DevOps Meets DataOps: The Next Frontier in Agile Data Engineering
Quote from jiten patil on July 2, 2025, 9:38 amData is a growing concern for enterprises, which are generating more data. The challenge now is to make that data accessible, reliable, secure and useful in real time. Enter dataOps, the intersection of agile operations and data engineering.
DevOps practices are now driving DataOps innovations. This article examines how DevOps practices and tools are reshaping data pipelines. It also explains how DevOps courses in Pune or DevOps programs in Pune will prepare you to take on this new frontier.
What is DataOps?
DataOps consists of a set practices that apply DevOps concepts such as automation, CI/CD monitoring and collaboration, to data workflows.
The focus is on:
Reduce the time it takes to get insights from raw data
Improve data quality and reproducibility
Ensure governance and data protection
Automating data transformation, testing and deployment
DataOps, in other words is the version of DevOps for the data world.
DataOps and DevOps: How they work together
Compare DevOps and DataOps:
DevOps Principle DataOps: How it Works Continuous Integration Validate schema and data models automatically Continuous Delivery Instantly deploy new pipelines and reports Infrastructure as Code Provision data lakes, Kafka clusters via Terraform Watching Data drift, for example, can be detected in pipelines. You can also find out more about the Automated Vehicles by clicking here. Automated ingestion, cleaning, and transformation tasks
The course in Pune teaches the DevOps Foundations in depth.
Tools that bridge DevOps with DataOps
DevOps Tool DataOps: How it powers the system Airflow Automating ETL/ELT jobs Git Version control of data pipelines and models Jenkins CI/CD for Spark/BigQuery Pipelines Terraform IaC is a cloud-based infrastructure for data. Kubernetes Data microservices and Notebooks Scalable Execution Great Expectations Data quality check in automated tests
Enroll in Devops classes in Pune to learn how to master these tools.
Retail analytics is a real-life DataOps use case.
Imagine a retailer that collects:
Sales data from POS Systems (JSON).
Customer data from CRM (SQL).
Textual feedback from social media
Without DevOps, you cannot:
Manual ingestion - delays
Inconsistent quality checks
Dashboard updates once a week
DataOps powered By DevOps
Airflow automatically pulls data every hour
Integrity is validated by Great Expectations
CI/CD introduces new ML models in production dashboards
This type of integrated automation can only be achieved by a deep understanding of devOps automation.
DevOps + data governance: a crucial combination
DevOps isn't just about delivery; it also strengthens data management.
Secrets (e.g. API keys) can be safely managed with Vault
IAM roles automate access controls
Logging provides audit trails to ensure compliance (e.g. GDPR).
Training in DevOps in Pune includes modules that combine security and CI/CD. This ensures that governance is baked right into each pipeline.
Career Scope: DataOps + DevOps Engineer Roles
New job titles are emerging as DataOps adoption grows:
DataOps Engineer
DevOps For Data Platforms Engineer
Data Reliability Engineer
ML Ops + DevOps Architect
The following professionals should be able to:
Manage hybrid cloud data pipelines
Automated ingestion and reporting
Monitor data pipeline health
Ensure zero-downtime data deployments
This course will help you become a strong candidate to fill these roles. It includes a practical experience of Kafka Spark and Snowflake.
What you learn in DevOps classes for DataOps
Here's a list of the top DevOps course in Pune that you can take.
CI/CD pipelines for Data Projects
GitHub – Jenkins – Airflow – Data WarehouseIaC Data Infrastructure
Provisioning Redshift, BigQuery, S3 buckets via TerraformMonitoring pipelines
Alerts to failed DAGs and data drift using PrometheusSecuring pipelines
vault + role-based access for analysts and data scientistsVersion control
Tracking SQL code and schema evolution with GitThere are many other topics covered by DevOps Training in Pune, including real-time dashboards and assignments.
DevOps automation in DataOps - What you should know
You want to reduce the time it takes to process data manually from 6 hours to just 6 minutes?
automation is the key to DevOps. Here's how it works:
Data Ingestion (using Kafka REST API or)
Transformation logic (via PySpark or dbt)
New analytics dashboards are deployed
Rollback and alerting if the job fails
When your team has automated decision-making, it will be real time and not every week or month.
