This post was originally published by Sajjad Hussain at Medium [AI]
The demand for access to data assets and data products is increasing day by day. To gain competitiveness in the market, DataOps is an indispensable subject. The data (and management) team and their platforms cannot keep up with the rhythm of the DevOps-equipped teams demanding them, and this state has driven the development of DataOps (of the data team).
In short, DataOps brings together data scientists, analysts, developers, and operations staff to work together on the entire product/service life cycle from the design stage to production support.
DataOps VS DevOps
DataOps does more than follow DevOps principles and apply it to data analysis. Although this can achieve similar purposes of improving quality and shortening the cycle, it is not the same thing in essence.
DevOps relies on automation to accelerate the build life cycle. The goal is to achieve continuous and continuous software integration and delivery by allocating IT resources on demand and through automated code integration, testing, and deployment.
In other words, DevOps collaborates with development and operations teams and provides them with tools to make their work better and more efficient. The results are reduced deployment time, faster product delivery to the market, fewer code issues, and shorter time required to resolve issues.
DevOps allows top companies to reduce the release time from months to minutes, or even seconds in some cases. This provides them with an incredible competitive advantage, which is necessary in today’s fast-paced economy.
In essence, companies like Amazon and Google can release software multiple times a day, thanks to DevOps. Without DevOps, it is impossible.
The goal of DataOps is to improve the efficiency of data analysis. To this end, DataOps uses agile development principles to improve the efficiency and effectiveness of data teams and users.
This means that the data team can publish new analysis data in a short cycle of incremental methods, namely Sprints, thereby greatly reducing the waiting time. Research also shows that this agile development approach has fewer problems when a software development project is completed. In the field of data, this means that companies can respond to customer needs and pain points faster, thereby significantly increasing the speed of delivering value.
However, compared to DevOps, DataOps has an additional component that is constantly changing the data pipeline. The original data is processed from the side of the data pipeline, and displayed on the other side in different forms (reports, views, models, etc.). This data pipeline is often referred to as the data producer/consumer model.
In data flow, DataOps plays a vital role because it directs, monitors, and manages the data pipeline. Statistical process control ( ensuring that statistical information is kept within acceptable limits, thereby significantly improving the quality, efficiency, and transparency of data analysis) is one of the more powerful tools for this purpose.
DataOps combines the advantages of DevOps, agile development, and statistical process control.
Purpose of DataOps
Data is valuable and more valuable than ever before, and many companies have realized this. Data itself can become a product. However, only when the company effectively collects, processes, and transforms it into actionable insights (business insights that can be used to actually guide the company’s behavior) will the data truly reflect its value.
The problem is that how to collect and analyze data effectively is not clear to ordinary companies. Based on the principle of “we will collect data extensively and then figure out how to deal with it”, companies usually adopt such a seemingly all-encompassing method, which does more harm than good.
The company then formed a data team and assumed that the team could miraculously turn garbage into gold. This usually requires much more work than is actually required, and it is difficult to achieve the desired results. Of course, it is almost impossible to provide actionable insights on time to meet the needs of the DevOps team’s efforts to bring its code to market.
DataOps ended this chaotic situation and turned it into a smooth process, and the data team did not need to spend time to solve these problems. They don’t waste time trying to turn bad raw data into useful data. Instead, they can focus on the important thing, which is to provide actionable insights.
DataOps can ensure the availability of input raw data, ensure the accuracy of the results, focus on the value of personnel and the value of joint cooperation, so that the data team is always at the center of the company’s strategic goals. After all, they no longer need to spend a few months to produce results, and they are as efficient as the DevOps team.
DataOps evolution history
DataOps experienced major development in 2017. Therefore, with the growing interest of enterprises in this discipline, it has spawned the development of a strong supplier network that provides development and sales of various related products and services.
Any DataOps platform depends on five basic functional components, they are
- Data pipeline orchestration: DataOps requires a guided workflow based on graphics, which involves all steps related to data integration, data access, visualization and modeling;
- Testing and production quality: DataOps not only tests and monitors the quality of all production data, but also tests any changed code during the deployment phase;
- Automated deployment: DataOps will continue to obtain code and configuration from the development environment and migrate to the production environment;
- Data science model deployment and sandbox management: DataOps is also responsible for creating a replicable development environment and moving the model into the production environment;
- Other functions that need to be supported: code and artifact storage, parameter and security key storage, distributed computing, data virtualization, version control, and test data management.
Although DataOps is popularized, it is still a new concept and has not yet been widely used. The widespread application of DataOps may be limited by the available frameworks and solutions, as well as the lack of clear guidelines to be followed.
Even so, this is still the beginning of a market revolution, because companies are trying to explain their concepts. Data scientists and IT experts still have difficulty determining where to start and how to define success indicators.
On the Security of DataOps
A report on the results of the 451 survey shows that DataOps can accelerate the speed of innovation for global companies, and can also help them solve serious security and compliance issues, so they have turned to DataOps. In fact, 66% of respondents said that higher security and better compliance are the primary reasons for their adoption of DataOps.
Since many enterprises have experienced data breaches, they pay more attention to data security than before. At the same time, regulators are also facing greater pressure on data privacy. As a result, the company turned to DataOps to develop and implement a consistent data governance strategy, while allowing data to flow quickly while being completely secure.
As the number of people who need to access data increases, 68% of respondents said it is important to protect data shared with internal and external users.
Most data leaks in news are usually caused by external threats. However, in fact, the main threat often comes from internal users. Although it is not necessarily intentional, negligence often leads to serious consequences. This is also due to the organization’s lack of a unified and consistent security strategy and methods for implementing these strategies.
As long as the data has the correct data platform, DataOps can provide the same kind of security methods needed to ensure data security, regardless of the visitor, regardless of the technology used, this unified method can work in all areas of the organization.
Organizations and personnel supporting DataOps have issued a manifesto, which contains eighteen principles, summarizing the best practices, ideas, goals, missions, and values of implementing DataOps.
The Manifesto places individuals and their mutual actions above processes and tools. They focus on job analysis rather than comprehensive documentation. They promote customer collaboration instead of focusing on contract negotiation. They advocate experimentation, iteration, and feedback, rather than spending a lot of time on pre-design. They also believe that isolated responsibilities should be eliminated and cross-functional operational ownership should be advocated.
The details of the DataOps Declaration are as follows :
On the future of DataOps
Although DataOps has not yet been widely used, its future is obvious: DataOps will be retained and widely used by the waves. Like DevOps, we will see the value of related teams and positions continue to rise.
For example, before agile development, the value of release engineers was greatly underestimated, especially when compared with software developers. Now, companies that implement DevOps fully respect the value of release engineers. In addition, it is well known that DevOps engineers are one of the highest paid positions in software engineering. DevOps engineers are very difficult to recruit, even if they do not have a university degree, as long as they have the appropriate knowledge and experience, the company is willing to hire. This is also becoming a trend.
A similar thing may happen to the position of DataOps engineer. Regardless of the title of the employee, by implementing a reliable DataOps strategy, data analysts, data engineers, and data scientists can receive greater attention. However, this may take some time to achieve. DataOps is still a new concept. Despite many discussions around it, there are still some limitations and restrictions that hinder its wide application.
Of course, as DataOps becomes more and more popular, these limitations and restrictions will gradually disappear. In the near future, we may see more discussions about the principles and guidelines that can be successfully implemented. Just as DevOps plays a vital role in the management of IT infrastructure, DataOps is changing the way data is available, shared, and integrated. As more and more data is collected and/or produced every day, effectively managing data becomes an inevitable choice for more and more companies.
This post was originally published by Sajjad Hussain at Medium [AI]