The quality of data matters while deriving targeted business insights. Data plays an elemental role across industries with wider applications. This is where a specialized data science expert comes in with a massive pool of skill sets. Deploying the correct efficiencies and tools in the data science industry has made realizing business goals possible. DevOps and Data Engineering work in tandem to intensify data harnessing and business amplification. Today, the global DataOps platform market size is estimated to reach USD 4 billion by 2024 (Future Market Insights). Understanding these concepts shall impact the pace at which your business dwells and bring in enough boost for multitudinous succession ahead. Let us dig deep into the concepts.

About DataOps:

DataOps is a novel independent data analytics approach that combines several tools and teams across an organization on a common platform for better organization and data development. DataOps is mostly targeted at the interconnection of data design and development. This involves a proper operational framework between data analysts, data scientists, developers, and operationalists with data transformation, and delivering prompt and insightful analytics for business good. 

Is DataOps the same as DevOps:

Sound similar, but these two are quite different. However, both DataOps and DevOps are founded on the same basic philosophy of access and collaboration between teams. The two disciplines serve completely diverse functions within an organization.

Common Principles Guiding DataOps and DevOps:


DataOps emphasizes the automation of software deployment and infrastructure changes to ensure consistent prompt delivery. DataOps instantly recognizes the need for data pipeline automation. Whereas, DevOps might automate code testing and deployment. It instantly automates data validation, transformation, and loading processes.


The core of DevOps lies in the CI/CD pipeline that ensures continuous code changes with integration, tests, and production. Similarly, DataOps adopts CI/CD but in the data realm. It ensures seamless integration, consistent refinement, and delivery to data warehouses without hurdles.


DevOps adheres to the close collaboration of developers and IT operations professionals. DataOps extends this collaboration to include data engineers, data scientists, and business stakeholders. 

DataOps in Data Customization:

  • DataOps ensures the principles of continuous integration to handle diversity, and data integration from varied sources into a cohesive dataset.
  • DataOps incorporates automated data quality checks and validation processes; ensuring that the data utilized is accurate and trustworthy.
  • Scalability and performance lend efficiency with DataOps.

How DataOps Work?

DataOps combines DevOps and agile processes to manage data to meet business goals. It improves the lead conversion rate by optimizing marketing and product recommendation lines. DataOps consists of tasks beyond just writing codes, improving, and streamlining data warehouses.



  • Provides trustworthy real-time data insights
  • Reduces cycle time of data science applications
  • Enhances team and individual interaction
  • Increased transparency
  • Established systems
  • Ensures better quality data
  • Creates a unified data hub
  • Data infrastructures are tucked under the ever-expanding data volumes
  • Continuous monitoring and feedback loops
  • Emphasizing metadata management
  • Streamlining pipelines for real-time processing 
  • End-to-end data encryption
  • Automated compliance check
  • Data management task streamlining 

How does DataOps implementation help resolve business challenges?

DataOps takes care of the inefficiencies in the data. Error-free data gives error-free analytics as DataOps tackles the collection of unnecessary data to be put under a series of data quality checks. This further assists in deployment issues that occur due to limited collaboration. DataOps helps teams across data, development, engineering, and IT operations domains to work in collaboration to attain real-time feedback and gain long-term success. These feedback loops help organizations make a few tweaks or push the same procedures forward with greater impetus.

Steps to DataOps Implementation:

  1. Setting objectives while designating key performance indicators as tangible metrics.
  2. Building the right team with cross-functional collaboration, and continuous training via specialized data science certifications.
  3. Choosing the most appropriate tools for assessment and evaluation and enhancing integration capabilities.
  4. DataOps promotion while fostering collaboration, streamlining feedback loops, and lifelong learning.


  • How can I gain expertise in DataOps?

Top data science certification programs are an easier way to get enrolled and earn the most highly-rated data science skills including DataOps, among many others.

  • What are the stages of DataOps?

Data Acquisition>> Data Preparation>> Data Processing>> Data Analysis>> Data Delivery

  • What is the difference between DataOps and Data Engineering?

Data engineering and data science go hand in hand. Data engineering is primarily concerned with designing, developing, and maintaining data infrastructure; including data pipelines, data warehouses, and data lakes. DataOps targets emphasizing the automation and optimization of the entire data management lifecycle till decision-making.

The post HOW DATAOPS PIVOT THE FUTURE OF DATA ENGINEERING? appeared first on Datafloq.

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