In the global, increasingly growing IT industry, we hear the term ‘DevOps’ almost daily. But how often do you encounter ‘DataOps’ related topics?
We all use some of its practices, but it still seems relatively unfamiliar to many. In a world where data is the core of almost everything we do, it’s becoming just about crucial to know and use DataOps. This blog will introduce you to the ‘DataOps’ concept and some of its best practices helping you to gain base data management knowledge.
So, what is DataOps?
As DevOps focuses on improving software development management, DataOps makes data analysis workflows more efficient. DataOps, short for “data operations”, is an agile-oriented methodology that gathers best practices, processes, and technologies employed in data management. You can get some great tips on applying the Agile methodology in our earlier blog with Agile and Scrum tips.
DataOps follows the data from its source through the final product, optimizing the complete process. According to the definition of DataOps in TechTarget short blog:
“Inspired by the DevOps movement, the DataOps strategy strives to speed the production of applications running on big data processing frameworks. DataOps also seeks to liberate silos across IT operations, data management and software development teams, encouraging line-of-business stakeholders to work with data engineers, data scientists and analysts. The goal is to ensure the organization’s data can be used in the most flexible, effective manner possible to achieve positive and reliable business outcomes.”
The DataOps Lifecycle
The previous photo represents the so-called DataOps lifecycle created based on the model from the Snowflake data platform blog named The Rise of DataOps: Governance and Agility with TrueDataOps.
In practice, it consists of the following key steps:
- Plan – Every process needs a good plan. It’s important to define how business requirements can be addressed using data analytics. You should know what steps should be taken and which technologies should be used. Most often, it’s also needed to specify the budget and define performance requirements.
- Develop – Adequate code and data pipelines should be developed to effectively work with data, including data analysis, transformation, and ingestion. Based on the requirements and team preferences, code is mainly written in Python, R, SQL etc.
- Build – Developed models should be built into a functional whole in this phase.
- Test – Data should be tested to verify if it matches the defined business logic and desired output. Data should also go through base analysis checks.
- Release – Data is released into the test environment for further validation.
- Deploy – Once validated, data is deployed into the production environment.
- Operate – Data product is delivered, and stakeholders are asked for feedback. Any deviations from the desired output should be fixed.
- Monitor – This step includes observing the complete process continuously. Code running against data should be observed, and if any problems show up, they should be handled as soon as possible.
The DataOps lifecycle is a process that begins with raw data and ends with real insights. Now, let’s look into the best practices to apply throughout this cycle.
Top DataOps practices to make your data management extra efficient
In this paragraph, we’ll focus on some of the best practices in DataOps which will make your data management more accessible and efficient.
Utilize the agile approach
Advice is to start small and build up on it. Instead of building everything at once, we should begin from specific components and data subsets and then scale the DataOps process step by step.
Agree with stakeholders on what good data quality looks like
It is essential to clearly define measures for good data quality to use the most of your available time and potential. You should be clear on who will use the data product, in which way they will be using it and its final purpose. These key performance indicators (KPIs) should be revisited periodically to ensure all requirements changes are updated accordingly.
Automation is the time-saving key
Everything that can be automated – it should! This, of course, refers to iterative tasks that your team uses frequently. If you encounter a one-time task for which automation may not be simple, there is no point in investing your time in automation when you could be resolving that task much faster manually. However, investing time in the automation of repeating tasks can be a game changer for your team. Besides saving time, you will also reduce the impact of human errors.
Define your MVP and then evolve
Data products don’t need to be perfect from the start to satisfy basic requirements. The suggestion is to define a minimum viable product (MVP), test, and release it. Then, if possible, monitor it in production to improve it, working together with stakeholders and using constructive feedback. You will also have real insight into your data product so you can detect patterns and possible issues as they arise, ultimately improving your data product over time.
Empower business stakeholder with self-serving tools
Avoid being a bottleneck for stakeholders seeking basic information. Enable self-service access to the right tools, allowing them to retrieve the data they need independently. This promotes efficiency within your team and empowers stakeholders to access data without unnecessary assistance.
Know the priorities
Recognize that not all data is equally important. Begin by focusing on the most important data assets – the ones that will be the main deal-breakers for the stakeholders.
Handle data sensitivity
It is important to have in mind which type of data we are dealing with. Sensitive data always requires extra security measures, and teams need to be aware of data confidentiality and special permissions it requires.
Ensure efficient data distribution
Investing time in optimizing data delivery for all users can save time and energy for everyone involved.
I can not stress enough how important this is! Clear and comprehensive documentation is a revolutionary element in data-related processes (in any process, really!). It makes knowledge sharing easier and also allows you to address and fix issues more promptly.
Using DataOps in your processes is an ongoing effort that requires continuous monitoring and taking action. This involves feedback from stakeholders, addressing new requests, fixing detected issues, and optimizing the process in general.
Simplify your (data) life – use DataOps!
Implementing DataOps practices in your teams is a significant step forward. Today, there are also many tools available, depending on your needs, which make DataOps implementation in your processes much easier. For example, there’s DataKitchen DataOps platform, which streamlines and optimizes data analytics and data engineering processes in organizations with various solutions. There are also many other useful tools, such as:
- dbt (an open-source tool that transforms and manages data in the cloud)
- Apache Airflow (an open-source platform for instrumenting complex data workflows)
- Atlan Data Governance (an active data governance platform offering scalable ways to secure data while ensuring data accessibility)
In an interesting article explaining 3 Examples of AI at Work in DataOps you can read about examples of DataOps real-life implementations. And not just any examples, it’s about some of the most popular organizations in the world (Uber, Netflix, and Airbnb). These companies have utilized the power of DataOps to boost their productivity and overall success significantly.
The benefits of embracing DataOps are uncountable, even though we’ve only “scratched the surface” of the DataOps world in this blog. The advantages can clearly be seen, and DataOps has all the potential to lift your organization to a higher level. The real question is, are you ready to dive into the DataOps world and transform how you manage your data?
If you found this useful, check out other Atlantbh blogs!