DataOps: Intense Data Management and Faster Analytics

By HiFX Engg. Team | February 18, 2021 | 6 min read

As we travel through the AI era, efficient handling of data has been time-consuming and unmanageable. Notably, a process oriented automated methodology, named as DataOps, has aided the data and analytic teams with quality data and quicker data analytics. DataOps, which is a prerequisite for the success of an enterprise, has also proven to assure security, repeatability and scalability.

What is DataOps?

A data management methodology that controls the agile, seamless and fast processing of data from the input to the results is referred to as DataOps or Data operations. DataOps combines Agile methodologies, DevOps and statistical process controls (SPC), and applies them to data analytics. While DevOps assists in optimization of code, building of products and delivery through CI/CD pipelines, cloud formation templates, deployment, auto scaling and auto infra alerts; agile methodologies assist in data governance, gets easily adapted to requirements, and assures fast delivery for quicker feedbacks. Moreover, DataOps is also viewed as ‘lean manufacturing’, where consistent monitoring and verification of the data analytics pipelines is performed by the SPC. Further, SPC ensures that the statistical values lie within the executable range, guarantees data quality and efficient data process, and alarms an error on detecting errors.

DataOps is referred to as a prominent agile operations procedure that concentrates on enhancement of speed, precise data analysis, high data quality, improved data integration, and thus, significant data management and deployment. DataOps can also be viewed as the accurate alignment of data in accordance with the goals set for an effective management and delivery of data. Uninterrupted reception of data, supervision of performance, and accurate assignment of data are the roles to be employed by the data managers/data consumers involved in DataOps. Synchronization among developers, technologists, and data scientists for the effective leverage of large amount of data is intended from DataOps.

Notably, the prominent cloud platform company, IBM, defines DataOps as the systematic arrangement of people, process, and technology to ensure delivery of high quality and trusted data. Or in other words, DataOps can be defined as the orchestration of people, processes, and technology that share part in the victorious delivery of information to data citizens (people who are entitled to have access to a company’s proprietary data), applications, other operations involved in the data lifecycle.

Abstract model of DataOps

What problem is DataOps trying to solve?

Though the data teams coordinate well with their users in originating new proposal ideas, immediate execution of the ideas, and rapid iteration in obtaining enhanced quality models and analytics, a contrary response is observed. Data scientists use three-fourth of their time cleaning up poorly formatted data and executing manual steps. Moreover, data teams are often disrupted by data and analytics errors. The sluggish and erroneous development discourages and frustrates data team members as well as stakeholders. The amount of time passed between the presentation of a new concept and the deployment of completed analytics is referred to as “cycle time”. It has been observed that several organizations take months to deploy 20 lines of SQL. In addition to impeded creativity, prolonged cycle times discourage and disappoint users. Lengthy analytics cycle time occurs for a variety of reasons as depicted in the below figure.

Obstacles that delay analytics lifetime

DataOps governs the workflows, technical practices, norms and architectural patterns, decimating the indefinite hindrances that prevent a data organization from accomplishing low error rates, and high levels of productivity and quality.

Prospects of a DataOps Team

The global revenues with the use of artificial intelligence are expected to spring up to $22.3 billion by 2025. To make this possible, the plethora of data secured in various organizations need to be freely accessible to the data team instead of requesting, waiting, and slowing the process. The DataOps team can thus furnish all the available data to the required data users and also ensure security of the data. The presence of a DataOps unit additionally ensures the following benefits:

  • Facilitates perception of real-time data processing
  • Reduced cycle time of data applications
  • Enhanced collaboration of team members and teams within an organization
  • Heightened transparency with the use of data analytics
  • Enhanced focus on the business policies and demands of data users
  • A unified data hub, i.e., unification of localized and centralized teams (for example, centralized data engineering/data science teams, centralized production teams)
  • Ease of access to all available data, i.e., self-service by the data users marinating compliance
  • Enhanced quality projects at a faster pace and lower cost.

How to Implement DataOps Quickly?

The most important technical pillars that must be clung on to while developing a DataOps team are: CI/CD, orchestration, testing, and monitoring.

Continuous integration/Continuous delivery (CI/CD)

The CI/CD methodology employs a central repository, such as the GitHub to branch and alter codes in an efficient way without hampering the production. Once altered and tested, the changed code can be merged into the production without a havoc. Thus, effective reuse of codes can be done without duplicating the processes.


Orchestration facilitates the seamless coordination of software, codes and tools across the data pipeline through data source, data ingestion, data engineering, and data analytics. Into the bargain, it also lessens human activity, and allows management of several pipelines in production by a single data engineer.


Tests can be focused on both data and code and used to test the variable or fixed data/code before rolling it out into production. Apart from the testing of the data quality, the tests also evaluate the functionality of pipelines, thus, ensuring consistent delivery of data.


This involves the constant monitoring of pipelines in the production phase, and monitoring of tools, hence, keeping check on the storage requirements or infrastructure that needs to process the data. Additionally, the following steps should also be kept in mind while initiating DataOps in an organization to utilize data in a pliable and effective manner without disturbing the ecosystem:

  • Democratization of data: Smooth access to a large volume of data is an absolute necessity with rise in machine learning and deep learning applications.
  • Leverage of advanced data science products and tools: AWS Sage Maker and Databricks MLFlow tools should be used to manage the ML life cycle.
  • Automation: Automation of procedures in data driven projects should avoid insignificant steps, such as manual quality testing and monitoring of data pipeline, i.e., add automated monitoring and tests.
  • Cautious Governance: Preparation of data catalogues and road maps dealing with the tools, procedures, processes, and key performance indicators (KPIs) involved in data processing ensures the adherence of the data pipeline to the policies set by an organization.
  • Version Control System: The use of Version Control System looks out for every alteration made to the codes in a database.
  • Branch and Merge: This enables parallel experimentation of new features on a code by multiple members of a team without interrupting each other’s work.
  • Use Multiple Environments: Deployment in duplicate environments that replicate the real-time production environment.
  • Use of Machine Learning life cycle management tool: This clearly defines the structure and role of a data science project and the involved members, respectively.
  • Reuse and Containerize: Faster, consistent and uniform processing of coding is ensured with this feature.
  • Parameterize Your Processing: A seamless functioning of the data team members can be ensured with the use of various parameters, such as name, numbers, etc. for the workflow, testing, filtering, and production of data.
  • Timely Alerts and Checks: A keen eye should be maintained to mitigate errors and ensure smooth data processing.

Other practices while rolling out DataOps include setting of performance benchmarks, feedback loops ensuring validation of data, possession of an efficient DataOps team with an amalgamation of background and technical skills. Hence, it is a requisite for any business to form a DataOps team to assure faster and cheaper maintenance and delivery of data.

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