10 Best Data Pipeline Tools 2024 (Reviewed by Data Experts)

July 19, 2024

Your organization must manage large data volumes daily. Valuable insights that could improve your business growth remain hidden within this operational data. To uncover those insights is like searching for a single grain of sand on a beach. That is where data pipeline tools come in. 

Data pipeline tools allow you to focus only on gaining valuable insights and making smart decisions effectively for your business. 

However, with numerous data pipeline tools available, choosing the right one can take time and effort. This article has reviewed the ten best data pipeline tools in 2024 for efficient business workflows. Explore their key features and pricing plans to find the best fit for your business requirements.  

Ready to turn your data into valuable insights? Let’s get started!

What is a Data Pipeline?

Data Pipeline

The data pipeline is a process specifically designed to ingest data from different sources, transform it, and load it to a destination. The destination can be a data warehouse, backend database, analytics platform, or business application. 

The goal of a data pipeline is to execute data collection, preprocessing, transformation, and loading tasks in a structured manner. This ensures timely and consistent data arrives at its destination ready for further analysis, decision-making, reporting, and other business needs.  

Why Does Your Organization Need a Data Pipeline Tool?

Here are a few reasons to incorporate a data pipeline tool into your organization. 

Maximize Efficiency

The data pipeline tool helps you automate manual data tasks to allocate resources toward strategic initiatives. Instead of repetitive data export and import, the tool allows your organizations to focus on valuable insights that drive business growth.  

High Data Quality and Consistency

A data pipeline tool effectively enables you to cleanse, standardize, and organize your data during data transfer. This results in higher-quality data for decision-making. Additionally, this approach also promotes data consistency by consolidating all your organization’s data into a centralized location.

High Scalability

As your business grows and data volumes increase, a data pipeline tool offers the flexibility to scale automatically without compromising performance. This ensures your data infrastructure can keep pace with your organization’s growth. 

10 Best Data Pipeline Tools To Streamline Your Data Integration

Let’s look into each of the top 10 data pipeline tools for 2024 in more detail.

1. Airbyte - Top Pick for Data Movement

Airbyte is a robust data pipeline tool designed for seamless data integration and replication. Its user-friendly no-code/low-code interface empowers you to streamline data movement between various sources and destinations.

With a vast catalog of 350+ pre-built connectors, you can easily build data pipelines. This helps you focus on extracting meaningful insights from your data, avoiding the complexities of traditional coding requirements.


Key Features of Airbyte

  • Customized Connectors: If you can’t find the connector you need from the existing catalog, Airbyte lets you create a custom connector using the Connector Development Kit (CDK). This allows you to integrate even custom applications, ensuring seamless data flow.
  • Change Data Capture: With Airbyte’s Change Data Capture (CDC) approach, you can easily capture and synchronize data changes from the source, keeping the destination system updated with the latest modifications. 
  • Python-friendly Interface: In addition to UI, Airbyte also provides a PyAirbyte, a Python library. This caters to users comfortable with Python programming, allowing them to programmatically interact with Airbyte connectors and build data pipelines within their Python workflows.

Why Choose Airbyte?

  • Open-Source: Airbyte’s open-source nature allows you to extend its capabilities and manage your data integration process according to your business needs.
  • Integration with Data Stack: Airbyte offers the flexibility to integrate with data stacks such as dbt, Airflow, Dagster, Kubernetes, or Prefect to handle your data processing requirements efficiently.
  • Fully Secured: To protect your data from unauthorized access, Airbyte provides multiple security mechanisms. These include credential management, audit logging, access control, encryption in transit, and regular security assessments.
  • Extensive Community Support: Airbyte leverages the support of a vibrant community of 15000+ members who are actively involved in maintaining its open-source platform. You can collaborate with others to discuss integration practices, resolve data ingestion issues, or share articles and resources.

Airbyte Pricing Plan

Airbyte offers flexible and transparent pricing plans:

Cloud: This plan is a fully managed service designed for businesses seeking a seamless data synchronization experience. Cloud version eliminates the need for complex setups or ongoing maintenance. It also offers predictable and scalable pricing, ensuring you only pay for the services you use.

