There has been a lot said about DevOps in the world of software and application development, but have you heard about DataOps? If you’re unsure what DataOps is, you’re in luck since we are about to dive into this new IT discipline and why it’s an essential factor in today’s development world.
What Is DataOps?
DataOps (short for "data operations") is a methodology that gathers DevOps teams, data scientists, and data engineers to bring agility and speed to the end-to-end pipeline process, beginning with the collection and ending with delivery. It brings together the Agile framework, DevOps, and lean manufacturing.
DataOps provides:
- Data integration
- Data validation
- Metadata management
- Observability
By facilitating effective data operations and a reliable data pipeline, DataOps delivers accurate, actionable information with shorter development and delivery cycles. DataOps helps you align your data management processes with your expectations for that data.
What Are the DataOps Principles and Manifesto?
Like any new self-respecting IT methodology, DataOps has a manifesto, presented as a series of guiding principles. Considering that Agile has a manifesto and is a part of the DataOps process, this is hardly surprising.
1.Continually Satisfy Your Customer
Keep the customer satisfied by continuous and early delivery of valuable analytic insights, ranging from a few minutes to weeks.
2.Value Working Analytics
Acknowledging that the primary measure of data analytics performance is how well insightful analytics are delivered, using a mix of accurate data with robust frameworks and systems.
3.Embrace Change
Customers’ needs evolve, and we embrace this to create a competitive advantage. The most agile, effective, and efficient method of customer communication is face-to-face conversation.
4.It’s a Team Sport
Analytic teams always have a variety of skills, tools, roles, and titles. Diverse backgrounds and opinions foster productivity and innovation.
5.Daily Interactions
Analytic teams, customers, and operations need to work together every day to finish the project.
6.Self-Organize
Self-organizing teams provide the best analytic insight, algorithms, architectures, designs, and requirements emerge from self-organizing teams.
7.Reduce Heroism
Analytic teams must make an effort to reduce heroism and instead create scalable and sustainable data analytic processes and teams.
8.Reflect
Analytic teams should take the time to self-reflect regularly on customer and team member feedback and operational statistics. This principle helps fine-tune team performance.
9.Analytics is Code
Analytics teams use many different tools that access, model, integrate, and visualize data. Each tool generates codes and configurations.
10.Orchestrate
You must orchestrate data, code, tools, and environments from beginning to end.
11.Make it Reproducible
Teams need reproducible results; thus, they must version everything. This process includes low-level software and hardware configurations, data, and the configuration and code specific to every tool in the chain.
12.Disposable Environments
Minimize costs by giving analytic team members safe, isolated, and easily disposable technical environments where they can experiment.
13.Simplicity
It’s essential to maximize the amount of work not done. Teams enhance agility by paying continuous attention to good design and technical excellence.
14.Analytics is Manufacturing
Analytic pipelines are comparable to lean manufacturing lines. Thus, you must focus on process-thinking intended to achieve continuous efficiency in analytic insight manufacture.
15.Quality is Paramount
Analytics pipelines must be created with a foundation that automates the detection of code, configuration, and data abnormalities and provides continuous feedback for error avoidance.
16.Monitor Quality and Performance
Continuously monitor quality and performance measures to find unexpected variations and generate operational statistics.
17.Reuse
Improve efficiency by avoiding repetitive work done by the team or individuals.
18.Improve Cycle Times
Always work towards minimizing the time and effort needed to turn a customer’s need into an analytic idea, develop it, release it as a repeatable production process, then refactor and reuse the product.
What’s a DataOps Framework?
The DataOps framework consists of five essential and distinct elements. The elements are:
1.Enabling Technologies
These technologies include artificial intelligence (AI), machine learning (ML), data management tools, and IT automation.
2.Adaptive Architecture
Adaptive architecture supports continuous innovations in major processes, services, and technologies.
3.Data Enrichment
This data is intelligent metadata created by the system and put into useful context for timely and accurate analysis.
4.DataOps Methodology
This methodology involves building and deploying your data pipelines and analytics, following your model management and data governance.
5.People and Culture
You must create a collaborative culture among your IT department and cloud operations, data architecture and engineering teams, and data consumers like data analysts and scientists. This culture helps put the right information in the right place at the right time to maximize your organization’s value.
What’s the Difference Between DataOps and DevOps?
The chief difference is scope. DevOps, which came first, fosters collaboration between the development and operation teams within IT. It entails one delivery pipeline, from code to execution.
DataOps, on the other hand, builds and demands collaboration across the whole enterprise, from the IT people to the data experts to finally the data consumers. DataOps has multiple pipelines that execute data flows and train data models.
So, DevOps makes your IT department more effective, while DataOps makes the entire organization more effective.
Will DataOps Be the Latest Hot Thing?
Communication is essential in any successful relationship, and that includes the IT world. DataOps was designed to solve many of the communications problems that arise between stakeholders and developers, and plenty of organizations can benefit from improved collaboration.
DataOps also emphasizes improved data utilization, which in turn leads to more informed decisions and strategies, ultimately manifesting itself in a healthier bottom line and a more extensive, more loyal customer base.
Many organizations already use DevOps, so DataOps is the next logical step.
The jury’s still out whether DataOps will be the new development darling, but it’s still just getting traction. According to this survey taken in 2018, 73 percent of companies surveyed intend to invest in DataOps to manage their data teams.
The prospects look good, but if the adoption of DevOps is any indicator of things to come, it will be a gradual process.
Interested to begin a career in DevOps? Enroll now for the DevOps Certification Course. Click to check out the course curriculum.
What Types of Professionals Make Up the Ideal DataOps Team?
DataOps teams need the right people to make the process work at optimal levels. Your DataOps teams should incorporate any or all the following:
- Business intelligence analyst
- Analytics manager
- Data analyst
- Data architect
- Data engineer
- Data scientist
- Data security/cybersecurity
- DataOps engineer
The Shape of Things to Come
To sum it up, DataOps is the process of orchestrating people, processes, and technology to deliver high-quality, reliable data to the right people rapidly. Let’s see if and how it catches on. In the meantime, if you are thinking about getting a head start in the relatively new field of DataOps, a solid foundation in the more established world of DevOps is a great way to get started. Check out our Post Graduate Program in DevOps, offered in collaboration with Caltech CTME, to move your career forward today!
