DevOps
in
Data &
Analytics
Driving Operational Excellence
DevOps
in
Data &
Analytics
Driving Operational Excellence
DevOps in Data & Analytics applies DevOps principles—traditionally used in software development—to manage data pipelines, analytics, and data infrastructure. By fostering collaboration between teams, automating workflows, and ensuring continuous delivery, we maintain high quality across the entire data lifecycle.
Our DevOps Focus:
Data & Analytics Platform Support
Continuous Improvement
- Ongoing efforts to enhance processes, performance, and cost efficiency.
- Focus: Code fine-tuning in Cloudera, Databricks CI/CD, and other platforms to reduce completion time and costs.
Proactive Monitoring
- Continuously monitor IT systems, networks, and applications to identify and resolve issues before they escalate.
- Tools: Azure Monitor with App Insights, AWS CloudWatch with LogicMonitor.
- Focus: Cost analysis of services across Azure/AWS infrastructure.
Incident Management
- Efficiently identify, respond to, and resolve incidents to minimize business impact.
- Tools: Azure Boards, ServiceNow.
Automation & Scalability
Drive growth and efficiency through automation and scalable solutions.
Key Contributions:
- Automated infrastructure provisioning with Terraform.
- Managed CI/CD pipelines for Snowflake and Redshift.
- Containerized dbt-core using Docker.
- Leveraged AWS EC2 with auto-scaling groups for scalable applications.
Recent Our DevOps Projects in
Data & Analytics
Objective: Streamline deployment and management of multiple technologies using DevOps.
Phase 1:
- • Implemented CI/CD pipelines for Databricks, Azure Data Factory (ADF), and Cloudera.
- • Automated data processing workflows and infrastructure deployment.
Phase 2:
- • Extended CI/CD pipelines to Kafka, automating topic creation and role binding.
- • Developed a user-friendly interface for action selection and execution.
Outcome: Improved deployment efficiency, scalability, and security across environments.
Objective: Streamline infrastructure provisioning and pipeline orchestration.
Key Contributions:
- • Automated Azure infrastructure using Terraform.
- • Established approval-based workflows in Azure DevOps. .
- • Developed YAML-based Azure Pipelines for CI/CD automation.
Outcome: Scalable, approval-driven, and automated Azure environments.
Objective: Automate deployment of Azure Data Factory (ADF) pipelines.
Key Contributions:
- • Integrated ADF with GitHub for version control.
- • Implemented automated testing for configuration validation.
Outcome: Reduced deployment times, minimized errors, and ensured consistency across environments.
Objective: Optimize workflows using AWS services and DevOps tools.
Key Contributions:
- • Leveraged EC2, S3, Lambda, and CloudWatch for scalable, secure solutions.
- • Automated infrastructure provisioning with Terraform.
- • Managed CI/CD pipelines for Snowflake and containerized dbt-core using Docker.
Outcome: Enhanced scalability, security, and automation.
Key Contributions:
- • Built CI/CD pipelines using GitHub, EC2 Jenkins, and Flyway.
- • Integrated ETL tools like DBT and Fivetran with Redshift.
- • Leveraged Databricks Catalog and DLT for scalable compute.
Outcome: Improved deployment efficiency and scalability.
Objective: Automate deployment and infrastructure provisioning for Power BI Data Gateway.
Key Contributions:
- • Provisioned infrastructure using Terraform (Auto Scaling Groups, Security Groups).
- • Automated deployment and configuration using PowerShell scripts.
- • Developed workflows for cluster maintenance and inactive node removal.
Outcome: Scalable, secure, and automated Power BI Data Gateway setup.
Recent Our DevOps Projects in
Data & Analytics
Objective: Streamline deployment and management of multiple technologies using DevOps.
Phase 1:
- • Implemented CI/CD pipelines for Databricks, Azure Data Factory (ADF), and Cloudera.
- • Automated data processing workflows and infrastructure deployment.
Phase 2:
- • Extended CI/CD pipelines to Kafka, automating topic creation and role binding.
- • Developed a user-friendly interface for action selection and execution.
Outcome: Improved deployment efficiency, scalability, and security across environments.
Objective: Streamline infrastructure provisioning and pipeline orchestration.
Key Contributions:
- • Automated Azure infrastructure using Terraform.
- • Established approval-based workflows in Azure DevOps. .
- • Developed YAML-based Azure Pipelines for CI/CD automation.
