A data pipeline is a software that allows data to flow efficiently from one location to another through a data analysis process. The steps in a data pipeline usually include extraction, transformation, combination, validation, visualization, and other such data analysis processes. Without a data pipeline, these processes require a lot of manual steps that are incredibly time consuming and tedious and leave room for human error.
The best analogy for understanding a data pipeline is a conveyor belt that takes data efficiently and accurately through each step of the process. For example, data pipelines help data flow efficiently from a SaaS application to a data warehouse, and so on.
Why is a data pipeline important?
This efficient flow is one of the most crucial operations in a data-driven enterprise, since there is so much room for error between steps. Data can hit bottlenecks, become corrupted, or generate duplicates and other errors. The bigger the dataset and the more sources involved, the more likely it is errors that will occur, and the errors will be bigger and more harmful overall.
A data pipeline begins by determining what, where, and how the data is collected. It automates the processes of extracting, transforming, combining, validating, further analyzing data, and data visualization. Data pipelines provide end-to-end efficiency by eradicating errors and avoiding bottlenecks and latency. A data pipeline can even process multiple streams of data at a time. These characteristics make data pipelines absolutely necessary for enterprise data analysis.
Since data pipelines view all data as streaming data, they allow for flexible schemas. Whether data comes from static sources or real-time sources, a data pipeline can divide data streams into smaller pieces that it can process in parallel, which allows for more computing power.
The ultimate destination for the data in a pipeline doesn’t have to be a data warehouse. Pipelines can send data to other applications as well, like maybe a visualization tool like Tableau or to Salesforce.
What is a data pipeline used for?
A data pipeline can be used to automate any data analysis process that a company uses, including more simple data analyses and more complicated machine learning systems. It may automate the flow of user behavior or sales data into Salesforce or a visualization that can offer insights into behavior and sales trends. Those insights can be extremely useful in marketing and product strategies.
For example, a data pipeline could begin with users leaving a product review on the business’s website. That data then goes into a live report that counts reviews, a sentiment analysis report, and a chart of where customers who left reviews are on a map. Those are all separate directions in a pipeline, but all would be automatic and in real-time, thanks to data pipelines.
Data pipeline architecture
Data pipeline architecture refers to the design of the structure of the pipeline. There are several different ways that data pipelines can be architected. The following are three examples of data pipeline architectures from most to least basic.
Batch-based data pipeline
This is the simplest type of data pipeline architecture. It has a few simple steps that the data goes through to reach one final destination.
Streaming data pipeline
This type of data pipeline architecture processes data as it is generated, and can feed outputs to multiple applications at once. This is a more powerful and versatile type of pipeline.
Lambda data pipeline
This is the most complicated type of pipeline out of the three. It combines the other two architectures into one, allowing for both real-time streaming and batch analysis. This data pipeline architecture stores data in raw form so that new analyses and functions can be run with the data to correct mistakes or create new destinations and queries.
Enterprise data pipeline options
If your company needs a data pipeline, you’re probably wondering how to get started. There are two options here, which are essentially build or buy.
In order to build a data pipeline in-house, you would need to hire a team to build and maintain it. Building a data pipeline involves developing a way to detect incoming data, automating the connecting and transforming of data from each source to match the format of its destination, and automating the moving of the data into the data warehouse.
Then, maintaining the data pipeline you built is another story. Your team needs to be ready to add and delete fields and alter the schema as requirements change in order to constantly maintain and improve the data pipeline. This process is costly in both resources and time.
A simpler, more cost-effective way to provide your company with an efficient and effective data pipeline is to purchase one as a service. Algorithmia is a machine learning data pipeline architecture that can either be used as a managed service or as an internally-managed system. Since Algorithmia’s data pipelines already exist, it doesn’t make much sense to start building one from scratch.