Building a cloud analytics data warehouse 106: DevOps (DataOps) in your cloud data warehouse

106 Series: DevOps (DataOps) in your cloud data warehouse

In this concluding part of our series on cloud data warehouses, we're tackling an area that often doesn't get the attention it deserves: DataOps. If you're familiar with DevOps in software engineering, DataOps is its counterpart in the data world. While DevOps has become a staple in software development, many data analysts and engineers have yet to fully embrace DataOps.

If you've been looking for ways to optimize your data environment, this post is designed for you. We'll cover what DataOps is, why it's crucial for managing data effectively, and offer actionable steps to implement it. By the end, you'll have a clear understanding of when these steps go from being optional to essential for your data strategy.

What is DevOps?

DevOps combines software development (Dev) and IT operations (Ops) to make processes more efficient and reliable. One key feature of DevOps is CI/CD, short for Continuous Integration and Continuous Delivery. This makes it easier for developers to build, test, and deploy code, removing manual steps that can lead to errors and delays.

An added bonus of CI/CD is it helps multiple developers work on the same project without causing issues. DevOps also includes automated testing and monitoring to ensure the code works as intended and helps catch performance issues quickly.

Continuous Deployment (or Delivery) techniques also allow for the usage of Infrastructure as Code (IaC) to further automate your processes. Infrastructure as Code (IaC) has gained popularity in recent years with its ability to let you manage cloud resources as you would code. Instead of manually setting up resources on a platform like Google Cloud, you define them in code. This code is stored in a version control system like Git. This approach has several benefits:

Easy Rollbacks: If something breaks, you can easily revert to a previous version.

Resource Recovery: If a resource is accidentally deleted, you can quickly recreate it by redeploying the code.

Standard Templates: You can create templates that meet specific quality and security standards, making it easier to launch new projects.

Peer Review: Before deploying, team members can review changes to ensure everything is up to standard. For example, you could use IaC to set up a Google Cloud Run application that interacts with a Pub/Sub topic and writes to a BigQuery table. Everything, from user permissions to the application and data storage, can be managed through IaC. And because IaC is a declarative code, it can also be managed by CI/CD processes.

Why is DataOps relevant for data warehouse environments?

As data becomes increasingly vital to organizations, the importance of ensuring its quality, usability, and maintainability is also growing. That's why more organizations are adopting measures to streamline development processes around data warehouses, ensure data quality in their systems, and gain insights into the data pipelines running in their environments.

Types of DataOps Initiatives to Consider Alerting and Monitoring

Understanding the health of your data pipelines is crucial for effective data management. Alerts should not only notify but also prompt action. When setting up alerts, consider what concrete steps you or your team will take upon receiving one. If the alert doesn't lead to action, it's likely just adding noise.

While alerts can flag immediate issues like pipeline failures, monitoring provides a broader, historical view of performance metrics. This can include tracking failed jobs over time or analyzing application logs for debugging. Providers like Google Cloud offer comprehensive tools for both alerting and monitoring.

Data Quality Checks

Ensuring your pipelines run smoothly is important, but it's equally vital to verify the accuracy of the data they produce. Regular quality checks can confirm table uniqueness, identify missing data segments, and validate column value patterns.

Automating these checks can streamline the process and enhance efficiency. Regular checks can include: Uniqueness of primary keys in tables Missing data chunks (period of time, categories, etc.) Column value patterns (data type checks, regex checks)

Overall Data Integrity

Automating these checks can make the process even more efficient. Deployment Automation If resource deployment is a significant part of your workflow, automation can increase efficiency significantly. This often involves integrating a CI/CD stack within your version control system, such as GitHub or GitLab, and utilizing build tools like Terraform or Ansible for the actual deployment.

Staged Deployments

When multiple users are actively working within the same infrastructure, the concept of staged deployments becomes particularly important for maintaining a stable production environment. The idea is to create different deployment settings or 'stages,' allowing for a more controlled and error-free transition of code and data changes into the production environment. However, it's crucial that these staged setups are designed with user-friendliness in mind. If the staged environments are too complex or cumbersome to navigate, they can actually introduce new challenges. For instance, they might slow down the development process or lead to errors that are hard to trace back to their source.

The goal is to make these environments accessible and easy to use for both data analysts and data engineers. If this isn't achieved, the staged deployments could end up causing more issues than they prevent, negating their intended benefits.