DevOps and DataOps: Best Practices to Integrate the Two
Keep Your Pipelines Modular
Break down into microservices each with a single function
Version All
All configurations, including SQL, Python and YAML, must be migrated to Git
Use Data Contracts
Make sure teams are on the same page about API and schema expectations
Automated Quality Checks
Fail quickly if the data is corrupted or unexpected
Start small, scale fast
Test pilot projects before full-scale implementation
Want to get hands-on with these? Attend a certified Devops training in Pune.
DevOps is the Backbone of DataOps
Companies want to:
Real-time dashboards
Zero-lag insights
Data flows that are secure and automated
Model-to-market cycles are shorter
DevOps is the foundation of all this.
DevOps will help you become future proof, whether you are a data analyst or software developer.
Learn how to automate pipelines
Applying CI/CD for data
Mastering infrastructure for big data
Ensure security and compliance
Data is a growing concern for enterprises, which are generating more data. The challenge now is to make that data accessible, reliable, secure and useful in real time. Enter dataOps, the intersection of agile operations and data engineering.
DevOps practices are now driving DataOps innovations. This article examines how DevOps practices and tools are reshaping data pipelines. It also explains how DevOps courses in Pune or DevOps programs in Pune will prepare you to take on this new frontier.
What is DataOps?
DataOps consists of a set practices that apply DevOps concepts such as automation, CI/CD monitoring and collaboration, to data workflows.
The focus is on:
-
Reduce the time it takes to get insights from raw data
-
Improve data quality and reproducibility
-
Ensure governance and data protection
-
Automating data transformation, testing and deployment
DataOps, in other words is the version of DevOps for the data world.
DataOps and DevOps: How they work together
Compare DevOps and DataOps:
| DevOps Principle | DataOps: How it Works |
|---|---|
| Continuous Integration | Validate schema and data models automatically |
| Continuous Delivery | Instantly deploy new pipelines and reports |
| Infrastructure as Code | Provision data lakes, Kafka clusters via Terraform |
| Watching | Data drift, for example, can be detected in pipelines. |
| You can also find out more about the Automated Vehicles by clicking here. | Automated ingestion, cleaning, and transformation tasks |
The course in Pune teaches the DevOps Foundations in depth.
Tools that bridge DevOps with DataOps
| DevOps Tool | DataOps: How it powers the system |
|---|---|
| Airflow | Automating ETL/ELT jobs |
| Git | Version control of data pipelines and models |
| Jenkins | CI/CD for Spark/BigQuery Pipelines |
| Terraform | IaC is a cloud-based infrastructure for data. |
| Kubernetes | Data microservices and Notebooks Scalable Execution |
| Great Expectations | Data quality check in automated tests |
Enroll in Devops classes in Pune to learn how to master these tools.
Retail analytics is a real-life DataOps use case.
Imagine a retailer that collects:
-
Sales data from POS Systems (JSON).
-
Customer data from CRM (SQL).
-
Textual feedback from social media
Without DevOps, you cannot:
-
Manual ingestion - delays
-
Inconsistent quality checks
-
Dashboard updates once a week
DataOps powered By DevOps
-
Airflow automatically pulls data every hour
-
Integrity is validated by Great Expectations
-
CI/CD introduces new ML models in production dashboards
This type of integrated automation can only be achieved by a deep understanding of devOps automation.
DevOps + data governance: a crucial combination
DevOps isn't just about delivery; it also strengthens data management.
-
Secrets (e.g. API keys) can be safely managed with Vault
-
IAM roles automate access controls
-
Logging provides audit trails to ensure compliance (e.g. GDPR).
Training in DevOps in Pune includes modules that combine security and CI/CD. This ensures that governance is baked right into each pipeline.
