The coordination of people, processes, and technology to quickly deliver reliable, high-quality data to users is known as DataOps (data operations). Data operations aim to increase communication, integration, and automation of data flows between data managers and data consumers throughout an organization.
DataOps utilizes technology to automatically create, deploy, and manage data delivery with the required governance levels. It uses metadata to increase the usability and value of data in a dynamic environment.
DataOps is a paradigm shift that significantly transforms the core ideas and methods for delivering data and challenges traditional data integration methods. DataOps uses technologies to discover data and detect changes. The focus of data operations is on providing the data values rather than perfecting every underlying component involved in the process. The figure below summarizes why organizations should adopt DataOps.
Figure 1 Simple, automated DataOps features
In the modern tech world, only a few organizations can manage different aspects of data their business requires entirely by themselves. Data is the oil of the century upon which organizations built their complex logic to run their business. DataOps enables faster delivery of extensive data services and product considering an organization’s dynamic environments, requirements, and infrastructure.
DataOps modifies the dataflows rather than the application’s behaviour. Like an assembly line used to build cars, a DataOps assembly line excels in producing quality data products while increasing speed, efficiency, and cost-effectiveness. As data is transmitted through its lifecycle, DataOps makes sure the best practices and controls are in place, including:
Several phases, standards, and best practices are involved in the implementation of data operations. Four key responsible areas involve in implementation processes are:
Dataflow Governance
Data flow governance involves high-level management supervision to monitor data-related operations, establish direction and guidance, and make critical decisions about proposals, opportunities, and threats. Data flow governance includes;
Provide Information to Professionals and Stakeholders
The process includes knowledge sharing about data usage functions and processes inside the enterprise with stakeholders. Developing best practice methods and approaches is also part of the process.
Data Strategy Development
Develop a data strategy to support initiatives for digital transformation and digital analytics in the following areas:
Planning and Design
The planning and design phase involves the following process;
Figure 2 DataOps purpose and responsibilities
Data is moved through the DataOps platform using pipelines. DataOps can handle many channels within a project. These many pipelines are employed to carry out different activities, such as hourly, daily, and weekly ingestion tasks or jobs.
Data Operations support two main types of pipelines:
DataOps Batch Pipeline
The batch pipeline follows the ETL process in which data is moved from left to right. In the batch pipeline, the data is extracted, loaded, and then transformed to the required format and made available to the end user.
DataOps Event Pipeline
Real-time (or event) pipeline execution does not result in data movement, testing, or transformation. Instead, the software is developed, tested, and then deployed to a specific environment using the standard DevOps process. When implemented, this pipeline is anticipated to continue operating while waiting for external real-time events to occur that will cause it to take action. An example of an event pipeline is API Gateway.
DataOps is not DevOps for the data; instead, it brings together DevOps teams with data scientists and engineers to provide tools, processes, and skills to enable data-driven organizations. DevOps optimize the software development pipeline by improving the quality and cycle time. It allows enterprises like Microsoft and Amazon to execute millions of code releases annually. While DataOps speed up software development by managing dynamic data operations. Data Operations work together with DevOps to manage an enterprise-critical data operations pipeline.
To implement DataOps procedures, there are numerous tools and capabilities available, such as:
Power BI: Data from many sources can be combined in Power BI to provide dynamic, immersive dashboards and reports that offer valuable insights and promote business outcomes.
Azure Data Lake: Single data storage platform to secure data with threat protection encryption.
Apache NiFi: A system for data processing and data distribution.
Azure Databricks: Azure Databricks support Al’s solution for data operations.
Microsoft Purview: A data governance solution that helps to govern on-prmises and multicloud data.
Datafold: Data quality software for detecting data quality issues.
Transitioning to DataOps provides the following advantages to an organization;
Today’s businesses are incorporating machine learning into a wide range of products and services. DataOps is a strategy designed to meet all of machine learning’s operational requirements.
In their book Machine Learning Logistics, Ted Dunning and Ellen Friedman write. “The DataOps approach is not limited to machine learning,” they add. “This organization style is useful for any data-oriented work, making it easier to take advantage of the benefits of building a global data fabric.” The authors also highlight that DataOps work well with microservices architectures.
In today’s big data environment, where speed, quality, and dependability are crucial, DataOps ensures the smooth availability of high-quality data. Data Operations achieve it by reducing data defects and quick data distribution for analytics and reporting. DataOps-enabled companies are aware of the data assets they have access to and have confidence in the accuracy and reliability of the data.
Platform as a Service is a cloud development and deployment environment with the resources organizations need to deliver anything from basic cloud-based apps to complex enterprise applications.
A hybrid cloud is a type of cloud computing environment that combines public and private cloud resources. The primary benefit of this type of configuration is the ability to provide access to the services of both public and private.
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