The 50 Best Data Pipeline Tools for 2023 (Complete List)

The 50 Best Data Pipeline Tools for 2023

The market is full of many data pipeline tools, each offering unique features and functionalities. Here are some popular tools worth considering:

1. Portable

Portable is an excellent data pipeline tool with over 500 connectors, offering the long-tail connectors you won't find on Fivetran.

It is a complete data integration solution that allows you to work with various data sources and long-tail destinations.
It is compatible with all kinds of enterprise requirements and use cases, including quick custom connector development, ongoing maintenance, and excellent support for API integration.
Portable offers many add-on features like monitoring, notifications, alerting mechanisms, and automation support, making it an excellent choice for businesses of any type and size.

2. Apache Airflow

Apache Airflow is an open-source platform for workflow automation and scheduling. It provides rich features for building, monitoring, and managing complex data pipelines.

3. AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL process) service provided by Amazon Web Services. It simplifies the process of creating and managing ETL workflows at scale.

4. Google Cloud Dataflow

Google Cloud Dataflow is a serverless data processing service that enables you to build and execute batch and streaming data pipelines using Apache Beam.

5. Talend

Talend is an enterprise data integration platform offering a comprehensive suite of tools for data integration, quality, and governance.

6. Informatica PowerCenter

Informatica PowerCenter is a popular enterprise data integration platform that provides end-to-end data integration and management capabilities.

7. Microsoft Azure Data Factory

Azure Data Factory is a cloud-based data integration service by Microsoft Azure. It allows you to create, schedule, and orchestrate data pipelines at scale.

8. Fivetran

Fivetran is a real-time monitoring data pipeline tool with robust ETL support.

9. Stitch

Stitch is an easy-to-use ETL tool that both data engineers and data analysts can use to pull data from multiple sources.

10. Shipyard data orchestration

This data pipeline tool helps build workflows and provides easy integration with Snowflake, Fivetran, and dbt Clout.

11. Astera Centerprise

Astera is a data integration tool with several features that help with data quality, profiling, and transformation operations.

12. Skyvia

Skyvia is a cloud-based data platform with support for ETL and ELT pipelines. It is a low-code tool that is easy to use and supports all major data sources and cloud platforms

13. Integrate.io

It is a scalable data integration platform that can easily adapt to any business working with big data.

14. Hevo Data

This is a no-code, drag-and-drop, bi-directional platform with support for ETL. ET and reverse ETL. It also has several automation features

15. Panoply

This is an ETL-less end-to-end data platform that is suitable for use in cloud applications.

16. Mage

Mage is available as a free tool to integrate AI into a data management system for gaining useful insights and predictions

17. Striim

Striim is an enterprise-level real-data tool that is helpful for operations like real-time data ingestion, data replication, high-speed stream processing, and more

18. Data streamer

This data platform helps you work with unstructured data and supports building data pipelines with unstructured or semi-structured data.

19. Aiven

This tool provides an open-source data management tech that is compatible with all major cloud platforms

20. CData Sync

This data tool can work with both on-premise and SaaS schema sources and destinations to integrate data from multiple sources.

21. Streamsets

This DataOps platform provides advanced analytical features and supporting tools for data collection and management. It is available in a subscription-based pricing model.

22. Rudderstack

It is an open-source customer data platform with data pipelines that work with websites, SaaS platforms, and other applications.

23. Apache Kafka

Apache Kafka is an open-source data pipeline tool used in social media, banking, e-commerce, and other industries for building high-performance data pipelines.

24. Keboola Connection

Keboola is a cloud-based data integration platform that provides tools for data enhancement, integration, and analytics.

25. AWS data pipeline

This is a web service from AWS that can be utilized to access data across the AWS storage and on-premise data sources

26. Cribl Stream

It is a data pipeline tool that helps users collect data from multiple sources without worrying about the destination or existing data infrastructure. It is vendor agnostic.

27. Meltano

This is an open-source and DevOps-based data lifecycle management platform.

