Running a team in a company dealing with a large volume of data can be pretty challenging. It’s difficult to monitor all those processes and people which reflects on the error rates and the quality of team collaboration.
The good news is that you can solve all those problems by implementing DataOps.
DataOps, as a set of technical practices, cultural norms, and workflows allows data engineers to save time and resources and separate high-quality data from low-quality data in no time. It enables companies to lower their error rates and have more transparency of results.
This practice basically introduces Agile methodology into data analytics.
Now, all this probably seems interesting but where do you start? Well, it’s actually pretty simple – all you need to do is follow the next 6 steps and you’re good to go.
[Source: Unsplash]
- Build a new test for each change.
The first step to properly implementing DataOps or Data Operations involves testing as soon as you add new data.
Typically, companies start by adding a large amount of data and new features. Once they’re finished, they perform manual tests to find out if what they’ve created makes sense and if the changes affected what was there before.
Now, don’t get me wrong, manual testing is not dead. It cannot be entirely replaced by automated testing. But there’s no reason to wait for the deployment to finish to start testing. It’s way better to perform an automated test each time you add a new piece of information. That way, new features won’t mess up the existing system.
Furthermore, you’ll be able to free up the team’s time to focus more on new development, instead of dealing with unintentional breakages or issues.
Of course, you don’t need to implement complex tests right off. Start with simple tests, even they will be able to catch an error before it is released to the users.
- Start using a version control system.
It’s not unusual to see companies struggling to enhance collaboration between employees, especially if you’re handling a large volume of data. Furthermore, other problems such as storing several versions of files and difficulties when it comes to data backups make things even more complicated.
A great way to handle these obstacles is to start using a version control system.
Version control system platforms keep track of all changes your team members make and allow you to roll back to the previous version whenever you need to. It also makes sure everyone is working on the latest version of the file simultaneously.
Some of the best version control systems include GitLab, GitHub, AWS CodeCommit, Beanstalk, and Team Foundation Server.
- Create branches and marge.
Once you start using the version control system of your choice, you’ll need to learn what branching and merging are and how to use them.
A large amount of data can’t be handled by one data engineer. Typically, you have multiple data engineers working on the project at the same time. To do that, they’ll need to access the version control system, pull a copy of the part of the data they need, and start working on it locally. The local copy they’re working on is called a branch.
When they’re done with their part, they’ll test it and put it back into the version control system and this is called merging.
It’s branching and merging that allow multiple data engineers to work at the same time without endangering the source code and what others are working on.
- Store data on multiple environments.
Storing data using only one environment is risky. First things first, if anything happens to your cloud service provider or your on-premise storage environment, you’re doomed. Also, data engineers need to have local copies of data to be productive and make the branching and merging processes work.
So, instead of storing data in only one environment, we advise you to use at least two of environments of which one should be local for data engineers.
Now, some people believe this is too expensive but not anymore – one TB of cloud storage costs less than $25 per month. This is typically enough space to work on but you can always take on more if you feel like you need to.
- Containerize code.
Containerization basically means that you place one component and its environment, dependencies, and configuration, into a unit called a container. These containers can be consistently deployed in multiple environments and are easy to transfer from one team to another without fearing something won’t work.
For instance, one component might need to call a custom tool, use FTP, run a python script, and use other specialized logic. This component might be difficult to set up because it requires a specific set of tools to work properly.
This is when containers come in handy – even data engineers who aren’t familiar with what’s inside of the container can use it.
- Be patient and start slow.
The key when it comes to implementing DataOps in your company is to start slow. If the tests you create for bits of code are a bit too complex, simplify them. If deployment is taking too long, try to identify the places where you can include automation.
Don’t forget to talk to your teammates and ask them for feedback. This is incredibly important when you’re implementing a big change.
Conclusion
Implementing DataOps may seem complicated but it’s not. Start making some small changes and just observe.
The goal is to avoid mistakes and the displeasure of your customers, so make sure to have observability across the entire data pipeline, not just the end. Of course, this shouldn’t affect the performance of your team. If it does, try to find a way to navigate these changes differently.
At last, keep in mind that you don’t necessarily need to start with Step #1. You may find it easier to start by containerizing code than by creating tests for each new piece of data. Remember – you do you because each company and each team is different. You can check some at this business listing.