Infrastructure as Code (IaC)

As previously discussed, Infrastructure as Code (IaC) offers numerous benefits, including easy rollbacks, the ability to recreate deleted resources and standardized deployment templates. These advantages are directly applicable to data management. With IaC, you can manage all systems in your data pipeline, ensure secure user rights, and even handle your complete BigQuery setup, from datasets to table schemas.

Monitoring and alerting policies can also be templated in an IaC script, ensuring uniform benefits across all applications. In scenarios where tight control and fail-safes are crucial, IaC can be particularly beneficial. However, be aware that adopting IaC can limit the flexibility end-users have to modify existing resources, presenting a trade-off that needs careful consideration. Every major cloud provider offers its own IaC solutions: GCP uses Deployment Manager AWS employs CloudFormation Azure utilizes Bicep Additionally, there are cloud-agnostic languages like Terraform that work with all three of the above-mentioned providers, and it's a tool we use extensively.

When is DataOps relevant for your data organization?

As with everything in technical development, optimizing DataOps in your data organization requires a trade-off of time and resources. DataOps often demands a more technical skill set than what's typically needed for data warehousing. Tools like Terraform, Google Cloud Build, Azure Bicep, AWS CloudFormation, Jenkins, GitHub Actions, and GitLab Runners are generally not part of a data analyst's or engineer's standard toolkit.

Acquiring these skills may not always justify the investment. In our opinion, some level of DataOps is always relevant. Knowing how well your data pipelines are performing and receiving notifications when they're not is essential. Maintaining data quality, whether through automation or periodic internal checks, is also crucial. However, if you're just starting out and not yet using data for key decisions or processes, even basic DataOps might be premature.

Questions to consider for DataOps relevance include:

To what extent would critical processes or decision-making be impacted by outdated or incorrect data? If the impact is significant, investing in DataOps becomes a compelling business case.

How large and complex is your data environment? If it involves extensive collaboration, streamlining processes through DataOps could yield significant benefits.

5 Key Takeaways

1) Trade-offs in Implementing DataOps: Adopting DataOps is an investment of both time and specialized skills. Tools like Terraform and Google Cloud Build are powerful but may require additional training for data analysts who are not familiar with them.

2) The Importance of Monitoring and Quality Checks: Even a basic level of DataOps is beneficial for most organizations. Monitoring the performance of data pipelines and maintaining data quality is essential for making informed decisions.

3) Assessing Readiness for DataOps: Before diving into DataOps, organizations should evaluate their specific needs. Critical questions include the potential impact of outdated or incorrect data on business processes and the complexity of the existing data environment.

4) Scalability Across Organizations: DataOps is not just for large enterprises; it's scalable and can benefit organizations of all sizes. Whether you're a small business or a large corporation, DataOps can improve efficiency and reduce errors in your data management processes.

5) Toolkits for DataOps: The article highlights several tools commonly used in DataOps, including cloud-specific and cloud-agnostic options. Knowing the right tools for your needs is crucial for effective implementation.

Conclusion

While complete DataOps is not for every data organization, and value should always be considered in comparison to costs, effort and added complexity, some parts of DataOps will always bring value to your data organization.

With this final post in our Cloud Data Warehouse series, we round off a series on building your Cloud Data Warehouse. Take a look at the previous articles in this series to get expert insights from transitioning to Google Analytics 4 and building your Data Warehouse to orchestrating ETL and activating data.

FAQ

What is DataOps and why is it important? DataOps is a set of practices and tools aimed at improving the quality, speed, and reliability of data analytics. It borrows from the DevOps philosophy, applying it to data pipelines and data management. DataOps is important because it helps organizations make better decisions by ensuring that data is accurate, available, and actionable.

What tools are commonly used in DataOps? Common tools in DataOps include data orchestration software like Apache Airflow, data version control systems like DVC, and automation tools like Terraform and Google Cloud Build. These tools help automate various aspects of data pipeline creation, monitoring, and deployment, making the entire process more efficient and error-free.

Is DataOps only for large organizations or can small businesses benefit too? DataOps is scalable and can benefit organizations of all sizes. For small businesses, it can help streamline data processes, making it easier to glean insights without a large dedicated team. For larger organizations, DataOps can manage complex data ecosystems, improving efficiency and reducing errors across multiple teams and systems.

How do I know if my organization is ready for DataOps? If your organization relies on data for decision-making and you're finding that data errors or inefficiencies are impacting your operations, you're likely ready for DataOps. Key indicators include the need for real-time data analytics, growing data volumes, and an increasing number of people who need data access. If you're facing challenges in these areas, implementing DataOps practices can offer significant benefits.