Data Pipeline Tools

TrustRadius Top Rated for 2024

Top Rated Products

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Skyvia

Skyvia is a cloud platform for no-coding data integration (both ELT and ETL), automating workflows, cloud to cloud backup, data management with SQL, CSV import/export, creating OData services, etc. The vendor says it supports all major cloud apps and databases, and requires no software…

All Products

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Control-M

Control-M from BMC is a platform for integrating, automating, and orchestrating application and data workflows in production across complex hybrid technology ecosystems. It provides deep operational capabilities, delivering speed, scale, security, and governance.

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Astera Data Pipeline Builder (Centerprise)

Astera Data Pipeline Builder is a no-code solution for designing and automating data pipelines. It allows users to read and write data across various file formats, databases, and applications. Users can execute ETL and ELT pipelines manually, schedule automated reruns, or integrate…

3
Skyvia

Skyvia is a cloud platform for no-coding data integration (both ELT and ETL), automating workflows, cloud to cloud backup, data management with SQL, CSV import/export, creating OData services, etc. The vendor says it supports all major cloud apps and databases, and requires no software…

4
Apache Airflow

Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced…

5
Fivetran

Fivetran replicates applications, databases, events and files into a high-performance data warehouse, after a five minute setup. The vendor says their standardized cloud pipelines are fully managed and zero-maintenance. The vendor says Fivetran began with a realization: For modern…

6
Integrate.io

Integrate.io’s platform allows organizations to integrate, process, and prepare data for analytics on the cloud. By providing a coding and jargon-free environment, Integrate.io’s scalable platform ensures businesses can benefit from the opportunities offered by big data without having…

7
Hevo Data

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows to save engineering time/week and drive faster reporting, analytics, and decision making. The…

8
Panoply

Panoply, from Sqream since the late 2021 acquisition, is an ETL-less, smart end-to-end data management system built for the cloud. Panoply specializes as a unified ELT and Data Warehouse platform with integrated visualization capabilities and storage optimization algorithms.

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Stitch from Talend

Stitch, or Stitch Data, now from Talend (acquired in late 2018) is an ETL tool for developers; the company was spun off from RJMetrics after that company's acquisition by Magento. Talend describes Stitch as a cloud-first, open source platform for rapidly moving data. It is available…

10
Confluent

Confluent Cloud is a cloud-native service for Apache Kafka used to connect and process data in real time with a fully managed data streaming platform. Confluent Platform is the self-managed version.

11
Mage

Mage is a tool that helps product developers use AI and their data to make predictions. Use cases might be predictions for churn prevention, product recommendations, customer lifetime value and forecasting sales.

12
Striim

Striim is an enterprise-grade platform that offers continuous real-time data ingestion, high-speed in-flight stream processing, and sub-second delivery of data to cloud and on-premises endpoints.

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Datastreamer

Datastreamer is turnkey data platform to source, unify, and enrich unstructured data with less work than building data pipelines in-house. Traditional ETL processes and pipelines might not meet the needs of organizations who want to implement unstructured and semi-structured sources…

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AWS Data Pipeline

AWS Data Pipeline is a web service used to process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. With AWS Data Pipeline, users can regularly access data where it’s stored, transform and process it at…

15
IBM InfoSphere Optim

IBM InfoSphere® Optim™ solutions manage data from requirements to retirement, to improve governance across applications, databases and platforms by managing data properly, enabling organizations to support business goals with less risk.

16
StreamSets DataOps Platform

StreamSets in San Francisco offers their DataOps Platform, a subscription based streaming analytics platform including StreamSets Data Collector data source management, Control Hub for data movement architecture management, StreamSets Data Collector Edge IoT manager, DataFlow Performance…

17
Aiven

Aiven provides managed open source data technologies on all major clouds, providing managed cloud infrastructure so that developers can focus purely on creating applications. Meanwhile, Aiven will manage the user's cloud data infrastructure.

18
TimeXtender

TimeXtender was designed to be a holistic solution for data integration that empowers organizations to build data solutions 10x faster using metadata and low-code automation.

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Keboola Connection

Keboola provides an open and extensible cloud based data integration platform that enables clients to combine, enhance and publish data for their internal analytics projects and data products. Keboola aims to help companies of all sizes: Reduce time to launch for analytics projectsEnable…

20
CData Sync

CData's Sync is a data pipeline tool able to connect data sources to the user's database or data warehouse, supporting at present over 200 possible sources, and a range of destinations (e.g. Snowflake, S3, Redshift), connecting on-premise or SaaS sources and destinations.

21
Rudderstack

Rudderstack is an open source Customer Data Platform (CDP) that provides data pipelines making it easy to collect data from every application, website and SaaS platform.

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Azure Event Hubs

Event Hubs is a managed, real-time data ingestion service that’s used to stream millions of events per second from any source to build dynamic data pipelines and respond to business challenges. Users can continue to process data during emergencies using the geo-disaster recovery…

23
Cribl Stream

Cribl Stream is a vendor-agnostic observability pipeline used to collect, reduce, enrich, normalize, and route data from any source to any destination within an existing data infrastructure. It is used to achieve full control of an organization's data stream.

24
Astro by Astronomer

For data teams looking to increase the availability of trusted data, Astronomer provides Astro, a data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Astronomer is the driving…

25
Manta

Manta offers an automated approach to visualize, optimize, and modernize how data moves through an organization through code-level lineage. Manta can scan numerous modeling, BI, ETL, and big data tools and programming languages and push the lineage into any third-party governance…

Learn More About Data Pipeline Tools

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 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 vs. ETL Tools

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.

Data Pipeline Tools Features

The most common data pipeline tool features are:

  • Customizable search parameters
  • Custom quality checkpoint parameters
  • Historical version management
  • Data masking tools
  • Data backup and replication tools
  • Batch processing tools
  • Real-time and stream processing tools
  • Data cloud, lake, and warehouse management
  • Data integration tools
  • Data extraction tools
  • Data orchestration tools
  • Data monitoring tools
  • Data analysis tools
  • Data visualization tools
  • Data modeling tools
  • Log management tools
  • Job scheduling tools
  • Multi-job processing and management
  • ETL/ELT pipeline support
  • Cloud and on-premise deployment

Data Pipeline Tools Comparison

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.

Pricing Information

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:

  • Per month: Ranges between $50 and $120 per month at the lowest subscription tiers.
  • Per minute: Ranges between 10 cents and 20 cents per minute at the lowest subscription tiers.
  • Per job: Ranges between $1.00 and $5.00 per job at the lowest subscription tier.

Enterprise pricing, free trials, and demos are available.

Related Categories

Frequently Asked Questions

What do data pipeline tools do?

Data pipeline tools transfer data between multiple sources and destinations. The pipelines can be customized to clean, convert, and organize data.

What are the benefits of using data pipeline tools?

Data pipeline tools can handle ingested data from multiple sources, even from outside of the user’s owned data environment. As such, these tools are excellent data cleaning, quality assurance, and consolidation tools.

How much do data pipeline tools cost?

Paid data pipeline tools have many pricing plans, with the most common between per month, per job, and per minute price plans. There are several free options available, usually with limited features compared to their paid counterparts. Enterprise price plans, free trials, and demos are available.