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Case Study

How We Enabled 50% Reduction in Product Release Cycles with Our DevOps and DataOps Services


Lack of DataOps skills can become an impediment for release engineers who have to manage tight deployment windows. The release engineers of one of our Banking Clients faced a similar situation and were constantly challenged by errors arising from automated release of a database and related application codes.

Without knowledge of automated tools, Developers have to make backups manually before releasing any new change, while storing data in the event of a failure. With growing volumes of data these Data Operations can get immensely expensive and time consuming. The need of the hour was to reduce valuable time, money, and effort spent on error-handling and rollbacks. This also meant onboarding experienced DevOps engineers who can write software extensions for connecting new digital banking services to the end customer. The skills involved included knowledge of continous automated testing and the ability to quickly replicate infrastructure for every release.

Our Solution: Conquering DevOps for Data with Snowflake

  • Reduces schema change frequency
  • Enables development in preferred programming languages
  • Supports SQL, Python, Node.Js, Go, .NET, Java among others
  • Automates Data Cloud implementation automates DevOps tasks
  • Helps build ML workflows with faster data access and data processing
  • Powers developers to easily build data pipelines in Python, Java, etc.
  • Enables auto-scale features using custom APIs for AWS and Python


Automated release of database and related application code were building up several challenges, including:

Data Integrity Issues: Automated releases may lead to unintended changes in database schema or data, causing data integrity issues, data loss, or corruption.

Downtime and Service Disruption: Automated releases may result in downtime or service disruption if database migrations or updates are not handled properly, impacting business operations and customer experience.

Performance Degradation: Automated releases may inadvertently introduce performance bottlenecks or degrade database performance if changes are not thoroughly tested and optimized.

Dependency Management: Automated releases may encounter challenges with managing dependencies between database schema changes and application code updates, leading to inconsistencies or deployment failures.

Rollback Complexity: Automated releases may complicate rollback procedures, especially if database changes are irreversible or if application code relies on specific database states.

Security Vulnerabilities: Automated releases may introduce security vulnerabilities if proper access controls, encryption, or data protection measures are not implemented or properly configured.

Compliance and Regulatory Risks: Automated releases may pose compliance and regulatory risks if changes are not audited, tracked, or documented appropriately, potentially leading to data breaches or legal consequences.

Testing Overhead: Automated releases may require extensive testing to validate database changes and application code updates across various environments (e.g., development, staging, production), increasing testing overhead and time-to-release.

Version Control Challenges: Automated releases may encounter challenges with version control, especially if database changes and application code updates are managed separately or if versioning is not synchronized effectively.

Communication and Collaboration: Automated releases may strain communication and collaboration between development, operations, and database administration teams, leading to misalignment, misunderstandings, or conflicts during the release process.

How We Helped

  • Our Developers helped stand-up multiple isolated, ACID-compliant, SQL-based compute environments as needed
  • Toolset expertise eliminated the time and effort spent on procuring, creating, and managing separate IT or multi-cloud environments
  • We helped automate the entire process of creating new environments, auto-suspend idle environments
  • Enabled access to live data from a provider account to one or many receiver/consumer accounts
  • Our solution creates a copy of the live data instantly in metadata, without the need to duplicate

The Impact

  • 40% improvement in storage costs and time spent on seeding preproduction environment
  • 80% reduction in time spent on managing infrastructure, installing patches, and enabling backups
  • 80% of time and effort saved in enabling software updates so that all environments run the latest security updates
  • 80% elimination of expensive backups required to restore Tables, Schemas, and Databases that have been changed or deleted
DevOps with Snowflake


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Get inspired by some real-world examples of complex data migration and modernization undertaken by our cloud experts for highly regulated industries.

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