What are Data Pipeline Tools? Data pipeline tools help create and manage pipelines (also called “data connectors”) that collect, process, and deliver data from a source to its destination using predefined, step-by-step schemas. Data pipeline tools can automatically filter and categorize data from lakes, warehouses, batches, streaming services, and other sources so that all information is easy to find and manage. Products in this category can be used to move data across many pipelines and ...
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Data pipeline tools help create and manage pipelines (also called “data connectors”) that collect, process, and deliver data from a source to its destination using predefined, step-by-step schemas. Data pipeline tools can automatically filter and categorize data from lakes, warehouses, batches, streaming services, and other sources so that all information is easy to find and manage. Products in this category can be used to move data across many pipelines and between multiple sources and destinations.
Data pipeline tools can be helpful because they can automate movement between multiple sources and destinations according to user design. They can also clean and convert data, as data can be transformed during the pipeline process. Data pipeline tools are commonly used to transfer data from multiple entities and enterprises, making these products efficient for data consolidation. Finally, combining data ingestion through multiple pipelines allows for better visibility, as data from multiple sources can be processed and analyzed along the same pipeline.
Data pipeline tools are sometimes discussed interchangeably with extract, transform, and load (ETL) tools. While they do share many functionalities and features, ETL tools are much more restricted in their utility than data pipeline tools. For example, data pipeline tools can optionally transform data if certain schema parameters are met, but ETL processes always transform data in their pipelines. ETL pipelines generally stop once the data is loaded to a data warehouse, while data pipeline tools can define further destinations for data.
ETL tools can be thought of as a subset of data pipeline tools. ETL pipelines are useful for specific tasks connecting a single source of data to a single destination. Data pipeline tools may be the better choice for businesses that manage a large number of data sources or destinations.
The most common data pipeline tool features are:
When choosing the best data pipeline tool for you, consider the following:
In-house vs. Cloud-based pipelines: Data pipeline tools can be deployed on-premises, through the cloud, or as a hybrid of the two. The option that is best for you will depend on your business needs, as well the experience of your data scientists. In-house pipelines are highly customizable, but they must be tested, managed, and updated by the user. This becomes increasingly complex as more data sources are incorporated into the pipeline. In contrast, cloud-based pipeline vendors handle updating and troubleshooting but tend to be less flexible than in-house pipelines.
Batch vs. Real-time processing: The best data pipeline tool for you may depend on whether you are more likely to process batch or real-time data. Batch data is processed in large volumes at once (i.e. historical data), while real-time processing continuously handles data as soon as it’s ingested (i.e. data from streams). More often than not, your tools will need to delegate processing power to handle only one of these sources at the expense of the other. Choosing a product that makes it easier to separate these processes, or finding a vendor that can help you create pipelines that handle both batch and real-time, will be essential to find a cost-efficient and effective solution.
Elasticity: Traffic spikes, multiple job processing, or unexpected events increase the amount of data being processed, and thus the performance of your pipelines. As data ingestion fluctuates, pipelines need to be able to keep up with the demand so that latency is not disrupted. This is especially true if your company handles sensitive information, as increased latency can reduce your ability to detect and respond to fraudulent transactions with this data. (This aspect is also referred to as “scalability” as a data pipeline feature.)
Automation features. Pipeline tools generally operate without user intervention, but the depth or type of automation features available will be a key factor in choosing the best product for you. This is especially true if you are moving data flows over long periods of time, or if you are pulling in data from outside of your own data environment. The most reported necessary features can include automated data conversion, metadata management, real-time data updating, and version history tracking.
There are several free data pipeline tools, although they are limited in their features and must be installed and managed by the user.
There are several common pricing plans available. Pricing levels can vary based on features offered, number of jobs processed, amount of time software is used, or number of users, although other variations may occur depending on the product. The most common plans available are:
Enterprise pricing, free trials, and demos are available.