By Shantanu Kaushik
DataOps or Data Operations is based on Agile methodology. It is an emerging trend for data analytics that shows the potential to revolutionize DevOps. It could be the next evolutionary step for DevOps methodology. DataOps is process-oriented and works to bring DevOps, data scientists, and engineers together to form a data-focused organizational structure with more productive tools and practices.
Alibaba Cloud has provided enterprises with the latest tools of the trade and platforms, such as IAC, to build and deploy. If you want to deploy with Alibaba Cloud, DataOps can be incorporated into your DevOps cycle easily.
DataOps is a practice that incorporates data-based products that extract business value by utilizing different technologies to bind a solution together. DataOps works from Infrastructure to the user as a solution that defines each step to extract more value and productivity.
DataOps has to enable a more productive and collaborative team effort from data teams and users. To achieve that, DataOps has to manage the collaboration by introducing Agile development into the mix with data analytics. Just like DevOps, innovation is key. With Agile, the data team releases new or upgraded analytics in short and continuous bursts.
Innovation in rapid release intervals enables the development team to access the cycles frequently and adapt to the requirements presented to maintain the Agile methodology. Here, setting the priorities is the main task, and priorities can only be managed with regular user feedback and metrics collection. This complete cycle accounts for a more responsive system, more responsive than ever seen before.
Upgrading from the traditional waterfall model has kept long development cycles at bay. DevOps has revolutionized the industry with continuous integration and delivery. Similarly, DataOps further revolutionizes the DevOps lifecycle by introducing data analytics into the mix.
The customer requirements are ever-evolving, and with the shift in business values, these requirements have accelerated. With Agile, organizations can quickly respond to customer requirements and accelerate the total development to deployment process to save time and add value.
With DevOps and Agile, the defects in production have considerably decreased, as it doesn't follow the sequential methodology of the waterfall model. The waterfall model had a long-development to deployment SDLC that hampered any quick integration cycles. DevOps helped overcome this shortcoming by introducing Continuous Integration and Delivery cycles.
Lean Manufacturing focuses on minimizing waste. It is a methodology that works to increase productivity and decrease any waste of resources. When utilized in sync with DevOps, this methodology applies to analytics development and deployment. The whole process helps orchestrate a data pipeline that enables better usage of resources.
Lean Manufacturing alongside Agile and DevOps enables a continuous flow of data that enters the pipeline from the side, gets processed by the analytics solution, and exits the pipeline as detailed reports and business models. This is the operations data if we compare it with the DevOps pipeline within a DataOps environment.
DataOps monitors, manages, and orchestrates the combined data and analytics. The flow of data is the most important factor. With DataOps, one has to measure the data and operations within a DataOps pipeline. DataOps utilizes the Statistical Process Control (SPC), which is a Lean Manufacturing tool to manage and monitor data based on its operational characteristics. With SPC, data is verified at every step of the analytical pipeline and accounts for massive improvements in efficiency and quality of data operations.
DataOps takes the most important bits of its pipeline from DevOps. Every piece of technology that evolves to something better and more equipped comes from old tech. In this case, DevOps is evolving, and DataOps is a step up in the SDLC game. DevOps optimizes the development code, while Agile enforces analytical assessment. Combined with SPC, they constitute the architectural foundation of DataOps.
The main goal of DataOps is to apply data analytics as a foundation for operations and development. This should seamlessly integrate the build, deployment, and maintenance of an application. The second goal is to improve how business goals are met. It is directly proportional to how the data is managed and how tools handle data.
It is abundantly clear that DataOps shares its DNA with DevOps. Alibaba Cloud and its inherent need to outperform industry standards have created an equilibrium that is hard to match. Within the DevOps pipeline, services like Terraform, Kubernetes, and microservices have the highest levels of productivity. These services, combined with DataOps, can enhance productivity and performance.
DataOps utilizes analytics as a primary measure to get insights into customer requirements. It is about understanding the ever-changing needs of the customer and evolving accordingly. It is about adapting to the evolutionary cycle by adequately changing the teams and processes to be more sustainable and scalable.
DataOps teams also seek to orchestrate data, tools, code, and environments from beginning to end to provide reproducible results. DataOps teams tend to view analytic pipelines as analogous to Lean Manufacturing lines and regularly reflect on feedback provided by customers, team members, and operational statistics.
In Part 2 of this article series on DataOps, we are going to compare DevOps and DataOps. We are also going to discuss best practices for DataOps and the future of DataOps. We will discuss where DataOps fits in the evolutionary cycle and how integrating it with a DevOps practice can provide your organization an edge over standard DevOps practices. In the end, we will focus on the process of building a DataOps team and how to extract productivity from that team.
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