Enterprise: The Enterprise plan is suitable for organizations that require more security and control over their data pipeline processes. It has a custom pricing and is available for multiple users and workspaces.

2. Stitch Data - A Simple and Scalable Solution

Stitch Data is a fully managed data integration platform with a no-code interface. It lets you swiftly move data from its 140+ data sources into a cloud-based data lake or warehouse. This simplifies the process of consolidating data from various sources, empowering you to drive valuable decisions.

Stitch Data

Key Features of Stitch Data

  • Automatic Scaling: Stitch Data’s high availability infrastructure enables you to manage billions of records daily. With automatic scaling, you can adjust to rapidly growing data volumes without concern about hardware provisioning or workload management. 
  • Pipeline Scheduling: It allows you to schedule your data pipeline to run at specific intervals or based on triggers. This will ensure timely access to the most relevant data whenever required.

Stitch Data Pricing Plan

Standard Plan: Basic features are limited to 5 users with 7-day historical sync and extraction log retention. You can extract data from 10 sources and load between 5 to 300 million rows to only one destination monthly.

Advanced Plan: You can export data from unlimited sources and sync 100 million rows to any of the three destinations monthly. This plan has all the standard features, 60-day extraction log retention, advanced scheduling, and post-load webhooks.  

Premium Plan: In addition to the above features, this plan includes advanced connectivity, such as Site-to-site VPN, AWS private link, reverse SSH tunnel, and VPC peering. You can export data from unlimited enterprise sources and sync 1 billion rows/per month to any of the five destinations.

3. Fivetran - An Automated Data Movement Platform

Fivetran is a data pipeline tool designed to automate ELT processes with a low-code interface. With its 500+ pre-built connectors, you can migrate data from SaaS applications, databases, ERPs, and files to data warehouses or data lakes. You can also create custom connectors with its function connector feature as an extension to Fivetran.


Key Features of Fivetran

  • Auto Schema Mapping: When you add a new column or modify an existing one in your source data schema, Fivetran automatically recognizes and replicates them to the destination schema. 
  • Multiple Deployment Models: Fivetran provides cloud, hybrid, or on-premise deployment options to meet every business's requirements. 
  • Secure and Reliable: With Fivetran’s automated column hashing, column blocking, and SSH tunnels, you can protect your organization's data during migration. 

Fivetran Pricing Plan

Starter: This plan is designed for smaller businesses of up to 10 users. You can sync the data from your source to your destination every 1 hour.

Standard: This plan is ideal for rapidly growing teams requiring robust analytics across multiple departments. It supports unlimited users and allows you to sync data every 15 minutes. Enterprise: In addition to the above plan features, this plan provides advanced security features like data governance and granular roles. It also allows teams to sync data in minutes.  

Free: This plan is useful for individuals working with a small amount of data. You can access the Standard plan features without any cost for 14 days.

4. Apache Airflow - Dynamic Workflow Management Solution

Apache Airflow is an open-source platform that lets you automate and monitor the execution of data pipelines. It helps streamline the process of building and scheduling complex workflows. Airflow offers a wide range of operators for performing common integration tasks. With these operators, you can interact with various data sources and destination platforms, including Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure, and many more.

Apache Airflow

Key Features of Apache Airflow

  • Directed Acyclic Graph (DAG): DAG is the core component of Airflow workflows. Defined in a Python script, DAGs allow you to visualize and define your data pipelines as a series of tasks. These tasks can be arranged with dependencies, specifying the order of execution.
  • Dynamic Pipeline Generation: With Airflow, you have the flexibility to create data pipelines dynamically using Python code. This allows you to define workflows dynamically.
  • Extensibility: You can effortlessly define your custom operators that best suit your processing requirements. Here, operators contain logic for each data processing step in Python classes.

Apache Airflow Pricing Plan 

As Apache Airflow is open-source, it is a freely available Python-based orchestration platform licensed under the Apache License 2.0.

5. Hevo Data - Real-Time Data Pipeline Tool

Hevo Data is a data integration and replication solution that helps you move data from various sources to a target system. It enables you to collect data from 150+ data sources, such as SaaS applications or databases, and load it to over 15 destinations utilizing its library of pre-built connectors. Hevo Data also offers a no-code interface, making it user-friendly for those without extensive coding skills.

