quality automation to make data-driven decisions with confidence
Data quality trust is hard to get and easy to lose. In this blog, we review causes of data corruption, how to set confidence levels, and data quality goals.
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