Data Operation, aka DataOps, is a collection of strategies, procedures, and technologies that combine an
integrated and process-oriented view of data with automation and agile software engineering approaches. The
DataOps platform increases quality, speed, and collaboration and helps foster a culture of continuous
improvement across data analytics.
With an exponentially increasing data volume and a growingly complex data infrastructure, DataOps is
becoming popular day by day. DataOps was first introduced by Lenny Liebmann in 2014 in a blog on the IBM Big
Data & Analytics Hub titled "3 reasons why DataOps is essential for big data success."
uses a series of
principles, combining the concepts of Agile, DevOps, and Lean Manufacturing, to support innovation with low
error rates across heterogeneous teams, tech, and environments.
One of the leading DataOps platforms is DataKitchen. The DataKitchen DataOps Platform enables data organizations to adhere to the below DataOps principles
- Orchestrate production pipeline
- Monitor production data for errors and trends
- Use multiple self-service environments to experiment outside of
- Add tests to catch problems quickly using automated data and logic tests
- Reuse and containerize components to save time and reduce complexity
- Parameterize code to run on multiple environments
- Schedule pipeline processing for regular and predictable deliverables
DataKitchen DataOps: Features
Streamlined Data Management
The DataKitchen DataOps Platform provides a central hub for managing all data needs. With the DataKitchen
DataOps platform, businesses can quickly bring in data from multiple sources, cleanse and transform it, and
store it securely in a centralized location. This simplifies managing and analyzing data across departments
and business units and helps ensure data consistency and accuracy.
Enhanced Data Quality
Data quality is a critical factor in any data analysis project. The DataKitchen DataOps platform thoroughly
tests input and output data so that businesses can make informed decisions and drive better outcomes. It
supports a wide range of tools and data sources, such as:
- Native Support – Integration is achieved through dedicated Data Sources and Data Sinks. Almost all
data sources, data types, ETL tools, and storage types are supported, whether traditional databases or
AWS, Azure, or GCP (Google Cloud Platform) data services.
- Container Support – Integration can be achieved using Containers – a lightweight form of
virtualization that encapsulate an app and its environment.
Improved Efficiency and Productivity
The DataKitchen DataOps automation feature automates many time-consuming and repetitive data management and
analysis tasks. This can free up valuable time for team members to focus on higher-level tasks, such as
identifying trends and insights, creating data-driven strategies, and driving business outcomes.
DevOps vs. DataOps – What is the difference?
Below is a simplified overview of the difference between DataOps and DevOps processes:
DevOps CI/CD (Continuous Integration / Continuous Development) systems, i.e., Jenkins, Bitbucket, and Azure
Pipelines, focus on the CI/CD phase of the
development pipeline - the build and delivery of code. They manage software development tools but not data
toolchains. The DevOps CI/CD strategy is generally used by software engineers for building software products
efficiently using various languages, tools, and technologies.
While the term 'DataOps' indicates that it is significantly influenced by DevOps, the conceptual background
of DataOps comprises all three approaches — Agile, DevOps, and statistical process control. The
DataOps platform enables you to adapt the DevOps CI/CD strategy to fit the demands of data science and
analytics teams. Environment management, orchestration, testing, monitoring, governance, and
integration/deployment are all automated.
Below are some DataKitchen DataOps advantages that are not achieved by DevOps tools -
- The DataKitchen DataOps platform enables the creation of Kitchen workspace sandboxes quickly to give
data engineers a regulated and safe working environment. Kitchens include pre-configured tools,
databases/datastores, and tests that provide developers all prerequisites to develop and innovate. As
new analytics are available, Kitchens effortlessly merge into aligned contexts to transform an
individual's work into a team's work and, finally, into production.
- Data analytics cannot be agile if the release methods are error-prone and time-consuming. The
DataKitchen DataOps automation feature automates the deployment process, allowing analytics teams to
test and deliver new analytics on demand. Kitchens coordinate and connect toolchain environments, making
it easier for continuous deployment orchestrations to move analytics to production.
- The DataKitchen DataOps platform enables visibility of end-to-end data journey irrespective of tools,
data, infrastructure, and organizational boundaries.
Fig 1: DataKitchen - My Project View
- It reduces the number of data errors due to active data quality checks and continuous tests.
- It helps pinpoint root cause analysis with historical event-based views.
Fig 2: DataKitchen - Data Quality
The exciting potential of the DataOps platform can help data teams regain control of their data pipelines
and provide value immediately and error-free. Whether teams use an all-in-one platform like DataKitchen
DataOps or develop a DataOps solution, the right mix of tools, procedures, and people can ensure true
DataOps success. Explore DataOps with Nous’ advanced analytics solutions experts and learn how we can
help you leverage a DataOps Platform to analyze data at lightning speed while eliminating errors.