Data pipelines, traditionally run on open-source platforms like Airflow or Prefect, are undergoing a shift in mindset. Rather than simply moving data to serve the business, there is now a focus on reliability, efficiency, and a software engineering mindset. Continuous Data Integration and Delivery is the process of releasing data into production and is simpler than Continuous Delivery in software engineering. Considerations such as data movement, frequency, and ELT are important in designing a system for Continuous Data Integration and Delivery. A single user interface for viewing data deployments is crucial, and observability and metadata gathering are fundamental for a strong data pipeline. Platforms that enable reliable, efficient, and version-controlled datasets and rock-solid data pipelines, with built-in observability capabilities, are the direction data engineering is heading.
How we think about Data Pipelines is changing
Data Pipelines are a series of tasks that help organize and update data in locations like data warehouses or data lakes. Traditionally, these pipelines have been managed by data engineers or platform teams using open-source workflow orchestration packages like Airflow or Prefect. However, there is now a shift happening in the industry towards a mindset of reliability, efficiency, and software engineering.
Continuous Data Integration and Delivery
Continuous Data Integration and Delivery is the process of reliably and efficiently releasing data into production. Unlike software engineering, where having an exact replica of code is crucial, in data engineering, as long as certain conditions are met, the data can be released into production. The process of releasing data is simple, involving copying or cloning a dataset. Additionally, data engineering involves reacting to new data as it arrives, which is not necessary in software engineering.
Additional considerations
Releasing data in production is just one aspect of data engineering. Data is not static and often needs to be moved between tools and processed before it reaches a data lake or warehouse. Using tools like Github Actions for this work is not sufficient. Designing a system capable of Continuous Data Integration and Delivery requires considering factors beyond just releasing data.
User Interface
Having a single User Interface to view data deployments is crucial. Using multiple cloud data providers’ UIs makes it difficult to aggregate metadata and effectively manage data operations (DataOps) and business and finance operations (BizFinOps). A single pane of glass for orchestration, observability, and ops would be a valuable feature.
Observability or Metadata gathering
Observability and metadata gathering are essential for a strong Data Pipeline. Placing observation within the Pipeline itself, rather than providing metadata after the fact, is important. This allows data engineers to identify issues and take action before it’s too late. An orchestration tool that executes data pipelines with access to granular metadata is a powerful solution.
Summary
The way we think about Data Pipelines is changing. There is a shift towards Continuous Data Integration and Delivery, where data teams focus on reliability, efficiency, and version control. Platforms are emerging that provide full, reliable, and efficient data management with built-in observability capabilities. It’s important to consider automation opportunities, define KPIs, select the right AI tools, and implement gradually to leverage AI effectively.
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