Data pipeline change management: Solving challenges with automation

Data pipelines are scaling rapidly as organizations find more use and value for the data they collect and analyze. Meanwhile, more organizations are getting in on the data game itself, and that means standing up data pipelines and embracing the culture of a data-driven company. 

If data is anything, it’s ever-changing. Managing that change in traditional data pipelines is already an inadequate, outdated process. So the increasing demands of digital experiences, data-sourced business insights, and industry advancements like artificial intelligence add complexity that slows things down, stifling progress and innovation, if not day-to-day operations, too. 

With a strategic approach to change management in the data pipeline that introduces automation to replace manual, error-prone processes, every team involved – data, database, development, DevOps, and beyond – gets maximum benefit from the data pipeline. And they get it without slowing down the speed at which data can be packaged, delivered, and used to inform the business. 

What is data pipeline change management?

Data pipeline change management is the processes, tools, and practices used to manage modifications to infrastructure, code, configurations, and datasets. It involves methodically controlling changes to ensure efficient, reliable, high-quality data flows without causing any disruptions. 

A holistic approach to data pipeline change management adopts the DevOps and CI/CD philosophies popularized and proven in application development pipelines. This includes version control, automated testing, and robust monitoring and alerting systems underpinned by a culture of collaboration and communication across the data team and between other relevant teams. 

With data pipeline version control, teams have meticulous and accurate tracking as they manage updates to data and data stores. Driving a culture of smaller, more frequent releases with versioning aligns with DevOps culture and makes it easier to facilitate rollbacks should an issue occur that requires a change’s undoing. 

Automated testing preemptively identifies errors, adopting a “shift left” approach that ensures modifications do not detrimentally affect performance. The integration of CI/CD automation with the deployment process significantly reduces manual intervention and accelerates the delivery of data pipeline enhancements. It also allows for granular visibility to enable monitoring and alerts that ensure pipeline health, security, and continuous optimization. 

Data pipeline change management is rooted in cultural alignment with DevOps philosophies and the prioritization of productive and open cross-team communication. When changes are systematically managed and everyone is adequately informed, business intelligence analysts, data scientists, DevOps teams, and DBAs remain in sync, enabling informed decision-making and continuous feedback.

In essence, change management for data pipelines is a cohesive strategy to maintain the end-to-end integrity and reliability of data throughout the organization amidst increasingly frequent changes and larger volumes of data. It elevates data pipelines to handle the agility, scalability, and efficiency they need to be the business’s most valuable asset today and for every advancement to come tomorrow. 

Evolving pipelines: Data warehouses, lakes, and lakehouses

To understand the need, challenges, and role of automation in data pipeline change management, it helps to walk back through some of the data store technologies driving evolutions in pipeline management. 

Data warehouses have existed since the ‘80s, becoming a foundational aspect of storage and analysis for data-driven businesses. They store structured data that has been cleansed and processed, ready for analysis and business intelligence applications. 

They support complex queries and provide quick insights from structured data. They emerged as a solution to the increasing amounts of data that businesses were collecting. Data teams needed to separate data stores used for analytics and processing from the transactional databases driving applications and user experiences in order to protect and improve data integrity. 

Data lakes, emerging in the early 2010s, responded to the growing variation in data that businesses were collecting, such as from social media, connected devices, and content metadata. They store vast amounts of raw data in its native format, including structured, unstructured, and semi-structured data. Data lakes support flexible data exploration, machine learning models, and big data processing, offering a more cost-effective solution for storing large volumes of data compared to traditional data warehouses.

Data lakehouses are a new approach combining the best features of data lakes and data warehouses. They are designed to store large volumes of data (like a data lake) while supporting the transactional and analytical capabilities associated with data warehouses. This enables organizations to perform high-quality data analytics and machine learning on diverse datasets without compromising on data governance, quality, and performance. Data lakehouses provide a unified platform for data management and analysis that supports both structured and semi-structured data.