28. ScoreData ScoreFast

This is a data tool that is specifically used for enhancing data quality. It helps embed ML algorithms to analyze data and detect fraudulent activities quickly.

29. DataOps Live

This is a SaaS platform data solution best suited to Snowflake. It provides end-to-end data orchestration, code management, CI/CD, and more, all available through an easy-to-use developer interface.

30. K2View Enterprise data pipeline

This data tool is built for analytical applications and helps integrate data from multiple sources to any kind of target destination at an enterprise scale.

31. Pandio

It is a cloud-native data solution that helps with data orchestration and modeling and has a good set of ML tools.

32. Lyftrondata

It is an enterprise-level data pipeline tool that helps build data pipelines quickly.

33. Datazip

This is a low-to-no-code data pipeline tool that anyone can use for data integration and visualizations. It provides powerful querying and visualization capabilities.

34. Mixed Analytics

This tool is an add-on for Google Sheets and can pull data from different sources like Facebook, Mailchimp, YouTube, and more with its APIs.

35. Nextflow Tower

This is a centralized command tool that allows for performing data analysis at scale. It helps build and launch data pipelines across various environments, both cloud-based and on-premises.

36. Lifebit

Lifebit is a data platform that helps integrate data from multiple research projects. It allows data science experts and researchers to run complex analytics on the collected data.

37. Latch

This is an end-to-end data platform that is used in research projects and aids in scientific discovery. Biologists use it to create custom workflows and downstream analyses.

38. BigBI software

It is a no-code data tool that helps data engineers easily build big data pipelines.

39. Crux

This external data automation platform can help integrate data from third-party data.

40. Calyptia

Calyptia is a data management tool that quickly integrates and processes data from new online sources.

41. DataKitchen dataOps

This is a data platform with powerful automation capabilities that can be used to improve the key functions of a data team.

42. Soda

Soda is a data platform that helps you improve data quality and reliability.

43. Prefect

This is an open-source data orchestration tool that provides automated workflows

44. Etleap

This is an ETL automation tool that provides no code features for data transformation and data modeling

45. DNAnexus

This tool offers a cloud-based data platform for clinical trial data management. It is especially suited to work with genomic data.

46. Gathr

This is a streaming analytics platform that can work with multi-structured data. It can be useful when working with real-time analytics and high-velocity data.

47. Hazelcast

This is a real-time data platform that helps capture data in transactional and analytical workloads in real time

48. Rivery

This is a data integration platform that helps automate the ELT process. It works well with all cloud data warehouse types such as Snowflake, BigQuery, and Azure.

49. Dagster

Dagster is an open-source data platform that helps develop efficient data pipelines in different environments, whether fully serverless or hybrid deployments.

50. Manta

Manta is a data platform that automates data visualizations and data movement across the data pipelines.

What is a Data Pipeline Tool?

Data pipeline tools are software applications that help drive raw data through the various stages of the data lifecycle. These tools can help you set up the required automation, data checks, and transformation procedures as the data moves from one stage to another.

They provide a streamlined framework that lets you extract data from the identified sources, transform it to the required data formats, and finally load it into the final target destinations, be it a centralized repository, data warehouses, or analytical applications.
Data pipeline tools also provide several other features, such as scheduling, data enrichment, and validation, to help you build a reliable and robust data management system.

Key Features of Data Pipeline Tools

When selecting a data pipeline tool, it's essential to consider the features and capabilities that align with your organization's requirements. Here are some key features to look for:

Data Extraction

The tool should support various data sources, including databases, cloud storage, APIs, and log files, enabling easy data extraction from multiple systems.

Data Transformation

The ability to transform data into the desired format is crucial. Look for tools that provide transformation functions like filtering, aggregation, joining, and data type conversion.

Data Validation

Ensure the tool offers robust data validation capabilities to identify and handle data quality issues, such as missing values, outliers, or inconsistent data.

Data Enrichment

Look for tools that allow you to enrich your data by integrating external data sources or applying machine learning algorithms for data augmentation.

