DataOps assists in overcoming obstacles and complexities in order to deliver analytics with speed and agility while maintaining data quality. As a result, it focuses on obtaining quick insights by leveraging the interconnectedness of each chain of the analytics process by emphasising people, process, and technology.
About
DataOps Certified Professional (DOCP)
DataOps is an automated, process-oriented methodology that analytic and data teams employ to increase data analytics quality and reduce cycle time. While DataOps originated as a collection of best practises, it has evolved into a distinct and unique approach to data analytics. DataOps understands the interrelated nature of the data analytics team and information technology operations and applies to the full data lifecycle from data preparation to reporting.
DataOps uses the Agile methodology to reduce the time it takes to generate insights that are aligned with business goals.
DevOps emphasises continuous delivery by utilising on-demand IT resources and automating software testing and deployment. The combination of software development and IT operations has enhanced software engineering and deployment velocity, quality, predictability, and scale. DataOps tries to bring these same advances to data analytics by borrowing methods from DevOps.
Statistical process control (SPC) is used by DataOps to monitor and govern the data analytics pipeline. The data coming through an operational system is regularly examined and validated to be functional with SPC in place. An automated alert can be sent to the data analytics team whenever an abnormality occurs
DataOps isn't bound by any one technology, architecture, tool, language, or framework. Collaboration, orchestration, quality, security, access, and ease of use are all benefits of DataOps tools.
-
Objectives
The major goal of DataOps is to produce reliable, efficient data insights that can be used to make informed business decisions. To achieve the goal, the DataOps team must follow the organization's data governance and security rules.
What is?
What is DataOps?
DataOps, also known as Data Operations, is a group of people, processes, and solutions that work together to provide consistent, automated, and secure data management. It's a delivery method that relies on massive databases being joined and analyzed. The name "DataOps" was coined since collaboration and teamwork are the two pillars to a successful corporation. The goal of DataOps is to be a cross-functional approach to data gathering, storage, processing, quality monitoring, execution, improvement, and distribution to end users. It taps on people's abilities to work for the greater good and corporate progress. As a result, DataOps necessitates the collaboration of software operations and development teams, which is also known as DevOps. This new developing profession, which brings together engineers and data scientists, promotes the sharing of expertise and the invention of tools, processes, and organisational structures to improve management and security. DataOps' major goal is to improve the company's IT delivery results by bringing data users and suppliers closer together.
Benefits
What is the Benefits of DataOps?
- The rapid and continuous supply of environments for the development and test teams supports the full software development life cycle and boosts DevTest speed.
- Improves quality assurance by providing "production-like data" that allows testing to run test cases before clients experience issues.
- It enables businesses to migrate to the cloud safely by simplifying and expediting data migration to the cloud or other destinations.
- Both data science and machine learning are supported. The data science and artificial intelligence efforts of any organization are only as good as the information available. As a result, DataOps assures a consistent flow of data for digestion and learning.
- Assists with compliance and sets standardized data security rules and controls to ensure a smooth data flow without putting your clients at danger.
Learn
Why should we learn DataOps?
Team
Meet Our Mentors & Regents
200+ years of industry experience bringing in core strengths and industry network.
Rajesh Kumar
DevOps Princial Architect & Co-founder, Cotocus.
Augustine Joseph
CEO, Jetexe
Shubhanshu
Co-Founder at GoScale Technologies
Sandeep Aggarwal
SVP & Executive Board Member, Happiest Minds Technologies.F.A.Q
Frequently Asked Questions
-
What are the responsibilities of DataOps?
Statistical process control (SPC) is used by DataOps to monitor and govern the data analytics pipeline. The data coming through an operational system is regularly examined and validated to be functional with SPC in place. An automated alert can be sent to the data analytics team whenever an abnormality occurs.
-
What exactly is the DataOps framework?
DataOps is a methodology for automating data orchestration throughout a company by combining technology, processes, principles, and people. By accelerating the creation and deployment of automated data workflows, DataOps provides high-quality, on-demand data to corporate customers.
-
What is the purpose of DataOps?
DataOps refers to a set of strategies and technologies for operationalizing data management and integration in order to maintain robustness and agility in the face of rapid change. It aids you in extracting order and discipline from chaos, as well as addressing the major problems of turning data into corporate value.
-
What are the Tools for DataOps?
- Reflow
- Data Kitchen
- Apache Oozie
- Jenkins
- Open Data Group
- Domino
- Meltano
- Naveego
- Data Build Tool (DBT)
- Airflow
-
When it comes to DevOps and DataOps, what's the difference?
DataOps focuses on the transformation of intelligence systems and analytic models by data analysts and data engineers, whereas DevOps focuses on the transformation of development and software teams' delivery capacity.
It is a cultural phenomenon to consume prospective improvements. A culture of shorter timelines and higher quality that supports continuous improvement in measurements is essential for achieving concrete results from a DataOps strategy. Every task/step in the process should be assessed in terms of how automation and intelligence could improve it. A culture of ongoing development in terms of quality, agility, and collaboration in both data and insights could be a good step in the direction of DataOps.
There is no single element that can make an organization DataOps compliant, hence consistency in efforts is necessary. To achieve a streamlined process, the stakeholders (data engineers, analysts, scientists, or stewards) involved in each step must have a shared understanding and sense of teamwork. Constant data promotion, transformation, and release should be paired with continuous model application to generate insights and improve analytics on a frequent basis. Additionally, analysts and engineers should receive continuous feedback on the quality and profile of data and insights, which would speed up the process of correcting errors and ensuring a stable process.
Kshitiij Gupta
Ceo & Founder
Proin iaculis purus consequat sem cure digni ssim donec porttitora entum suscipit rhoncus. Accusantium quam, ultricies eget id, aliquam eget nibh et. Maecen aliquam, risus at semper.
Abhinav Gupta, Pune
Designer
The training was very useful and interactive. Rajesh helped develop the confidence of all.
Indrayani, India
Store Owner
Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.
Ravi Daur , Noida
Freelancer
Good training session about basic Devops concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.
Sumit Kulkarni
Software Engineer
Very well organized training, helped a lot to understand the DevOps concept and detailed related to various tools.Very helpful.
Vinaya
Software Engineer
Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.