The pressure of AI

AI is everywhere, increasing its presence across business and consumer experiences starting in roughly 2022 and growing like wildfire ever since. To businesses, AI promises exponential increases in efficiency, productivity, and even creativity – yet most crucially, value. AI suddenly skyrockets the value of data – lots of it. These models, powered by deep learning and complex algorithms, are adept at uncovering insights, automating tasks, and making predictions with unprecedented accuracy, with large, diverse datasets fueling them. 

This insatiable demand for data has naturally escalated the growth of data warehousing, pushing organizations to amass and manage an ever-expanding store of information. However, the growing size and complexity of these data warehouses cause significant challenges, particularly in managing the intricate web of data pipelines essential for feeding these AI models. 

The necessity to streamline and efficiently control these pipelines has never been more critical, as they are the lifelines that ensure the seamless flow of data, driving the AI models towards delivering on their promises of immense value to the business. 

So how are data-driven businesses to approach data pipeline change management, given the stakes have never been higher?

The challenges of change management

What keeps data pipeline change management from being an easy, approachable process? 

Collaboration and access control are some of the toughest challenges, requiring both cultural and technological shifts to manage. Robust mechanisms need to be implemented to facilitate seamless interaction among diverse stakeholders, including data scientists, data engineers, business analysts, and others on the database and development teams. 

And it all has to happen without upending compliance policies or running into governance problems. This dual focus on enabling cooperation and maintaining security and compliance is essential for navigating the complexities of modern data pipelines, ensuring that teams can work together efficiently without compromising on the integrity or confidentiality of data.

Evolving database schemas present a formidable challenge because they demand meticulous handling to ensure continuous, uninterrupted data flow. They also need a safeguard against possible adverse impacts on downstream applications. Schema change, when appropriately managed, allows for the seamless adaptation of database structures to meet emerging data requirements without risking data loss or corruption. 

Data pipelines also need to work well with the rest of the constantly developing digital ecosystem. Integrating change management with the broader CI/CD pipeline is a huge benefit, but it's not as simple as it sounds. When achieved, it unlocks a more streamlined, automated workflow for deploying code, data changes, and infrastructure updates, enhancing the data pipeline's agility and efficiency. 

Automation plays a pivotal role here, eliminating manual processes that are not only time-consuming but also prone to errors. By embedding change management within the CI/CD pipeline, organizations can achieve a higher degree of control and flexibility, enabling rapid adjustments to data pipelines in response to new requirements or issues.

Automating data pipeline change management with Liquibase

Liquibase’s approach to automating change management within data pipelines transforms the way organizations handle data infrastructure evolutions and cross-team collaboration. By integrating Liquibase into the data management workflow, teams can automate the tracking, versioning, and deployment of database changes, ensuring that every alteration is meticulously recorded and seamlessly synchronized across environments. 

This automation not only eliminates manual, error-prone processes but also significantly accelerates the pace at which data pipelines can be updated and optimized, keeping pace with the rapidly evolving data landscape – and supporting the latest gains in AI and other cutting-edge technologies. 

Liquibase fosters an environment of collaboration and consistency, bridging the gap between data teams and the database administrators, developers, and other individuals they work with throughout the pipeline. It enables these teams to work in unison, applying consistent change management practices that ensure a unified approach to database evolution. By enforcing policies and ensuring compliance, Liquibase maintains the delicate balance between agility and control, allowing organizations to rapidly adapt their data pipelines while adhering to governance requirements. 

Additionally, Liquibase enhances observability within the data pipeline, offering deep insights into deployment status and operations. This visibility is crucial for identifying potential issues early, facilitating timely interventions, and continuously refining the data pipeline to support optimal performance and reliability. 

‍Automated change management with Liquibase empowers organizations to navigate the complexities of modern data pipelines with confidence while prioritizing collaboration, compliance, and operational excellence.