DataOps vs DevOps: What are the Key Differences?
In the following areas, compare and contrast DataOps vs DevOps and the key differences
While the DevOps methodology has taken over software development, data teams are just beginning to see the advantages that a similar approach can provide for them.
DataOps is a relatively new field of study. The DataOps platform relies heavily on data pipelines. DataOps takes an automation-first, CI/CD-like approach to building and scaling data products, much like DevOps does for software development and operations.
Although DataOps and DevOps share many conceptual similarities, the responsibilities and skill sets of DevOps and DataOps engineers are vastly different when it comes to implementation.
The main difference is that DataOps focuses on breaking down silos between data producers and consumers to make data more reliable and valuable, while DevOps is a methodology that brings together development and operations teams to make software development and delivery more efficient. DataOps vs DevOps are discussed here.
DevOps Lifecycle:
There are four phases to a typical DevOps lifecycle. Planning, Creating, Providing, and Constant Improvement are all of them.
1. Planning:
This is the brainstorming stage; Depending on their importance, tasks are created and put on hold. Backlogs will arise from multiple products. Agile methodologies like Kanban or Scrum are utilized because the waterfall method does not work well with DevOps tasks.
2. Develop:
Coding, writing, unit testing, reviewing, and integrating code with the existing system are all part of this phase. Upon fruitful advancement of code, the equivalent is ready for arrangement into different conditions. DevOps teams automate manual and routine procedures. They gradually increase to achieve stability and self-assurance. Continuous deployment and continuous integration originate here.
3. Delivery:
The code is put into an appropriate environment during this phase. This could be stage, pre-production, prod, etc.
The code is deployed consistently and reliably no matter where it is used. By simply typing a few lines of code, the Git programming language has made it simple to deploy code on almost all popular servers.
4. Operate:
This is the stage that includes checking, keeping up with, and fixing applications underway. The actual location where downtime is discovered and reported is here. During the operational phase, DevOps teams use tools like PagerDuty to find issues before their customers find out about them.
DataOps cycle:
There are eight stages in a DataOps cycle: developing, integrating, testing, releasing, deploying, managing, and keeping an eye on progress. For a DataOps infrastructure to run smoothly, a DataOps engineer needs to be well-versed in all of these phases.
1. Planning:
setting KPIs, SLAs, and SLIs for the quality and availability of data in collaboration with the product, engineering, and business teams.
2. Development:
constructing the machine learning models and data products that will underpin your data application.
3. Integration:
incorporating the code and/or data product into your current data and technology stack. For instance, if you want the dbt module to run automatically, you could integrate a dbt model with Airflow.
4. Testing:
Verifying that your data conforms to business logic and meets fundamental operational requirements (such as the uniqueness of your data or the absence of null values)
5. Release:
Putting your data into a test set.
6. Deployment:
Putting your data together for production.
7. Operate:
Putting your data into dashboards and data loaders used by applications like Looker and Tableau to feed machine learning models.
8. Monitor:
Monitoring and alerting regularly for any data anomalies.