Hevo Data

Key Features of Hevo Data

  • Real-time Replication: With Hevo’s incremental data load technique, you can keep your target systems up-to-date, ensuring quick analysis. 
  • Data Transformation: Hevo Data offers analyst-friendly data transformation approaches, such as Python-based scripts or drag-and-drop transformation blocks. These approaches help you clean, prepare, and transform data before loading it into the destination.
  • Automatic Schema Mapping: Its auto-mapping feature eliminates the tedious task of manual schema management, automatically recognizing and replicating the source schema to the destination schema. 

Hevo Pricing Plan

Free: With a free tier, Hevo Data provides 1-hour scheduling, access to 40+ connectors, email support, and single sign-on for up to 5 users for 14 days.

Starter: The Starter plan includes free tier features and offers 150+ connectors, custom transformation with dbt, customer support, and SSH/SSL encryption for up to 10 users.

Professional: Along with starter tier features, the professional plan provides REST API for pipeline automation, reverse SSH, pipeline streaming, and additional add-ons for unlimited users. 

Business Critical: This option would be the best for real-time data ingestion with custom pricing. It has professional-tier features, including the RBAC, VPC peering, multiple workspaces, and advanced security certificates. 

6. Apache Kafka - An Event Streaming Platform

Apache Kafka is an open-source distributed event streaming platform. Its central component is formerly Kafka’s Publisher-Subscriber model, which automates data flow from Publishers to Subscribers in real-time. Apache Kafka allows you to build high-performance real-time data pipelines within your organization. It also helps you conduct streaming analytics, perform data integration, and support mission-critical applications. 

Apache Kafka

Key Features of Apache Kafka

  • High Scalability: You can easily scale the Kafka cluster horizontally by adding more brokers to the cluster. This allows you to manage petabytes of data and handle trillions of messages daily. Additionally, you can scale up or down by scaling storage and processing resources as needed. 
  • High Throughput: With Apache Kafka, you can achieve network-limited throughput by delivering messages through a cluster of machines with latencies less than two milliseconds. This efficiency makes Kafka suitable for real-time data pipelines.
  • Permanent Storage: You can securely store the data streams in a distributed, fault-tolerant, and durable cluster. This ensures that data is highly available, even in hardware failures or network issues.

Apache Kafka Pricing Plan

Since Apache Kafka is an open-source project, it is freely available for anyone to use.

7. AWS Glue - A Serverless Data Integration Service

AWS Glue is a cloud-based integration service that streamlines data preparation for faster analysis. It offers 70 different data sources with which you can easily extract and consolidate your data into a centralized system with a manageable catalog. With AWS Glue, you can build, monitor, and execute ETL pipelines visually or through code.

Key Features of AWS Glue

  • Data Catalog: AWS Glue Data Catalog serves as a central repository that allows you to store metadata in tables for your organization’s datasets. You can use it as an index to the location and schema of your data sources.
  • Data Quality: AWS Glue utilizes ML-powered anomaly detection algorithms to help you identify inconsistencies, errors, or unexpected patterns within your data. By proactively resolving complex data quality anomalies, you can ensure accurate business decisions. 
  • Interactive Sessions: AWS Glue provides interactive sessions that allow you to work with data directly within the AWS environment. You can integrate, explore and prepare data with your preferred tools like Jupyter Notebook.

AWS Glue Pricing Plan

AWS Glue pricing is based on the region and resources you consume. Here are some of the AWS Glue resources:

ETL Jobs: With AWS Glue, you only pay for the resources used by your ETL jobs, measured by the execution time. AWS Glue uses Data Processing Units (DPUs) to measure resource consumption.

Data Catalog Pricing: A data catalog object can be a table, partition, table version, or partition index. You can freely store up to one million objects in the catalog.

Data Quality: Adding data quality rules to your ETL jobs to prevent the ingesting of poor data into data lakes increases DPU consumption. This resource will build confidence in your data by helping you achieve high data quality.