Scheduling and Orchestration

The tool should offer flexible scheduling options to automate data pipelines at regular intervals or trigger them based on specific events. Additionally, it should support dependency management and job orchestration to handle complex workflows.

Monitoring and Alerting

A comprehensive monitoring dashboard and alerting mechanism are vital to track the performance and health of your data pipelines. Look for tools that provide real-time metrics, logging, and error notifications.

Integration and Extensibility

Consider the tool's integration capabilities with other systems and technologies in your data ecosystem. Look for APIs, connectors, and support for industry-standard protocols to ensure seamless integration.

How to choose the right data pipeline tool for your business

To make the selection easier, let us summarize it into a standard step-by-step guide you can follow.

Conduct your need analysis and gather requirements

Understand your business because that is the best way to know exactly what tool will work for your business. Your data tool should be in alignment with your business requirements. Research and identify your business needs, conduct a need analysis and gather the requirements into a proper documentation. Here are some basic questions to ask in this phase:

Do you need real-time or batch processing?
What is the data size you need to be processing each run
Types of data pipelines needed
The pipeline frequency for your data tasks
The data processing speed expectations
The latency requirements that denote the acceptable wait time for your data operations
Query patterns you need to support.

Evaluate tool capabilities

Now that your requirements are ready, you can use them as a base for evaluating possible data tools under consideration.
Compare the functionalities and capabilities the tool provides against your business intelligence requirements, and try to pick a tool that closely satisfies your needs.
Some of the components you will have to take a deeper look at with respect to your requirements include the scheduler, executor, event triggers, data quality checks, orchestrator, monitoring, and alerting options.

Plan the budget and contact vendors

Once you have shortlisted the possible tools you can work with, you can start looking at the budget constraints and try to work with the vendor to get the quotes for your data pipeline solutions.
Conduct your cost-to-benefit analyses for each tool and pick the tool that best matches your budget. Filter out the tools that cannot be a good fit for your company.
You could look at the budget constraints, infrastructure changes that might be needed, the hosting plans supported, the deadlines for deliverables, and so on as criteria for making this decision.

Conduct pilot tests and trials

Before you pump in your full commitment to a particular tool, trying it out on a trial basis or conducting a pilot test is a good idea.
This helps with better evaluating the product and making the necessary adjustments, if any, before you fully invest in it.

Implementing a Data Pipeline: Step-by-Step Guide

Step 1: Define data sources and destinations

As mentioned earlier, identifying your data source and target destination should be your first step to implementing a data pipeline.

Your sources determine the type of data operations and pipeline setup you need to make. They also form an important part of your overall infrastructure.

Without an idea of what your data sources entail, you cannot implement your data pipeline solutions.

Here are the basic pointers to consider during this stage of implementation:

All the potential sources of data that you can make use of
The data formats data scientists will be working with. This can be anything from flat files, JSON, XML, binary data, and more
The mechanism on how you should be connecting with these data sources.
Whether you make use of any historical data or real-time data and how to integrate them
Whether you will be using event-based data collection
Any 3rd party data sources such as social media apps or online platforms.

Step 2: Extract data from source systems

Based on the data format and nature, you must set up the appropriate extraction method. The most common techniques you will have to choose from will be batch extraction or real-time streaming

Batch extraction is carried out when you have data already available and ready to be integrated into your system.
You can set up batches where data is streamed into your pipelines at a fixed rate.
It is suitable for systems with legacy architecture and has predictable data operations.
Real-time processing involves gathering real-time data as soon as it is available.
It is much more suitable for cases where you need immediate insights or must deal with event-based real-time data collection. For instance, inventory management can be updated each time an order and sales info are updated.
With data pipelines in place, you can set up a batch data pipeline and real-time processing as required.
This can be done with the help of schedulers, event-based data pipeline executors, and more.
Data pipelines provide a simpler interface to automate data collection from various sources like databases, APIs, files, and cloud storage.

Step 3: Transform and clean the data

While data can be collected from multiple sources, it needs to be transformed into an acceptable format that can be stored meaningfully in the target data warehouse system (like Amazon Redshift, MySql, and Sql).

