From Data Pipeline Automation to Adaptive Data Pipelines

Data pipeline automation plays a central role in integrating and delivering data across systems. The architecture is excellent at handling repetitive, structured tasks, such as extracting, transforming, and loading data in a steady, predictable environment, because the pipelines are built around fixed rules and predefined processes. So, they will continue to work if you maintain the status quo, i.e., as long as your data follows a consistent structure.

Recently, however, businesses have been grappling with complex, dynamic demands that traditional data pipeline automation is not suited to. This is because such an architecture is typically designed with static mappings between source and target systems, which means that the pipelines can’t automatically adjust to evolving source data structures.

And so, these limitations make way for transitioning from data pipeline automation to self-adjusting data pipelines, also known as smart (intelligent) or adaptive data pipelines.

What are self-adjusting (adaptive) data pipelines?

Self-adjusting or adaptive data pipelines automatically adapt to metadata changes in your data sources. Because most of these changes are related to the metadata, they are also called metadata-driven data pipelines. The changes in metadata can include the addition of new fields, altered data types, or any other changes in the database table.

The goal with adaptive pipelines is to further reduce time-to-insights by ensuring that data continues to move, even when source data changes abruptly.

A refresher on data pipeline automation

Data pipeline automation is the process of automating data movement between systems or applications. For an ETL pipeline, it means automating the extraction, transformation, and loading (ETL) processes so that they run without significant manual intervention. The entire process is triggered automatically, for example, when new data arrives or via scheduling (e.g., every five minutes).

In the case of data integration, data pipeline automation enables you to connect to all your data sources and ingest data automatically, whether on-premises or on the cloud, which you can then use for downstream processes, such as data warehousing for long-term analysis and reporting.

This is as far as you can get with these pipelines, mainly due to their rigid nature. In other words, these pipelines are not adapted to handling changing data environments, for example, changes in source metadata. Let’s take a closer look at how these changes can affect your data pipelines.

The impact of schema evolution on data pipelines

Modifying the structure of a database or data source over time is what we refer to as schema evolution, and it directly impacts the metadata of the data source.

To understand what changing source metadata means for data pipelines, and because the primary goal of a data pipeline is to move data, let’s briefly touch upon data in motion, also called data in transit. It refers to information or data assets moving from point A to B. In terms of data integration, this implies the movement of data from multiple sources, such as a database, to a destination, which could be your data warehouse optimized for business intelligence (BI) and analytics.

An example could be when you need to migrate data from an on-premises setup to a cloud-based infrastructure. Note that ETL is just one of many methods to transport your data. Other common ways include change data capture (CDC) and extract, load, transform (ELT).

The key, however, is to move data in a way that it reaches the target system in the required format. For this to happen and to be able to derive value from it timely, your data must travel through the pipeline unhindered and unaffected. However, data sources rarely remain constant—even minor schema changes can result in data errors or dropped records. So, your data pipeline must be aware of such changes in the source metadata and be able to adjust accordingly for successful data delivery.

The dynamic nature of source metadata

Schema evolution can happen for many reasons, for example, when you add new features to an application, optimize database performance, or integrate new data sources. Although it provides flexibility for your organization’s evolving data needs, it causes significant challenges for data pipelines that rely on a stable schema. Even when we factor traditional automation into the equation, such data pipelines cannot automatically adjust their mappings and transformations to changing metadata without manual intervention.

Additionally, with the incorporation of artificial intelligence (AI) into organizational processes, data sources are evolving faster than ever. In terms of metadata, these changes include modifications in schema that may be as simple as the addition of a new column and adjusting field lengths or as complex as changing data types and table relationships.

As noted in a research paper presented at UNECE, such changes pose risks that you must address promptly to ensure your data remains fit for purpose, whether it’s data analytics or using it for projects like training a machine learning (ML) model.

Dealing with changes in source metadata

One way to deal with changing source metadata is to rework your ETL pipelines by altering the code and incorporating schema modifications. While flexible, the approach is laborious and prone to human error. Another more viable method is to leverage no-code ETL tools or data integration platforms designed for data pipeline automation. While you won’t need to code your way out of this problem, you’ll still need to modify hundreds of dataflows every time your source metadata changes, even more, if you have a complex data pipeline architecture.

This is why businesses look toward adaptive data pipelines. These pipelines are based on the metadata-driven approach to data movement, which promises to deliver data that is ready for consumption. The approach propels the data pipeline automation architecture to the next level by eliminating the need to update your dataflows to account for any schema modifications in your source metadata.

The benefits of adaptive data pipelines

Businesses have much to gain by replacing their rigid data pipeline architecture with a more adaptive and resilient one. Businesses rely on adaptive data pipelines to:

Improve agility

With AI being one of the primary drivers of mergers and acquisitions in coming years, businesses looking to acquire or merge with other businesses need a reliable pipeline architecture that can seamlessly integrate new data without disrupting operations.

Integrate new data sources

The addition of new data sources becomes a simple task of connecting them to your existing pipelines without making any changes. With modern data pipeline tools, you can achieve this by adding a new data source to your dataflow and setting up the source connection without disrupting the rest of the workflow.

Boost productivity

With your data teams no longer spending time manually debugging the pipelines, they dedicate more time to tasks of higher importance, such as collaborating with business stakeholders in solving novel data problems.

Scale on-demand

The growing reliance on generative AI and large language models (LLMs) is forcing businesses to re-evaluate their data pipelines as the massive amount of data these technologies produce is overwhelming their existing systems. When faced with sudden spikes in data volume, adaptive pipelines can quickly scale to accommodate the increased load and keep running, ensuring timely access to the data needed.

Democratize data integration

With a self-service data pipeline architecture, business functions like finance and marketing no longer need to rely on IT for access to the most up-to-date data. Instead, their metadata-driven data pipelines do all the heavy lifting for them, enabling them to focus on business-critical initiatives like analyzing data to reduce costs and improve customer experience.

Getting started with self-adjusting data pipelines

So, what does the adaptive data pipeline starter pack look like? First and foremost, you need an architecture that empowers all your teams to take control of their own data initiatives. This means adopting a no-code, user-friendly interface that allows users of varying technical skill levels to set up, manage, and interact with data pipelines effectively, whether they are data engineers, analysts, or business users.

Beyond the interface, your data pipelines must be able to detect and adapt to schema modifications as they happen without having to rework any part of the dataflow.

Such features can easily be found in modern data pipeline tools. The key, however, is adaptability—finding the right platform that adapts to your specific business needs. Remember, the goal is the democratization of data management, so in addition to alignment with the business objectives, the focus should also be on flexibility and ease of use.

How Astera sets you up for success with adaptive data pipelines

AI is changing how businesses use data to unlock insights and drive innovation. That’s why Astera is integrating AI into all its solutions so that anyone in your organization can design and deploy AI pipelines without disrupting existing processes.

With Astera, you can:

• Use AI-powered intelligent document processing to extract the data you need from continuously changing layouts
• Leverage built-in AI features, like Semantic Data Mapping, to accelerate the process of building adaptive and scalable data pipelines
• Create, test, and deploy your own AI projects within an intuitive drag-and-drop interface
• Use latest technologies like natural language queries (NLQ) and retrieval-augmented generation (RAG) to strike a conversation with your data and get the insights you need

And much more, all without writing any code. Ready to design your own AI pipelines? Try Astera Intelligence today.