8. Matillion - For Diverse Data Integration Needs

Matillion is a cloud platform designed to streamline data integration needs. It caters to various data processing needs, such as ETL, ELT, reverse ETL, and many more. With its extensive library of 100+ connectors, Matillion allows you to effortlessly extract, transform, and load data to your destination. For performing basic to complex transformations, you can either integrate with dbt or use SQL or Python. 


Key Features of Matillion

  • Intuitive Interface: Matillion provides a no-code/low-code interface to create powerful data pipelines within minutes. You can smoothly integrate data into a cloud data warehouse using its user-friendly drag-and-drop feature. 
  • Data Lineage Tracking: In Matillion, you can improve your understanding of data flow by tracing data lineage back to its source. This enables you to identify and resolve issues quickly.

Matillion Pricing Plan

Basic: With a basic tier, you can access Matillion’s features to move, transform, and orchestrate data pipelines. This plan starts from $2.00 per credit usage.  

Advanced: The advanced plan offers everything in the basic tier for an unlimited number of users. It is appropriate if you want to manage group-level or project-level permissions. This plan starts from $2.50 per credit usage.

Enterprise: The Enterprise plan is useful for organizations that require hybrid cloud deployment. This plan offers CDC pipeline development to ensure the data remains up-to-date. It starts from $2.70 per credit usage.

9. Google Cloud Dataflow - Cloud-Based Data Processing Service

Dataflow is a fully managed and serverless service for stream and batch data processing tasks. It can help train, deploy, and manage complete machine learning pipelines. In addition, Dataflow simplifies data processing by allowing you to easily share your data processing workflows with team members and across your organization using Dataflow templates. 

Google Cloud Dataflow

Key Features of Google Cloud Dataflow

  • Horizontal Autoscaling: The Dataflow service can automatically select the required number of worker instances for your job. The service can also dynamically scale up or down during runtime based on the job’s characteristics. 
  • Dataflow Shuffle Service: Shuffle operation is used for grouping and joining data in worker virtual machines. It can move to the Dataflow service back end for batch pipelines, allowing scaling to hundreds of terabytes without tuning. 
  • Dataflow SQL: It allows you to utilize your SQL skills, allowing you to write SQL queries directly within the BigQuery web UI to create simpler processing operations on batch and streaming data. 

Google Cloud Dataflow Pricing Plan

Google Cloud offers transparent pricing for the Dataflow based on the computing resources that your jobs utilize. While pricing is hourly, Dataflow usage is charged per second per job.

10. Azure Data Factory - Cloud-Based Data Integration Service

Azure Data Factory is a serverless data integration service offered by Microsoft. It allows you to easily integrate all your data sources using over 90 pre-built connectors, which require no additional maintenance costs. You can then load the integrated data into Azure Synapse Analytics, a robust data analytics platform, to drive business insights. 

Azure Data Factory

Key Features of Azure Data Factory

  • Code-free Data Flows: ADF utilizes a drag-and-drop interface and a fully managed Apache Spark service to handle data transformation requirements without writing code. 
  • Monitoring Pipelines: With Azure Data Factory, you can visually track all your activity and improve operational efficiency. You can configure alerts to monitor your pipelines, ensuring timely notifications to prevent any issues. 

Azure Data Factory Pricing Plan

The Azure Data Factory pricing is calculated based on the following tasks:

Pipeline Orchestration: It refers to activity runs, debug runs, and trigger executions.

Pipeline Activity Execution: Lookup, Delete, and schema operations are a few pipeline activities. These are executed during integration runtime.  

Data Flow Execution and Debugging: Data Flows are visual components that enable large-scale data transformation.

Data Factory Operations: In Azure Data Factory, read/write operations cost $0.50 per 50,000 updated entries, and monitoring activities charge $0.25 per 50,000 retrieved records.

Final Thoughts

You have explored insights into the top ten data pipeline tools. These tools cater to various integration and replication requirements, offering functionalities for different processing needs, data volumes, and deployment preferences.

Choosing the right data pipeline tool is important to simplify the data integration processes according to your business needs. Consider factors like ease of use, technical expertise, scalability, security features, and cost when making your choice.

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Frequently Asked Questions

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is ?

What data can you extract from ?

How do I transfer data from ?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.

What are top ETL tools to extract data from ?

The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.