DataOps: A Simple Concise Guide For 2021

Introduction

Enterprises today are increasingly adopting Machine Learning into the wide area of products and services, and DataOps is an approach tailored towards supporting the end-to-end needs of machine learning. Companies searching for innovative ways to gather, interpret and act on data should know about emerging data management procedures called DataOps. This new development aims to improve an organization’s use of data through better data quality, shorter cycle time, excellent data management, and error-free analytics.

In this article let us look at:

1. What is DataOps?
2. DataOps Goals
3. Advantages
4. DataOps vs. DevOps
5. Principles
6. How to Build a DataOps Team
7. Roles
8. Salaries

1. What is DataOps?

DataOps or Data Operation is an organization-wide data management practice that controls the flow of data from source to value, intending to speed up the process of deriving value from data.Data Operation is as much about people as it is about tools and processes. It is a process-driven, automated approach to data delivery and analytics.

2. DataOps Goals

DataOps is an approach that assembles DevOps workers, data scientists, and data engineers to bring agility and speed to the end-to-end pipeline process, beginning with the collection and ending with delivery. It focuses on facilitating effective data operations and a reliable data pipeline; Data Operation delivers accurate, actionable information with shorter development and delivery cycles. DataOps helps to align your data management processes with your expectations for that data.

3. Advantages

Some of the benefits derived from the functioning of Data Operation are:

• Increased Efficiency: With automation and a better process-driven method, DataOps functions smoothly. The Data Operation teams can devote attention to important strategic tasks rather than be concerned about inconsistencies. 

• Cloud Agnostic Integration: As enterprises use Data Operation improvements to import data from different sources and formats, and their data ecosystems often need to integrate with various data warehouses and storage platforms. DataOps platform connects to every cloud or on-premises source and scales across new technologies over time.

• Agility: It is one of the major benefits of Data Operation in that it brings more agility to business operations, all the processes are automated, and the results are achieved quickly.

4. DataOps vs. DevOps

DevOps requires coordination among software developers and IT, while DataOps is an agile approach to designing and implementing a data architecture that supports open-source tools and frameworks in production. DataOps is a subset of DevOps, and it includes the members of an organization that deals with data: data scientists, data engineers, and data analysts. Data Operation focuses on IT operations and software development teams and only works if line-of-business stakeholders work with data engineers, data scientists, and data analysts.

5. Principles

The Data Operation team strives to provide customer satisfaction by continuous and early delivery of valuable analytic insights.

6. How to Build a DataOps Team

Quality is paramount for the smooth function of Data Operation. It is necessary to build a strong team of qualified professionals to standardize metrics and control data quality. Teams can be bifurcated by local or centralized. The local teams would be closer to the business providing analytical service, consisting of Data scientists, analysts. The central teams would work from the back end and mainly be the Engineers, DBA, etc.

7. Roles

The DataOpsengineer has a major role to play in executing the Data Operation projects, and they are experts in learning algorithms and infrastructure, qualified to reduce model training time and put best efforts, practices, and tools among data science teams to improve productivity and avoid common mistakes. This role requires top technical skills, business communication skills, excellent attention to detail, follow-up, and the ability to self-manage. The primary roles and responsibilities a Data Operation engineer has to do are as follows:

• Orchestrating the customer’s existing tools and analytic assets via Docker, APIs, or CLIs. 

• Take a holistic approach to align business, and user demands with production data analytics environments, providing the tools, infrastructure, and processes required to respond rapidly and automatically while still maintaining high-quality data. 

8. Salaries

A DataOps Engineer is offered one of the most lucrative packages by hiring organizations. Data engineers, data analysts, and data scientists are all important roles, but they are valued more under Data Operation.  The opportunity to have a high-visibility impact on the organization will make Data Operation engineering one of the most desirable and highly compensated functions. If you are looking for an opportunity for growth as a DBA, ETL Engineer, BI Analyst, or another role, look into Data Operation as the next step. The average salary for a DataOps Engineer in India is around Rs. 10 – 12 lacs per annum. A Data Scientist’s average salary is Rs. 10 lacs per year. Data Analyst gets around Rs. 7 lacs per year.

Conclusion

Companies across the world are making data management changes that support more accessibility and innovation. Many organizations we think of today, Facebook, Netflix, Amazon, and others, have already embraced approaches that fall under the Data Ops umbrella. Organizations with well-developed Data Operation strategies, governance, and processes can expedite the delivery of data-driven workflows and results faster and better than others.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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