To make the collected data compatible, it will be moved through various data cleaning and transformation stages.

This process helps clean the data and handles the common data quality issues. There are multiple techniques and strategies used for data transformation, such as:

Bucketing/binning
Data aggregation
Data cleansing
Data deduplication
Data derivation
Data filtering
Data integration
Data joining
Format revision
Data splitting
Normalization
Min-max scaling and more

You can also apply your specific business rules and formats as required during this stage.

Step 4: Load data into the target system

The final phase of a data pipeline is the eventual data loading into the target destination system.
The destination can be an in-house data warehouse, cloud-based data lake solution, or any other kind of centralized repository.
This final phase ensures that the data from multiple sources is integrated under a single repository, allowing for efficient analysis and analytical operations on the collected data.
Data pipeline tools can help you abstract this process and provide a simple interface to connect with any target destination.
Some common data loading methods include full loading, increments loading, initial loading, and a full refresh.

Step 5: Schedule and automate data pipeline processes

Once you have set up your data pipelines, they should be set up to warrant minimal manual intervention.
Automation lets you do that and thus optimize your data operations as your operations scale up and data size increases.
Barring certain configurations, continuing with your data pipelines should be fairly easy if they are set up right the first time. And such optimized efficiency is made possible with the help of scheduling and automated workflows.
For instance, you can set up schedulers to batch-process your data weekly as a security measure. And you can also avoid manual data updates each time a change to your database is made.
Setting up an automated workflow for each sales operation can ensure that your inventory and sales data are all kept up to date with minimal manual effort. You can also use the monitoring, error handling, and alerting mechanisms provided by your data pipeline tools.

FAQs About Data Pipeline Tools

How do data pipeline tools ensure data security?

Data pipeline tools often provide features like data encryption, secure connections, and role-based access control to safeguard data during transit and at rest.

Can data pipeline tools handle real-time data streaming?

Yes, many data pipeline tools support real-time data streaming, allowing organizations to process and analyze data as it arrives.

Are data pipeline tools only suitable for large enterprises?

No, data pipeline tools are beneficial for organizations of all sizes. Regardless of the company's scale, they help streamline data workflows and improve data management.

Can data pipeline tools handle data from different sources and formats?

Yes, data pipeline tools are designed to work with diverse data sources and formats, including structured, semi-structured, and unstructured data.

Do data pipeline tools require coding skills?

While some data pipeline tools offer a code-free, visual interface for building pipelines, having basic coding skills can be advantageous for customization and advanced transformations.

How do data pipeline tools handle data lineage and auditing?

Many data pipeline tools provide features to track data lineage, allowing organizations to trace the data's origin and transformation history. Auditing capabilities help ensure compliance and data governance.

What factors should I consider when choosing a data pipeline tool?

Besides the functional features explained above, here are more features you can use to compare and evaluate data pipeline tools.

Scalability

If you start with a small data project, a simple pipeline tool that can support a few data streams would suffice your needs. But if you consider scaling it up as your business grows, you must consider options that provide easy scalability.

You should also consider the cost to scale up and down as the pricing plans vary by different degrees based on your desired scalability.

Flexibility

Look into how easy it is to adapt or integrate third-party apps, custom data connectors, and custom modules in your data pipeline tool. The needs of every organization could differ, and a general-purpose data tool may not always be the best fit for you.

You might have to look into how flexible the data pipeline tool is regarding its functionality and pricing options.

Support for data sources and formats

The first step to data management is identifying your data sources and supported formats. If your preferred data pipeline tool does not support the data sources and formats required for your data, it should no longer be of your preference.

Ease of use

Data pipeline tools need to have an easy learning curve. This helps you avoid the downtime that comes with adapting to a new tool and also helps with easy onboarding and training for your data team professionals.

Monitoring capabilities

The best way to measure the effectiveness of your data management efforts is to collect some tangible metrics on how the tasks are completed. This requires at least some basic monitoring and reporting capabilities you should look for in your tool.