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Stream processing Data governance ML platform Analytical data platform Cloud-native data platform Big data DevOps

As analytical data platforms become mainstream and receive significant funding, large enterprises need to onboard large data engineering teams and establish efficient delivery processes to ensure these platforms are successful. While the central ideas from traditional continuous delivery and DevOps can be reused for Big Data implementations, analytical data platforms require a specialized and customized approach because of the sheer volume of data that they process.

At Grid Dynamics, we have expertise in building analytical data platforms, data engineering, CICD, quality engineering and DevOps. When we started implementing analytical data platforms for our clients, we did it with full automation and continuous delivery best practices. In our work on data platform development, each data processing pipeline is treated as a microservice, which is the same technique we use when developing transactional microservices architectures. We then automate the delivery process for data pipelines, using our expertise with test data management, test automation and environment management.

Implementing Big Data and CI/CD at scale since 2010

Cross-functional teams

In order to implement a CI/CD pipeline for an analytics data platform, coordination is needed between various skills: data engineering, deployment engineering and quality engineering. It is difficult for one person to have all these skills, so our approach is to have all the necessary skills within the same team. That way, the team can function as a unit and release a high quality, efficient reliable data pipeline.

Full automation of delivery process

In order to attain a delivery process that is high in efficiency, quality and reliability, we automate all aspects of the data pipelines delivery, including: testing, deployment and release. When applied to Big Data, CI/CD must be handled differently because of its high dependency on data, and specifically test data management. Our approach to test data management in Big Data development includes creating synthetic datasets that can be used for unit testing, and providing production-like data for integration testing. Our usual recommendation for Big Data projects is to start testing with production-like data as early in the process as possible.

Protection of sensitive data

In many cases, data engineers need to work on data pipelines that process sensitive data. On one hand, it is better to provide teams with production-like data, but on the other hand, developers and quality engineers shouldn't have access to sensitive data. In some cases, there are additional requirements related to accessing sensitive data from offshore locations. To address this challenge, we use tokenization and encryption to obfuscate sensitive data before it is given to developers. If additional security is required, we may use fully synthetic data sets that were created with production data patterns.

Fast time-to-market

Fast time-to-market

Quickly develop and release data pipelines that business users need.
High quality

High quality

Ensure that businesses receives detailed and updated reports and insights.
High efficiency

High efficiency

Maximize the productivity of data engineering teams by having teams which possess all necessary skills.
Transparency and visibility

Transparency and visibility

Enable management monitoring and the control of the feature delivery process.
Enterprise-level controls

Enterprise-level controls

Ensure compliance with internal and external change management policies.
Security and compliance

Security and compliance

Protect sensitive data and ensure compliance with internal and external policies.
How it works
Distributed key-value store for critical data of distributed systems
Maintains configuration information and provides distributed synchronization
Performs virtualization (or containerization) at the operating system level
Container management system for deployment.
Manages Kubernetes applications
Automates software development process with continuous integration
Version control system for tracking changes in code
Cloud computing services from Amazon
Build automation tool
Version control repository hosting service
Creates templates for multiple platforms to help virtualization
Provides security for distributed systems using blockchain architectures
Agile project management tool
Continuous integration and development server
Distributed key-value store for critical data of distributed systems
Maintains configuration information and provides distributed synchronization
Performs virtualization (or containerization) at the operating system level
Container management system for deployment.
Manages Kubernetes applications
Automates software development process with continuous integration
Version control system for tracking changes in code
Cloud computing services from Amazon
Build automation tool
Version control repository hosting service
Creates templates for multiple platforms to help virtualization
Provides security for distributed systems using blockchain architectures
Agile project management tool
Continuous integration and development server

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