In today’s world, data plays an important role. This data is very important for organizations and companies.
The data flow is like the traffic flow in the city, in this city, the information packets are like the cars that are moving on the city streets. What will happen if roads and transportation traffic systems are not properly designed and planned? The city becomes crowded and chaotic and the cars do not reach their destination on time. The person responsible for managing this matter is the traffic police in the city.
The data flow is the same. There should be someone who takes responsibility for planning and managing data flow. This person is none other than the DataOps engineer.
A DataOPs engineer, like a traffic policeman, designs and implements the necessary systems for data movement, ensuring that data movement is done as quickly and efficiently as possible.
In other words, a DataOps engineer is responsible for planning and managing all operations related to data flow in an organization. In this article, we introduce the job of data flow management or DataOps.
Table of Contents
What is DataOps?
DataOps (abbreviated as Data Operations) means managing the flow of data with an agile approach in an organization. In effect, DataOps acts like a data transport scheduler. This responsibility includes designing and implementing the necessary systems for moving data, reviewing and improving their performance, managing data assurance and quality, and being responsible for planning and managing all operations related to data flow in an organization.
Who is a DataOps Engineer?
A DataOps engineer is an expert in the field of data flow management. The main task of a DataOps engineer is to design, implement and manage the systems necessary to move data in an organization. Like DevOps engineers (explained fully in the next section) who specialize in software development, DataOps engineers specialize in data flow and focus on improving the development process and executing data flow operations, including implementation, testing, maintenance, and improvement. Data flow environments. The DataOps engineer, as a member of the specialized data management team, seeks to improve and optimize data-related processes in the organization.
Comparing DataOps and DevOps
As mentioned in the previous section, the performance of DataOps and DevOps (Development and Operations) are very similar. The difference is that DataOps is used in data management and DevOps is used in software production.
DevOps provides tools to facilitate software delivery. For example, tools to automate the software delivery process, software testing, software performance monitoring, etc.
DataOps likewise provides tools and methods to improve data management processes. For example, methods for managing and controlling data quality, creating automated processes for data collection, facilitating communication between data teams, etc.
What are the duties of a DataOps Engineer?
The main duties of a DataOps specialist are:
1. Design and implementation of data flow systems
This task includes designing and implementing systems such as pipelines, data processing systems, and data storage systems.
2. Data quality monitoring
The DataOps Engineer is responsible for reviewing and ensuring data quality. To do this, he must determine the data quality criteria and then review the data based on them.
3. Support and maintenance
The DataOps Engineer is responsible for supporting and maintaining data flow systems so that they are always available and working properly.
4. Improving the performance of systems
The DataOps Engineer must improve the performance of data flow systems to improve the performance of the entire organization.
5. Communication with other teams
The DataOps Engineer must communicate and collaborate with other teams working in the field of data flow to achieve the best results.
6. Create and manage documents
The DataOps Engineer must create and update the necessary documentation for the data flow systems so that everyone can refer to it when needed.
Must train other teams in the use of data flow systems so that everyone uses them correctly.
8. Data security
The DataOps Engineer must be responsible for implementing security procedures and solutions to prevent data from being stolen, destroyed, or compromised.
The difference between DataOps and Data management
DataOps and Data Management are both related to data management, but they have differences.
Data Management usually includes a set of data management activities that include collecting, storing, interpreting, cleaning, maintaining, and managing the organization’s data. The main goal of Data Management is to manage data and maintain its accuracy and correctness.
DataOps, on the other hand, is a process for developing, upgrading, testing, and providing a set of services and software that are used to analyze data and obtain information for the organization. The main purpose of DataOps is to optimize data-related processes, and hence, DataOps is more closely related to software engineering and DevOps.
Data Management focuses more on managing and maintaining data and DataOps on optimizing processes related to data.
What technical skills should a DataOps specialist have?
To succeed in DataOps, engineers must master several technical skills. Some of these skills include:
1. Mastery of programming languages
DataOps engineers must have programming skills and coding ability, especially programming in Python. Familiarity with other languages such as SQL and Shell Scripting is also vital for a DataOps engineer.
2. Mastery of monitoring tools
Knowledge and use of monitoring tools such as Prometheus and Grafana are essential for monitoring data flow and system performance metrics.
3. Mastery of cloud technologies
Proficiency in cloud computing systems such as AWS and Kubernetes is essential for managing and processing data and data flow.
4. Mastery of data analysis
DataOps engineers must have the ability to analyze data and use data monitoring and analysis tools to improve data quality.
5. Mastery of databases
Skill in designing, implementing, and managing databases, such as MySQL and Postgres, is essential for storing and retrieving data and ensuring speed and high-quality database performance.
Review the DataOps job market and income
Due to the growing prosperity of the data industry and the increase in the volume and variety of data in organizations, the need for DataOps engineers in the job market is increasing.
Today, many companies, including technology companies, banks, insurance companies, and governments, are tasked with DataOps engineers to improve data generation, management, and development processes.
Due to the specialized nature of this job, the salaries of DataOps engineers are very high. The average salary of a DataOps engineer in the US is around $150,000 to $200,000 per year. Also, this job is one of the jobs that will continue to prosper in the future. Due to the increase in the volume of data and the need to analyze them, the job demand for DataOps engineers is increasing day by day.
Many companies are creating DataOps units. European countries, the United States of America, Canada, and Asian countries including China, India, Japan, and South Korea are active in the field of DataOps and are looking to improve performance and reduce their data management costs.
Where to start to become a DataOps expert?
If you are interested in DataOps and want to enter this specialization, follow the steps below:
1. Learn Python
First, you need to learn the basic principles and concepts of programming. The most widely used programming language in DataOps is Python. So try to learn the Python programming language in the first step.
2. Familiarize yourself with data concepts
In the next step, you should get familiar with data concepts, for example, types of data models, databases, neural networks, etc.
3. Learn DataOps tools
To develop, deploy and support DataOps systems, you need to be familiar with various tools such as Docker, Kubernetes, Jenkins, Git, etc.
4. Know a little about machine learning
Machine learning is used to predict and detect errors in DataOps processes. In general, machine learning is known as one of the tools used in DataOps.