The 12 Best Data Pipeline Tools for 2025

Top Data Pipeline Platforms at a Glance

• Astera Data Pipeline
• Apache Airflow
• Apache Kafka
• AWS Glue
• Google Cloud Dataflow
• Microsoft Azure Data Factory
• Informatica
• Talend
• Matillion
• StreamSets Data Collector
• Fivetran
• IBM InfoSphere DataStage

The volume of data generated today is immense, constantly growing and presenting both opportunities and challenges for businesses. The total amount of business data is projected to reach 175 zettabytes, with approximately 328.77 million terabytes created globally every single day.

Large enterprises and SMEs both have a lot to gain by leveraging this data effectively. But they need a reliable data pipeline platform for data movement that can consolidate their data into a centralized repository.

This article serves as a guide to data pipeline tools, explaining what they are, their types, and the challenges they help businesses with. It also provides a curated list of the best tools for building data pipelines and the factors to consider when selecting one.

Let’s first talk about what exactly a data pipeline platform is. Keep in mind that the terms “data pipeline tools,” “data pipeline software,” “data pipeline platforms,” or “data pipeline builder” are used interchangeably throughout this article as they all refer to tools for building data pipelines.

What are Data Pipeline Tools?

Data pipeline tools are software applications that automatically extract data from diverse sources, transform and clean it, and load it into target systems for analysis and decision‑making.

A unified data pipeline platform combines all the necessary features, integrations, tools, applications, and software into a single interface that empowers organizations to build automated data pipelines. It helps organizations ensure data remains accessible when it’s needed the most.

The primary goal of implementing data pipeline tools is to significantly reduce, or even eliminate, the need for manual intervention in data handling processes. To achieve this, these platforms typically feature an intuitive graphical user interface (GUI) that automates the often complex and repetitive stages of data integration:

• Data Extraction: Gathering raw data from a multitude of sources, which can include databases (SQL, NoSQL), APIs, cloud storage, IoT devices, and SaaS applications.
• Data Transformation: Converting and restructuring the extracted data to meet the requirements of the target system and analytical needs. This is a critical step in the traditional ETL (Extract, Transform, Load) process.
• Data Loading: Moving the transformed data into a target system. While historically this has often been a structured data warehouse, organizations are increasingly leveraging data lakes due to their superior ability to store vast quantities of structured, semi-structured, and unstructured data.

A related approach that data pipeline solutions use and is gaining traction is ELT (Extract, Load, Transform). ELT loads raw data into the target system (often a data lake or cloud data warehouse) right after extracting it. Transformations occur later, leveraging the processing power of the target system.

Learn more: What is ELT and why it’s important.

Types of Data Pipeline Tools

Now, let’s talk about different classes of data pipeline platforms. Speaking of data pipelines, it’s worth noting that there’s no universal solution as each platform offers a set of distinct features. So, we categorize data pipeline platforms into various groups based on their core functionality and the use cases they cater to.

Here are some common types:

Real-time Data Pipeline Tools

With real-time data pipeline tools, you can process and analyze your enterprise data as it is generated. These tools provide immediate insights and responses, which makes them crucial for applications that require up-to-the-minute information.

Batch Processing Pipeline Tools

Batch processing tools process data in fixed-size chunks or batches—hence called batch processing. These tools are suitable for scenarios where immediate analysis is not critical. Compared to tools with real-time capabilities, these are simple to implement and maintain. ETL pipeline tools serve as prime examples of batch data processing pipelines.

Open-Source Data Pipeline Tools

Popular open-source data pipeline tools, such as Apache NiFi or Apache Airflow, have gained widespread adoption due to their flexibility, community support, and the ability for users to tailor them to fit diverse data processing requirements. This, however, requires you to have the necessary technical expertise to implement, use, and maintain these frameworks.

