Transitioning from Database Administration to DataOps Engineer

According to Gartner,” DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization”.

Becoming a database administrator (DBA) can be a great stepping stone towards becoming a data operations (DataOps) engineer, as DBAs are responsible for managing and maintaining databases, which is a core aspect of DataOps. Here are some ways in which a database administrator can help to become a DataOps engineer:

Understanding of database technologies: DBAs are experts in database technologies, including relational databases, NoSQL databases, and cloud-based databases. This knowledge can be highly beneficial for a DataOps engineer, as it provides a strong foundation for managing and working with different types of databases.

Experience with database design and modeling: DBAs are responsible for designing and modeling databases to meet business needs. This experience can be highly valuable for a DataOps engineer, as it enables them to understand the underlying data structures and develop strategies for managing and optimizing databases.

Knowledge of backup and recovery strategies: DBAs are responsible for ensuring that databases are backed up and can be recovered in case of failures. This knowledge can be highly beneficial for a DataOps engineer, as it enables them to develop disaster recovery plans and ensure that data is available and accessible at all times.

Proficiency in SQL: DBAs are proficient in SQL, which is the primary language used for querying and managing databases. This proficiency can be highly valuable for a DataOps engineer, as it enables them to write and execute complex SQL queries for data extraction and transformation.

Familiarity with data security and compliance: DBAs are responsible for ensuring that databases are secure and comply with relevant regulations and standards. This knowledge can be highly beneficial for a DataOps engineer, as it enables them to develop and implement security policies and procedures to protect sensitive data.

Database Servers Continuous Monitoring: DBA's job is to continuously monitor all the database servers. Monitoring helps identify issues early on so they can be addressed in a timely manner. Automating the monitoring systems to get regular alerts on emails/slack channels is a regular work of the DataOps Engineer.

Overall, a database administrator can provide a strong foundation for becoming a DataOps engineer by providing knowledge and experience in database technologies, design and modeling, backup and recovery strategies, SQL, and data security and compliance.

Image source: https://www.devopsschool.com/blog/career-scope-in-dataops/

The position of DBA, guarantor of the integrity and secure access to highly available data has evolved into a more global function of what we call Data Ops: the meeting of agile development methodologies and data infrastructure administration needs, in line with the “Ops” movements (Dev Ops, Net Ops, etc.) that have emerged in recent years.

There is indeed a convergence between the need to automate the maintenance of database engines and the need to rely on automation and industrialization tools such as those used by Dev Ops teams.

With the arrival of tools such as Gitlab, Ansible, and Terraform, and agile deployment methodologies such as CI/CD, this movement makes total sense and we have the necessary means to make it happen!

The mindset then evolved. Database infrastructure management is no longer done through proprietary tools but through code. Python has replaced all the specific bricks to bring a converged vision of the administration methodology.

Looking for backup scripts? These are cronjobs on Kubernertes. Do you want to install a new instance of your engine? Ansible is the place to go.

As you can see, “infrastructure as code” has become part of the DBA’s daily life, echoing the needs of the infrastructure and Dev Ops teams, thus providing a fluid way of working.

Then the question is: What is the process for moving towards a Data Ops function, and what are the tools?

What will really make you reconsider the DBA function will often be the volume. You don’t handle a few gigabytes on a relational engine, instead, several petabytes spread over several distinct technologies, geo-distributed, redundant, sharded, etc. It is at this point that we must ask ourselves the question: how can we industrialize and automate all this data so that it becomes humanly manageable and observable? And the answer is often found in the code.

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If you are a database administrator and interested in transitioning to a DataOps role, here are some skills you should learn:

• Cloud platforms: Cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform provide many tools and services for building and managing data pipelines, such as Amazon S3, AWS Glue, Azure Data Factory, and Google Cloud Dataflow.
  
  
• Monitoring and logging tools: Tools such as Nagios, Grafana, and Prometheus can be used to monitor and analyze data pipeline performance, identify bottlenecks and errors, and optimize the pipeline.
  
  
• Programming Languages: Python for code, it is very rich in modules and suited to all environments.

• Version control and collaboration tools: Version control tools such as Git can be used to manage and track changes to data pipeline code and configuration, while collaboration tools such as JIRA and Confluence can be used to manage tasks and documentation.
  
  
• Containerization and orchestration: Containerization technologies such as Docker and Kubernetes allow for easy deployment and scaling of data applications and pipelines.
  
  
• Data visualization and reporting tools: Tools such as Tableau and Power BI can be used to create visualizations and reports based on data processed in the pipeline.
  
  
• Security and compliance tools: Tools such as HashiCorp Vault and CyberArk can be used to securely store and manage credentials, while tools such as SonarQube can be used to ensure compliance with security standards and best practices.

Learning and gaining expertise in these tools and technologies can help a DBA transition into a DataOps role by providing them with the necessary skills to design, develop, deploy, and manage data pipelines and applications.