Outcome: Scalable, approval-driven, and automated Azure environments.
Objective: Automate deployment of Azure Data Factory (ADF) pipelines.
Key Contributions:
- • Integrated ADF with GitHub for version control.
- • Implemented automated testing for configuration validation.
Outcome: Reduced deployment times, minimized errors, and ensured consistency across environments.
Objective: Optimize workflows using AWS services and DevOps tools.
Key Contributions:
- • Leveraged EC2, S3, Lambda, and CloudWatch for scalable, secure solutions.
- • Automated infrastructure provisioning with Terraform.
- • Managed CI/CD pipelines for Snowflake and containerized dbt-core using Docker.
Outcome: Enhanced scalability, security, and automation.
Key Contributions:
- • Built CI/CD pipelines using GitHub, EC2 Jenkins, and Flyway.
- • Integrated ETL tools like DBT and Fivetran with Redshift.
- • Leveraged Databricks Catalog and DLT for scalable compute.
Outcome: Improved deployment efficiency and scalability.
Objective: Automate deployment and infrastructure provisioning for Power BI Data Gateway.
Key Contributions:
- • Provisioned infrastructure using Terraform (Auto Scaling Groups, Security Groups).
- • Automated deployment and configuration using PowerShell scripts.
- • Developed workflows for cluster maintenance and inactive node removal.
Outcome: Scalable, secure, and automated Power BI Data Gateway setup.
Our DevOps Services For
Data & Analytics Platforms
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24/7 Monitoring
|
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CI/CD for Data Pipelines
|
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Security as Code
|
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Disaster Recovery
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Our DevOps Services For
Data & Analytics Platforms
24/7 Monitoring
|
CI/CD for Data Pipelines
|
Security as Code
|
Disaster Recovery
|
Why DevOps for Data & Analytics
Production Support?
Why DevOps for Data & Analytics
Production Support?
Operational Efficiency
- Utilized cluster auto-scaling for PowerBI Gateway and self-hosted Azure agents for builds.
Faster Issue Resolution
- Minimized manual intervention in ADF CI/CD processes.
- Improved build validation checks using SonarQube.
Scalability
- Automated scalable environments using Terraform and PowerShell.
- Leveraged AWS EC2 auto-scaling for scalable applications.
DevOps in Action
Success Stories
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MASHREQ |
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IRIDIUM PROJECT “Karthik & JP both have worked together to meet our scope of work defined in this assignment and completed the deployment without any issue. It’s a different experience to work in this project by using Terraform scripts & other stuffs without any manual intervention (automated scripts for scale-in scale-out) and we gained superb experience to work with you all. |
Operational Efficiency
- Utilized cluster auto-scaling for PowerBI Gateway and self-hosted Azure agents for builds.
Faster Issue Resolution
- Minimized manual intervention in ADF CI/CD processes.
- Improved build validation checks using SonarQube.
Scalability
- Automated scalable environments using Terraform and PowerShell.
- Leveraged AWS EC2 auto-scaling for scalable applications.
DevOps in Action
Success Stories
MASHREQ “Thank you for your exceptional work and dedication in successfully delivering the project on time. Your team’s professionalism and attention to detail are greatly appreciated”. |
IRIDIUM PROJECT “Karthik & JP both have worked together to meet our scope of work defined in this assignment and completed the deployment without any issue. It’s a different experience to work in this project by using Terraform scripts & other stuffs without any manual intervention (automated scripts for scale-in scale-out) and we gained superb experience to work with you all. |
About Our DevOps Blogs
| Sivakami Murugan Lead, DevOps Practice |
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Expertise in CI/CD automation, AWS, Azure, Terraform, Docker. |
| Kavivanan Kumar Senior Software Engineer, DevOps Practice |
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Expertise in CI/CD automation, AWS, Azure, Terraform, Docker. Certified in AWS and Azure |
| Velayutham Sathayan Senior Software Engineer, DevOps Practice |
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ETL integration, CI/CD pipelines, Terraform, and AWS expertise. |
| Hari Prasad Karuppaiya Software Engineer, DevOps Practice |
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DevOps solutions for Databricks, ADF, Cloudera, Kafka, and Azure DevOps. Certified in Azure. |
| Jayaprabakara Vijayamala Software Engineer, DevOps Practice |
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Infrastructure automation, Terraform, Azure DevOps. Certified in AWS and Azure. |