No-Code Data Pipeline Platforms

No-code data pipeline platforms, such as Astera Data Pipeline, offer data integration and orchestration tools developed and owned by private companies. These can be both on-premises and cloud-based. Unlike open-source frameworks, these tools require a commercial license or subscription to use. The benefits include dedicated vendor support, user-friendly interfaces, and integrated features designed to streamline the movement and transformation of data across systems.

On-Premises and Cloud Data Pipeline Tools

On-premises tools operate within the organization’s infrastructure, providing a heightened level of control and security over data processing. While they are deployed on-premises, they can still integrate with cloud platforms including cloud data warehouses, cloud databases, etc.

On the other hand, cloud data pipeline tools operate on infrastructure provided by third-party cloud service providers, offering organizations a flexible and scalable solution for managing their data workflows.

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Modern Data Pipeline Tools Go Beyond Basic Capabilities

Today, data pipeline tool and platform providers integrate advanced capabilities directly into their feature sets to make data pipelines ever more intelligent and automated. Here are key capabilities available in today’s data pipeline tools:

Direct LLM Functions as Part of Data Pipeline Tools

Leading data platforms, like Astera Data Pipeline, offer large language model (LLM) based components that can dynamically generate responses based on user-defined prompts. By combining LLM generation with other pipeline objects, such as data sources, transformations, and destinations, you can create AI-powered solutions that adapt to a wide variety of use cases.

Support for Diverse Data Types and Sources

Data pipeline platforms can handle a wide array of data formats, including semi-structured (like JSON, XML) and unstructured data (like logs, sensor data, images). They also offer connectors for a vastly wider range of sources, including cloud services, APIs, streaming platforms, and NoSQL databases.

Real-Time Data Processing

The demand for immediate insights has driven the adoption of real-time data processing. Modern tools can ingest, process, and deliver data with minimal latency, enabling your organization to react quickly to changing conditions, power live dashboards, and support operational analytics. This often involves technologies like change data capture (CDC) to stream data as it changes in the source systems.

The Best Data Pipeline Tools for 2025

Astera Data Pipeline

Astera is a comprehensive, AI-powered data management platform with powerful capabilities to build data pipelines. It offers a powerful yet simple to use visual UI along with built-in capabilities for connecting to 100+ data sources and destinations, data transformation and preparation, workflow automation, process orchestration, data governance, and the ability to handle big data.

It’s a unified solution that simplifies:

• unstructured data management and extraction
• end-to-end data integration via ETL, ELT, and CDC
• the data migration process
• the process of building a data warehouse
• API lifecycle management
• EDI management

Astera stands out in the data pipeline market for several reasons. It offers a comprehensive and powerful data integration tool, which, with its wide range of features, enables users to design, deploy, and monitor data pipelines efficiently. The visual design interface simplifies the pipeline creation process, making it accessible to both technical and non-technical users—it’s easy to navigate and do hours of work in mere seconds.

Astera provides end-to-end visibility and control over data workflows and enables users to track pipeline performance, identify bottlenecks, and troubleshoot issues before they escalate. The platform also offers comprehensive data transformation capabilities, empowering users to cleanse, enrich, and manipulate data within the pipeline itself.

Additionally, Astera offers advanced scheduling and dependency management features, ensuring seamless execution of complex dataflows and workflows. Astera also emphasizes collaboration and teamwork. The platform supports role-based access control, allowing multiple users to work simultaneously on pipeline development and management.

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Apache Airflow

Apache Airflow is an open-source data pipeline tool for creating, scheduling, and monitoring complex dataflows and workflows, offering flexibility and extensive integrations.

Pros

• A flexible and scalable data pipeline solution
• Active community helpful in resolving common challenges
• Ability to monitor tasks and set alerts

Cons

• Steep learning curve makes it difficult to learn and use effectively
• Being an open-source tool means users will need to rely on in-house expertise for their dataflows
• Scheduling feature is rather slow, especially when scheduling multiple tasks

Pricing: No licensing fees for the core platform. Costs incurred for deployment, maintenance, and integration.

