Embracing DataOps: Advice from the DevOps team

While your organization is investing heavily in big data initiatives, the data journey grows rapidly:

• More data from customers, applications, IoT devices, and third-party sources
• Pipelines expand to include non-relational data stores, 3rd-party APIs, and real-time feeds
• Data is managed, consumed, and analyzed by teams of architects, engineers, analysts, and scientists (among others)

Whether big data initiatives are still at proof-of-concept phase or grown into initial development and production, companies in the early stage of this data-centric lifecycle all seem to reach a common sticking point. 

They need a better way to manage the large – and growing – volume of data and changes that face teams day in and day out. 

The manual processes that worked before can’t keep up, nor can they offer the risk protection or acceleration modern data teams need. Data-hungry organizations quickly realize the need to streamline and automate their methods for keeping data and databases updated and aligned for everyone throughout the quickly scaling data journey. 

Luckily, data teams preparing for automation and beginning to embrace DataOps can look to the DevOps team for change management practices that make life easier and internal customers happier.

As the data journey grows and your organization’s DataOps maturity develops, these six tips from the DevOps team will reduce risk, accelerate the pipeline, and enable end-to-end clarity and value. 

What can early-stage DataOps teams learn from their DevOps cohorts?

Here are the six things DevOps wants DataOps to think about:

1. Version control 
2. Governance
3. Consistency
4. Automation
5. Collaboration
6. Tracking

Let’s break these down more tactically and explain their role in the data pipeline.

1: Use version control software, like Git, for data, code, and models

At the core of efficient DevOps deployments are version-controlled packages that enable collaboration and safety without sacrificing speed in the development pipeline. In a similar sense, data teams can embrace GitOps. Just as git for databases brings structure and efficiency to the database team’s change management, it can also streamline data, code, and model evolutions. 

Version control systems enable teams to track every change made to the data pipeline, including processing scripts, schema updates, and AI/ML model adjustments. By recording each change, teams can easily identify what was altered, who made the change, and when it occurred. It’s beneficial for auditability and compliance and facilitates collaboration while enabling teams to safely and easily revert to previous versions if necessary. Version control also ensures consistency and reliability by keeping all team members on the same version of data, code, and models, and by automating testing and deployment. 

2: Establish and enforce governance for data access, security, and compliance

Who can access, handle, manage, change, or revert data and database changes throughout the pipeline? How can data streams be protected from security risks and kept in compliance with regulations and IT policies? Critical questions, no doubt. 

Establishing and enforcing governance provides a structured framework of:

• Access controls to ensure that only authorized personnel can access sensitive data
• Encryption and other security measures to protect data integrity
• Auditable logs to track access and changes

Effective data pipeline governance minimizes the risk of data breaches and ensures that data is used ethically and responsibly without sacrificing velocity and collaboration.

3: Create consistent processes and tools across the data lifecycle

Consistency across the pipeline is a hallmark of DevOps practices. It leads to smooth operations, effective collaboration, and predictable deployments. Data teams can achieve similar benefits by standardizing how data is ingested, processed, stored, and analyzed. By defining and enforcing consistent workflows, from data ingestion to final analysis and ongoing evolutions, teams can reduce errors and unexpected disruptions.

Consistent processes and tools also help teams onboard and cross-train faster, accelerating the adoption of modern methods. Additionally, it paves the way for automation, as standard frameworks are more easily integrated into tech solutions. 

4: Implement automated processes for data pipelines, testing, and deployment

Automation, you say? 

Automation is a cornerstone of DevOps, driving speed, reliability, and efficiency. DataOps teams can benefit similarly by adopting automated testing and deployment for data pipelines. This approach reduces manual intervention, minimizes human error, and ensures that processes run smoothly and consistently. Automated workflows can handle tasks like data validation, extraction, transformation, and loading (ETL), ensuring that data is processed in a timely and accurate manner for real-time analytics and decision-making.

Automated testing significantly impacts data quality by continuously validating data, checking for schema changes, and ensuring correct data transformations. This continuous testing catches issues early in the pipeline, preventing faulty data from affecting downstream processes. Following automated testing, automated deployment processes ensure that updates to data pipelines, models, and code are rolled out efficiently and reliably, with minimal downtime and risk. New features, improvements, insights, and fixes are quickly and safely delivered to end-users, enhancing the overall agility and responsiveness of data operations.

5: Improve communication & collaboration

Everyone involved in the data pipeline — from data scientists, engineers, architects, and analysts to business users and executives—needs to be able to understand and work within the processes easily, fluidly, and without causing problems for others. Establishing clear lines of communication and fostering a collaborative culture is crucial. 

Regular meetings, updates, and open channels of communication help ensure everyone is on the same page. Documentation plays a key role in this, providing a single source of truth for processes, protocols, and changes within the data pipeline. Well-documented processes make it easier for new team members to get up to speed and for existing members to reference procedures quickly.

Collaboration tools like shared repositories, project management software, and version control systems facilitate coordinated efforts and prevent silos. These tools allow team members to collaborate on code, share data insights, and track project progress in real time. Encouraging a culture of openness and continuous feedback helps identify issues early and fosters a sense of shared responsibility.

By improving communication and collaboration, DataOps teams can work more efficiently, reduce misunderstandings and ensure that all stakeholders are aligned with the goals and processes.

6: Set up systems to track data quality, pipeline performance, and model drift

Just as DevOps relies on continuous monitoring to maintain system performance, DataOps teams need robust tracking mechanisms to ensure their data processes remain reliable and effective.

By implementing tools to continuously monitor data quality, data teams can constantly be aware of issues such as missing values, inconsistencies, and outliers. By defining data quality metrics and setting thresholds, teams can receive alerts when data deviates from expected norms. 

The process quality itself is important, too, hence the need to optimize workflows by tracking metrics such as data processing times, throughput, and error rates to help identify bottlenecks and inefficiencies. Performance monitoring tools provide real-time insights into the status of data pipelines, allowing teams to quickly diagnose and address issues. This proactive approach ensures that data pipelines run smoothly and efficiently, supporting timely data delivery and analysis.

For teams leveraging machine learning, tracking model drift is also advised. Over time, models can become less accurate as the underlying data evolves. Implementing systems to monitor model performance and detect drift ensures that models remain effective and relevant. Regularly retraining models with updated data and evaluating their performance against baseline metrics can mitigate the impact of model drift.

By setting up comprehensive tracking systems for data quality, pipeline performance, and model drift, DataOps teams can maintain high standards of data integrity, optimize their processes, and ensure that their models continue to deliver accurate insights.

Shifting towards DataOps

It’s becoming clear that data teams can start preparing for and streamlining this larger, more valuable, and potentially riskier pipeline, no matter their DataOps maturity level. Learning from the foundations of database DevOps can help data teams unlock the speed, efficiency, safety, and quality they need to scale with confidence. 

Ready to get tailored guidance to help plan and optimize your DataOps journey? Get in touch.