Career Scope: DataOps + DevOps Engineer Roles
New job titles are emerging as DataOps adoption grows:
-
DataOps Engineer
-
DevOps For Data Platforms Engineer
-
Data Reliability Engineer
-
ML Ops + DevOps Architect
The following professionals should be able to:
-
Manage hybrid cloud data pipelines
-
Automated ingestion and reporting
-
Monitor data pipeline health
-
Ensure zero-downtime data deployments
This course will help you become a strong candidate to fill these roles. It includes a practical experience of Kafka Spark and Snowflake.
What you learn in DevOps classes for DataOps
Here's a list of the top DevOps course in Pune that you can take.
-
CI/CD pipelines for Data Projects
GitHub – Jenkins – Airflow – Data Warehouse -
IaC Data Infrastructure
Provisioning Redshift, BigQuery, S3 buckets via Terraform -
Monitoring pipelines
Alerts to failed DAGs and data drift using Prometheus -
Securing pipelines
vault + role-based access for analysts and data scientists -
Version control
Tracking SQL code and schema evolution with Git
There are many other topics covered by DevOps Training in Pune, including real-time dashboards and assignments.
DevOps automation in DataOps - What you should know
You want to reduce the time it takes to process data manually from 6 hours to just 6 minutes?
automation is the key to DevOps. Here's how it works:
-
Data Ingestion (using Kafka REST API or)
-
Transformation logic (via PySpark or dbt)
-
New analytics dashboards are deployed
-
Rollback and alerting if the job fails
When your team has automated decision-making, it will be real time and not every week or month.
DevOps and DataOps: Best Practices to Integrate the Two
-
Keep Your Pipelines Modular
-
Break down into microservices each with a single function
-
-
Version All
-
All configurations, including SQL, Python and YAML, must be migrated to Git
-
-
Use Data Contracts
-
Make sure teams are on the same page about API and schema expectations
-
-
Automated Quality Checks
-
Fail quickly if the data is corrupted or unexpected
-
-
Start small, scale fast
-
Test pilot projects before full-scale implementation
-
Want to get hands-on with these? Attend a certified Devops training in Pune.
DevOps is the Backbone of DataOps
Companies want to:
-
Real-time dashboards
-
Zero-lag insights
-
Data flows that are secure and automated
-
Model-to-market cycles are shorter
DevOps is the foundation of all this.
DevOps will help you become future proof, whether you are a data analyst or software developer.
Learn how to automate pipelines
Applying CI/CD for data
Mastering infrastructure for big data
Ensure security and compliance
Quote from john muller on October 16, 2025, 3:26 amThat’s an excellent exploration of how DevOps is evolving into the realm of DataOps, and you’ve touched on something truly transformative. It’s fascinating to see how automation, CI/CD, and infrastructure as code are no longer confined to app deployment pipelines but are now the backbone of real-time data systems. It’s as if DevOps finally met its data-loving twin, and now they’re building dashboards together instead of just shipping code!
One point worth expanding on is the growing role of observability in DataOps. Beyond just monitoring data drift or DAG failures, modern teams are embedding end-to-end visibility into data lineage, schema evolution, and even machine learning model performance. Think of it as DevOps’ “you build it, you run it” philosophy applied to data pipelines, “you ingest it, you monitor it.”
Another nuance lies in the intersection of security and governance. As companies accelerate automation, embedding secrets management, RBAC, and audit logging directly into the data lifecycle becomes non-negotiable. Vault, IAM, and compliance policies are no longer the supporting cast, they’re part of the main show.
For professionals looking to bridge DevOps and DataOps, pursuing certifications that strengthen cloud and automation skills is a strong move. For instance, the AWS Certified DevOps Engineer Professional exam helps build the mindset and technical breadth needed to handle both application and data workloads seamlessly.
In short, DevOps and DataOps aren’t rival siblings, they’re collaborative partners building a more intelligent, automated, and compliant data future. Or, as I like to say, they’re two halves of the same YAML file, one deploying apps, the other deploying insights.
That’s an excellent exploration of how DevOps is evolving into the realm of DataOps, and you’ve touched on something truly transformative. It’s fascinating to see how automation, CI/CD, and infrastructure as code are no longer confined to app deployment pipelines but are now the backbone of real-time data systems. It’s as if DevOps finally met its data-loving twin, and now they’re building dashboards together instead of just shipping code!