Apache Kafka

Apache Kafka is another open-source data pipeline solution. Users can ingest and process data in real-time. It provides a distributed messaging system that allows producers to publish messages to topics and consumers (downstream processing systems, databases, or other applications) to subscribe to these topics and process the messages in real-time.

Pros

• Real-time data processing
• Handles high volumes of data with horizontal scaling
• Offers fault-tolerant replication for mission-critical data

Cons

• Steep learning curve makes it difficult to learn and use effectively, particularly when configuring advanced features such as replication, partitioning, and security.
• For simple scenarios or low data volumes, Kafka’s capabilities are an overkill
• While Kafka itself is open-source, deploying and managing a Kafka cluster involves costs associated with infrastructure, storage, and operational resources

Pricing: While Apache Kafka is free and open source, costs are incurred for related services.

AWS Glue

AWS Glue is a fully managed ETL service on Amazon Web Services. The data pipeline tool offers integration with various AWS services and supporting batch and streaming processing.

Pros

• The biggest advantage of using AWS Glue as a data pipeline tool is that it offers tight integration within the AWS ecosystem.
• Offers built-in features for data quality management
• Can be cost-effective for basic ETL processes

Cons

• Users need a good understanding of Apache Spark to fully utilize AWS Glue, especially when it comes to data transformation
• While it offers integration with external data sources, managing and controlling them remains the responsibility of the user
• Primarily caters to batch-processing use cases and has limitations for handling near real-time data processing.

Pricing: Charges on an hourly rate. Actual pricing information available on request.

Google Cloud Dataflow

A serverless data processing service by Google Cloud that offers batch and stream processing with high availability and fault tolerance.

Pros

• Offers both, batch and stream processing
• Ability to move large amounts of data quickly
• Offers high observability into the ETL process

Cons

• Requires considerable development effort compared to other no-code data pipeline tools
• Users on review platforms report that it’s difficult to learn and use, and the documentation is lacking
• Debugging an issue in a pipeline can be cumbersome

Pricing: Prices vary by region. Visit Dataflow’s pricing page to get the best estimate.

Microsoft Azure Data Factory

Azure Data Factory is an ETL and data integration service offered by Microsoft. It facilitates orchestration of data workflows across diverse sources.

Pros

• Like other data pipeline tools, it offers a no-code environment
• Being Microsoft’s own service means tighter integration within the ecosystem
• Azure Data Factory offers a single monitoring dashboard for a holistic view of data pipelines

Cons

• The visual interface quickly becomes cluttered as the workflows become more complex
• Does not offer native support for change data capture from some of the most common databases
• The error messages are not descriptive and lack context, making it hard to troubleshoot

Pricing: Users are charged in terms of data integration units (DIUs). Given the complex pricing model, it’s best to visit their pricing page.

Informatica PowerCenter

Informatica PowerCenter is a data pipeline software with the ability to extract, transform, and load data from various sources.

Pros

• Offers features to maintain data quality
• Ability to handle large amounts of data
• Like other data pipeline software, it provides built-in connectors for different data sources and destinations

Cons

• Steep learning curve, even for beginners with a technical background due to a largely confusing and inconsistent mix of services and interfaces
• Handling large amounts of data is resource-intensive with Informatica
• Another problem with Informatica is the cost, which is largely prohibitive, especially for small businesses

Pricing: Charges users based on their consumption. Actual prices are available upon request.

Read more about Informatica alternatives.

Talend Data Integration

Talend Data Integration is an enterprise data integration tool. It enables users to extract, transform, and load data into a data warehouse or data lake. Talend was officially acquired by Qlik in 2023, marking a significant shift in its positioning. As of 2025, Talend’s capabilities are now embedded within Qlik’s data integration and analytics ecosystem

Pros

• Handles large amounts of data
• Ability to integrate on-premises and cloud systems
• Can integrate with different business intelligence (BI) platforms

Cons

• Requires considerably high processing power, which means it’s not as efficient
• Joining tables from different schemas is not straightforward during ETL
• Users frequently report that Talend’s documentation is not comprehensive enough

Pricing: Offers four tiers, Starter, Standard, Premium, and Enterprise, with pricing available on demand.

