Data flows from one location to another is a vital operation in today’s data-driven business environment. Many businesses gather data for analysis. Even so, the data flow isn’t always seamless—it can be slowed down when it’s in transit from one location to another. When that happens, the dataset can become corrupt, cause latency, or create duplicates. A data pipeline can help ensure a smooth flow of information. It automates the extraction, validation, and loading of data, thereby combating latency and eliminating errors. Besides that, a data pipeline can process several data streams at one time. In this post, we’ll take a deep dive into what a data pipeline is and how you can build one for your company.
A data pipeline is a concept that enables companies to optimize data transfer while securing it at the same time. In today’s business environment, data integration is a must to increase a business’s competitive edge and improve strategic decision-making. The critical actions that occur within data pipelines are essential to achieving this feat.
During data transfer, data is converted into a more readable form, ensuring that it arrives in a state that can be analyzed and used to develop business insights. That said, the same set of data can be used by different pipelines. For instance, a message sent to customer services can lead to:
As data continues to increase at high rates, businesses are implementing data pipelines to unlock the power of their data and get the most from it.
Data pipelines have three key elements, namely:
A data pipeline allows for the automation of all these steps in an intelligible, coherent way, with the aim of ensuring data security and facilitating smooth transfer of data.
Below is an outline of the different types of data pipelines:
Batch pipelines are used to process data in batches. Batch processing is usually leveraged when a business wants to move high volumes of data at regular intervals. Batch processing jobs typically run on a fixed schedule (for instance, every 24 hours) or in some instances, once the volume of data reaches a given threshold. This process allows analysts to integrate large amounts of data and provide a reliable and fast decision model.
These data pipelines are optimized to process millions of events at scale, in real-time. Because of this, you can gather, analyze, and store large amounts of information. Streaming/real-time pipelines are useful when processing data from a streaming source, such as telemetry from connected devices or data from financial markets.
These pipelines are designed to work only with cloud-based data destinations, sources, or both. They are hosted directly in the cloud. As such they enable businesses to save money on expert resources and infrastructure.
ETL and data pipeline are usually used interchangeably. ETL, which stands for Extract, Transform, and Load, is typically used to retrieve data from a source system, transform it depending on the requirements and load it into a data warehouse, mainly for analytical purposes. So what is a data pipeline? While it can be viewed as a broad term encompassing ETL as a subset, a data pipeline is a system that’s used to move data across systems. The data may or may not undergo any transformations. It may also be processed in real-time or in batches depending on a business and data needs. This data may be loaded into several destinations such as Data Lake and AWS S3 Bucket, or it might even be used to trigger a Webhook on a different system to begin a specific business process. In a nutshell, while both are terms for processes by which data moves from point A to point B, the key differences between data pipeline and ETL include:
When you think of technological solutions that power a successful business, a data pipeline isn’t always at the top of the list. This is because, although most forward-thinking businesses now realize that data is one of their most valuable assets, they still underestimate the importance of data engineering. Yet data pipelines can enable your business to unlock the data within your organization. With a data pipeline, you will be able to extract data from its source, transform it into a readable form, and load it into your systems, where you can use it to make insightful decisions. So what do you need to build a data pipeline?
Many tools can be used for a data pipeline. The tool you use will depend on the project requirements, the data type, and objectives, among other factors. Some of the main tools needed include:
Real-time data streaming tools: These tools analyze large amounts of data when they are used or produced. As such, real-time data streaming tools extract valuable information for the organization as soon as it’s produced or stored.
Here are some of the things to consider when building a strategy for your data pipeline:
While you can build a data pipeline from the ground up, the process can be time-consuming, resource-demanding, and not to mention challenging. Fortunately, you don’t have to go through all that trouble. ANALYB provides you with everything you need to build a data pipeline. These include: