You might be familiar with DevOps, MLOps or AIOps but have you ever heard of DataOps? Most people might not be familiar with that concept. DataOps is basically a collection of workflows, technical practices and architectural patterns. It speeds up innovation, ensures continuous delivery and reduces complexity.
It can also improve the quality of your data and minimize the cycle time. Due to this, businesses can resolve their problems quickly by increasing employee engagement and productivity. The best part is that your analytics team won’t have to ditch their existing tools in order to implement DataOps in your organization. Tools will remain the same but methodology and philosophy will change.
All this might encourage you to implement DataOps in your organization but did you know how to implement it the right way? No, don’t worry. This article will help you by breaking the whole process down into seven easy-to-follow steps.
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In this article, you will learn about seven easy steps to implement DataOps in data analytics.
7 Steps For Implementing DataOps In Analytics
Follow these seven steps to implement DataOps in your analytics.
1. Conduct Logic and Data Test
The first thing you need to do is to test your exciting data analytics pipeline to figure out whether it is functioning properly or not. Conduct tests that check everything from inputs, outputs and even the business logic. you are applying at every stage of data analytics. This will assist you in detecting errors and also alert you by showing you the warning signs if there are any. Additionally, this will allow you to maintain the highest level of data quality. Try to automate the testing process as manual testing takes a lot of time and effort. Not only that, manual testing can severely hamper your ability to ensure continuous delivery. Automated testing also helps your business ensure continuous delivery, which is crucial for success in today’s fast-paced business world.
2. Use a Version Control System
Irrespective of how much raw data you have at your disposal, it is useless until you extract actionable insights from those huge data sets. Source code can play an important role in ensuring that you achieve that goal by controlling the data analytics pipeline from one end to another. Since the analytics files are present in different places, it can make things difficult. Lack of governance and control makes the situation even worse. That is where a revision control tool can come in handy.
It makes it easy to manage and store changes you make to your code. These version control tools can also offer users some kind of disaster recovery or DDoS protection by organizing and storing code in a code repository. This means that you can access your code anytime you want it. In addition to this, it can also help the development team to align their efforts.
3. Branch and Merge
Let’s say, you want to make changes to your code. You will go to the version control system and look at all the copies of the code which is stored there then make changes to a local and private copy of the code. The changes you make to your local copies are known as branches. One of the biggest advantages of the version control system is that it enables multiple developers to work on different branches simultaneously.
Once the developers are done making changes and those changes are then verified through testing. After going through verification, it is merged with the copies stored in the version control system, which then becomes a part of the main codebase. The benefit of branching and merging is that it gives your analytics team freedom to conduct their own tests. This gives them more room for experimentation
4. Use Multiple Environments
Most organizations maintain a production database and employees have access to that database. The problem with the approach is that it is inefficient and could lead to conflicts as well. The main reason for this is that data analytics professionals need access to a private copy of relevant data which is not always possible, which creates problems. Even though they maintain their own local copy of the code, they still need a private copy of relevant data. With most enterprises working with multiple environments concurrently, you need to rely on cloud storage to reduce dependencies and minimize the risk of conflicts.
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5. Reuse and Containerize
Since most data analysis teams work independently, they fail to take advantage of each other’s work. You can fix this problem by reusing code. Start off by componentizing functionalities so they can easily be shared between different team members easily and everyone can benefit from it. If you are working with too many complex functions, you can use container technology to put all of them inside a single container. If you are creating a lot of custom functions, this tactic can come in handy.
6. Parameterize Your Processing
When you are designing your data analytics pipeline, you need to answer the following questions.
- Which data sets should be used?
- Will you use your data warehouse for production or testing purposes?
- Will you apply filters on data?
- Should you include specific workflow steps?
Answering all these questions will give you a clear picture of creating a data analytics pipeline. This is important because all these conditions will be coded in your data analytics pipeline. Always design your data analytics pipeline for run-time flexibility.
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7. Overcome Fear of Failure
Nothing haunts data analytics professionals more than letting poor quality data reach its users or deploying changes that cause a system-wide breakdown. To fix these problems, businesses needs
- Value Pipeline
Value pipeline is where data flows into production and create value for the enterprise
- Innovation Pipeline
An innovation pipeline is a framework that leads to the sustainable and systematic application of innovation
Both these pipelines intersect in production.
Have you implemented DataOps in your organization? How did it help you in accelerating your business growth? Share your feedback with us in the comments section below.
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