One point worth expanding on is the growing role of observability in DataOps. Beyond just monitoring data drift or DAG failures, modern teams are embedding end-to-end visibility into data lineage, schema evolution, and even machine learning model performance. Think of it as DevOps’ “you build it, you run it” philosophy applied to data pipelines, “you ingest it, you monitor it.”
Another nuance lies in the intersection of security and governance. As companies accelerate automation, embedding secrets management, RBAC, and audit logging directly into the data lifecycle becomes non-negotiable. Vault, IAM, and compliance policies are no longer the supporting cast, they’re part of the main show.
For professionals looking to bridge DevOps and DataOps, pursuing certifications that strengthen cloud and automation skills is a strong move. For instance, the AWS Certified DevOps Engineer Professional exam helps build the mindset and technical breadth needed to handle both application and data workloads seamlessly.
In short, DevOps and DataOps aren’t rival siblings, they’re collaborative partners building a more intelligent, automated, and compliant data future. Or, as I like to say, they’re two halves of the same YAML file, one deploying apps, the other deploying insights.
Quote from john muller on October 16, 2025, 3:27 amThat’s an excellent exploration of how DevOps is evolving into the realm of DataOps, and you’ve touched on something truly transformative. It’s fascinating to see how automation, CI/CD, and infrastructure as code are no longer confined to app deployment pipelines but are now the backbone of real-time data systems. It’s as if DevOps finally met its data-loving twin — and now they’re building dashboards together instead of just shipping code!
One point worth expanding on is the growing role of observability in DataOps. Beyond just monitoring data drift or DAG failures, modern teams are embedding end-to-end visibility into data lineage, schema evolution, and even machine learning model performance. Think of it as DevOps’ “you build it, you run it” philosophy applied to data pipelines — “you ingest it, you monitor it.”
Another nuance lies in the intersection of security and governance. As companies accelerate automation, embedding secrets management, RBAC, and audit logging directly into the data lifecycle becomes non-negotiable. Vault, IAM, and compliance policies are no longer the supporting cast, they’re part of the main show.
For professionals looking to bridge DevOps and DataOps, pursuing certifications that strengthen cloud and automation skills is a strong move. For instance, the AWS Certified DevOps Engineer Professional exam helps build the mindset and technical breadth needed to handle both application and data workloads seamlessly.
In short, DevOps and DataOps aren’t rival siblings, they’re collaborative partners building a more intelligent, automated, and compliant data future. Or, as I like to say, they’re two halves of the same YAML file, one deploying apps, the other deploying insights.
That’s an excellent exploration of how DevOps is evolving into the realm of DataOps, and you’ve touched on something truly transformative. It’s fascinating to see how automation, CI/CD, and infrastructure as code are no longer confined to app deployment pipelines but are now the backbone of real-time data systems. It’s as if DevOps finally met its data-loving twin — and now they’re building dashboards together instead of just shipping code!
One point worth expanding on is the growing role of observability in DataOps. Beyond just monitoring data drift or DAG failures, modern teams are embedding end-to-end visibility into data lineage, schema evolution, and even machine learning model performance. Think of it as DevOps’ “you build it, you run it” philosophy applied to data pipelines — “you ingest it, you monitor it.”
Another nuance lies in the intersection of security and governance. As companies accelerate automation, embedding secrets management, RBAC, and audit logging directly into the data lifecycle becomes non-negotiable. Vault, IAM, and compliance policies are no longer the supporting cast, they’re part of the main show.
For professionals looking to bridge DevOps and DataOps, pursuing certifications that strengthen cloud and automation skills is a strong move. For instance, the AWS Certified DevOps Engineer Professional exam helps build the mindset and technical breadth needed to handle both application and data workloads seamlessly.
In short, DevOps and DataOps aren’t rival siblings, they’re collaborative partners building a more intelligent, automated, and compliant data future. Or, as I like to say, they’re two halves of the same YAML file, one deploying apps, the other deploying insights.