This is a guest article from Lewis Gavin. Gavin is a data architect who has been working with data for seven years and has also been blogging about skills within the data community for five years on a personal blog and Medium. During his computer science degree, he worked for the Airbus Helicopter team in Munich enhancing simulator software for military helicopters. He then went on to work for Capgemini where he helped the U.K. government move into the world of big data. He is currently using this experience to help transform the data landscape at easyfundraising.org.uk, an online charity cashback site, where he is helping to shape their data warehousing and reporting capability from the ground up.
Bringing data into a single location like a data lake or data warehouse is only a small part of the challenge. Data often requires some form of transformation before it is useful for downstream consumers, such as analysts and data scientists.
Data pipelines are used to manage data through each step in this lifecycle, from ingestion to utilization. Data pipelines are incredibly important, especially for data science. Models are only as good as the data they consume, and data pipelines ensure data is up to date, cleansed, and ready for use.
This article will break data pipelines down into their core components and discuss best practices for building them.
Data Pipelines in Action
Many different tools and applications are available to build and run data pipelines. However, the core architecture remains the same. The goal of a data pipeline is to ingest some data, transform and join it with other datasets as required, and then store the data in a suitable format and location for the intended consumer.
Regardless of the tools used, all data pipelines can be broken down into their core components: origin, destination, transformation, workflow, and monitoring.
Data Pipeline Components
Origin
The origin component is where the data enters the pipeline. This is the original data source: anything from a text file to a database table. It may also be a continuous stream of data.
The purpose of the origin is to obtain data from a source and, in some cases, a secondary aim is often to obtain any new or updated data generated to continuously refresh the data at the destination. This is usually achieved by establishing criteria that, when detected, will trigger pipeline updates — such as a scheduled execution time or detected changes to source systems. Automatic pipeline updates are crucial to keeping downstream systems in sync.
Destination
The destination, as its name implies, is the final endpoint for the data. This is typically a data storage location such as a data lake, data warehouse, or business intelligence (BI) tool. Data from one origin can have multiple destinations, depending on the end users of the data.
A data pipeline could ingest some data from an origin, transform and join the data with other data sources, and then store the results in the data warehouse for analysis. It might also export it to a different database used, for example, to serve a website.
Transformation
As we have discussed, data often comes from numerous different origins. Each origin will have different standards and formats for storing data, making it complex to combine these datasets in a meaningful way. For the best chance of producing accurate models, such as machine learning models, clean and standardized datasets are especially important.
The transformation stage of a data pipeline prepares incoming data for downstream consumers. It does this by standardizing, normalizing, validating, and cleaning the data. This could involve making dates and times consistent. It could also involve changing numbers and currencies to match. Additionally, you may need to eliminate or complete empty values.
Performing all these transformations up front is a boon to analysts and data scientists. The data is ready to use and the transformation happens in one place, avoiding repetition in each analysis.
Dependencies
Downstream users often transfer data to different places as required. This can be done by either using the data pipeline or managing it separately. For example, the data pipeline can take in some data, process it, and store the final result in a table. You can do this by using the data pipeline or managing it separately.
The data pipeline takes in data, processes it, and stores the final result in a table. This table can be the final destination for the data pipeline. However, an external BI tool can also import this data to perform further manipulation.
Even though it may seem unrelated, it is crucial to think about how you create and sustain these connections. Changes in the data pipeline could impact this downstream consumer of the data.
Monitoring
The final component of a data pipeline is monitoring. Monitoring plays a crucial role in every core process, particularly when there are dependencies involved. Data pipelines need monitoring tools to find problems because downstream users depend on timely, accurate data.
Checking the data pipeline's health and notifying engineers or support colleagues if it fails is important. Checking if new data has arrived from the source and looking for duplicates in the output is helpful.
Monitoring keeps data pipelines healthy and allows you to fix problems before they affect downstream data users.
Best Practices for Data Pipelines
Understanding the components is essential but is not enough by itself to build production-ready data pipelines. You should follow these best practices to ensure the successful deployment of your data pipelines.
- First, data pipelines should be predictable. It should be obvious where data comes from and how it flows through each stage of the pipeline. Dataiku generates a visual data pipeline that shows what it is doing, helping data practitioners understand the process.
- Second, your data pipeline should scale to meet any growth in data throughput. You can manually configure and update pipelines over time to align with the increase in data volume. Creating the pipeline in Dataiku and letting it scale automatically is easier. Pay attention to where you store data and how the compute engine can handle large amounts of data.
- Reliability is crucial. It should be easy to maintain a data pipeline and diagnose any issues. Dataiku provides cues on which data pipeline stages are causing bottlenecks or have failed completely. You can then drill down into the logs of each step to help diagnose and fix the issue.
- Finally, data pipelines must be testable. Continuous integration and continuous delivery (CI/CD) have been critical components of software engineering for several years, and data science pipelines should be no different.
Dataiku uses the concepts of metrics and checks to find model accuracy or count missing values in a dataset.
You can use checks on these metrics to automate further actions based on whether the check passes or fails. A failure might trigger retraining the model if its accuracy is lower than expected. Tools exist to create data compliance reports, notify about schema changes, and send alerts for pipeline failures.
Each of these features ensures that pipelines are reliable, validates future changes, and reports any issues.
Feeding Your Future With Premium Data Pipelines
Data pipelines can be complex, so tools like Dataiku provide visual aids for both building and monitoring data pipelines. They make sure your pipelines are ready for the future with automatic scaling and provide monitoring, diagnostics, and testing features.
Having quality data pipelines also serves as the foundation for moving into more advanced analytics and the cutting-edge Generative AI applications that are stirring conversation and driving value-generating opportunities across industries. Successful and reliable pipelines are critical for integrating large language models (LLMs) into business processes in a responsible way.
Clean data helps ensure that LLMs produce accurate and relevant outputs — the basis of good content generation, language translation tasks, customer relationship management, product feedback, and many, many more use cases. Bad/biased data from mismanaged data pipelines spells disaster and is a testament to the importance of investing in your data pipelines from the get-go.