Understanding DataOps: A Guided Tour

DataOps is an emerging discipline that aims to ensure seamless collaboration between data engineers, data scientists, and all other stakeholders involved in the data lifecycle. It marries the principles of DevOps with data management practices, thereby enabling teams to rapidly and accurately work with large volumes of data while ensuring reliability and security.

In this guide, we'll take you through the basics of DataOps, its benefits and challenges, and best practices for implementing it in your organization. Additionally, we'll explore the tools commonly used in DataOps and provide a step-by-step guide to building a DataOps pipeline.

What is DataOps?

DataOps is a methodology that fosters close collaboration between the various teams involved in data management. This includes data engineers, data scientists, business analysts, product managers, and executive stakeholders.

The goal of DataOps is to create a well-oiled machine that operates data pipelines with agility and speed. It centers on creating cross-functional teams that are responsible for end-to-end data management, from data ingestion to data modeling to deployment. The teams are combined up of experts representing diverse domains, such as the software, database, and network industries.

A DataOps pipeline additionally incorporates agile and lean methodologies, automation, and continuous feedback loops to deliver high-quality data with excellent speed and minimal operational risks. Implementing DataOps can help organizations become more agile, data-driven, and make more informed decisions based on accurate information.

Benefits of DataOps

• Increased agility: DataOps enables teams to quickly iterate on the data and put it to use to achieve business objectives.
• Cost-effective: It reduces development costs and eliminates duplication of efforts by developing reusable components, performing effective testing and monitoring, and continuous feedback.
• Better data quality: DataOps pipelines use automated testing and monitoring to identify data quality issues earlier, allowing for data cleansing and normalization before deployment.
• Reduced risks: DataOps provides organizations with more accurate data and safer data by streamlining the process and developing a predictable and secure data platform.
• Collaborative: By fostering cross-functional teams, DataOps allows for better communication and fosters a culture of agreement that drives productivity and excellence.

### Challenges of DataOps

• Organizational resistance: The biggest challenge facing organizations that embrace DataOps is resistance to change, particularly from teams that are siloed and work independently. Bridging the gaps between these teams and processes using technology and communication is essential to achieving success.
• Complex data landscapes: DataOps requires skillsets and tools to manage efficient, effective, and accurate data delivery for the long haul. Often, organizations have complex data that requires multiple tools, technology stacks, and expertise to manage.
• Legacy Systems: Many legacy systems are not equipped with the means to generate the data needed for DataOps. As such, there may be a substantial technology and staffing cost to move to DataOps.

### DataOps Architecture

The architecture of a DataOps pipeline must ensure that data can flow seamlessly through multiple stages of the pipeline. The data must be stored in a format that facilitates rapid deployment and is easy to manage, automate and monitor. A typical DataOps architecture consists of four phases:

• Data ingestion: This stage involves the gathering of information that may be from various sources such as streaming data, social media, or IoT devices. Data ingestion also includes the transformation and normalization of data so that it's usable.
• Data processing: In this phase, the data moves through a system of tools and flows. Data processing aims to build and maintain consistency and quality throughout the data pipeline, which includes streaming, batch processing, and near-real-time processing.
• Data storage: This phase includes the storage of the data in cloud-based data warehouses or Data Lake platforms to keep data safe and secure for analysis, processing, and storage.
• Data analysis: This phase includes advanced data analysis tools and frameworks that enable scientists and analysts to make data-driven decisions.

Key Technologies Used in DataOps

Below are some of the key technologies typically used in a DataOps pipeline:

• Pipeline Orchestration: Airflow, Luigi, Nifi, Azkaban, and Oozie are popular open-source pipeline orchestration tools that automate and manage data pipelines.
• Containerization: Docker is a popular containerization tool that offers easy provisioning, scaling and deployment of data pipelines, improving portability and reducing infrastructure costs.
• Cloud Computing: Cloud platforms such as AWS, GCP, and Azure enable the smooth integration of data processing, pipeline orchestration, and data storage, while also offering automatic scalability, fault-tolerance, and minimal cost.
• Data Governance: Tools such as Collibra, IBM Information Governance Catalog, and Informatica Axon help implement governance policies and standards for your data.
• Collaboration: GitHub, Bitbucket, and GitLab help track code changes, offer version control and team collaboration opportunities.

DataOps Best Practices

To ensure that DataOps works seamlessly with your teams, here are some best practices you can follow:

• Implement agile and lean methodologies to achieve project management goals and offer quick delivery of new features.
• Use modular approaches when building your data pipelines. This ensures that you can quickly modify individual components without having to replace the entire system.
• Take a phased approach to implementing DataOps, start with pilot projects to learn, measure and refine your vision.
• Automate all non-value-added tasks, such as testing and monitoring, so you can focus on value-added activities such as data modeling, analyzing data and team collaboration.
• Implement continuous feedback loops between stakeholders and teams to provide insights, diagnose issues, and help optimize data pipelines.

Building a DataOps Pipeline

Building a DataOps pipeline requires an understanding of the end-to-end data lifecycle and the skills necessary to leverage DataOps technologies effectively. Below is a high-level example of an Airflow-based DataOps pipeline.

Conclusion

DataOps offers substantial benefits to organizations that adopt its practices, including greater agility, cost-efficiency, and better-data quality. To implement DataOps, organizations must adopt agile and lean processes, build cross-functional teams, and foster a culture of collaboration.

The building of a successful DataOps pipeline starts with identifying the right technologies, creating a robust architecture, and following best practices to ensure rapid delivery of high-quality data. By following these guidelines, your data engineering team can operate at optimal efficiency, enabling your organization to make better decisions based on accurate data.

Category: DataOps