Read more about Talend alternatives.

Matillion

An ETL platform that allows data teams to extract, move, and transform data. While it allows users to orchestrate workflows, it’s mostly focused around integrating data only.

Pros

• Offers a graphical user interface
• Wide range of built-in transformations
• Generally easy to use compared to Informatica and Talend

Cons

• Git integration can be improved
• While it offers built-in connectors, setting them up is not straightforward in some cases
• Doesn’t offer advanced data quality features

Pricing: Offers four tiers, Developer, Basic, Advanced, and Enterprise. Price starts at USD 1,000 for 500 credits per month for the Basic tier.

Read more about Matillion alternatives.

StreamSets Data Collector

Now acquired by IBM, StreamSets was a data ingestion platform focused on real-time data pipelines with monitoring and troubleshooting capabilities.

Pros

• Ability to schedule jobs
• Features a graphical UI
• Supports both batch and stream processing

Cons

• Understanding and filtering the logs is not a straightforward task
• Processing with JDBC is significantly slow
• Debugging takes up a considerable amount of time

Pricing: Pricing information is available on request.

Fivetran

Fivetran’s approach to data pipelines is primarily around ELT, where data is loaded into the destination first and then transformed using tools like dbt. It focuses on providing pre-built, maintenance-free connectors for a wide array of applications, databases, and file systems, allowing businesses to centralize their data for analytics and other purposes without extensive engineering effort.

Pros

• Fivetran offers a large number of pre-built connectors for diverse data sources
• It automates schema changes and handles data synchronization with minimal user intervention
• The platform generally offers a user-friendly interface and straightforward setup for creating and managing data pipelines

Cons

• The consumption-based pricing model (based on monthly active rows – MAR) often leads to unpredictable costs
• While it offers many connectors, the transformation logic often requires reliance on post-load transformations in the destination
• Although it offers varying sync frequencies, it is primarily a batch-based system

Pricing: Fivetran offers four plans based on monthly active rows (MAR), Free, Standard, Enterprise, and Business Critical.

IBM InfoSphere DataStage

Primarily an ETL tool, IBM InfoSphere DataStage is a key component of the IBM InfoSphere Information Server suite. It enables businesses to build scalable and flexible data pipelines. Like all data pipeline platforms, DataStage utilizes a graphical interface where users design dataflows using a wide range of pre-built stages and connectors.

Pros

• DataStage offers a built-in library of transformation components and functions
• It supports both ETL and ELT pipelines
• Like Astera, it uses a scalable parallel processing engine to handle large volumes of data

Cons

• Users have reported that the interface can be non-intuitive, leading to a steeper learning curve
• DataStage is considered expensive compared to some competitors, which can be a barrier for smaller organizations
• Some users have found the available documentation insufficient, making troubleshooting and learning more challenging

Pricing: IBM offers several pricing plans for DataStage. Standard plan starts at 1.828USD per capacity unit-hour (CUH).

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How to Choose the Right Data Pipeline Platform for Your Business

With numerous options available in the market, choosing a data pipeline platform naturally becomes a technical and strategic decision. This is primarily because no pipeline tool is perfect. The right platform is the one that aligns with your team’s needs, technical environment, and data strategy.

So, it’s important to take time to evaluate the tools based on your actual use cases and future plans and not just feature lists. Before diving in, make sure any contender meets these essentials: seamless scalability, native connectors for your stack, versatile data transformations, clear operational visibility, and a transparent pricing model. If a tool excels in these areas, you can then consider other specialized features.

The selection process involves evaluating several key factors, which include:

1. Scalability: Assess whether the tool can handle your current and future data volume and velocity requirements.
2. Data sources and targets: List every data source and destination you use, including databases, file formats, cloud services, data warehouses, data lakes, and APIs, then validate whether the tool provides first-party connectors or certified integrations. Third-party plugins can work, but they often struggle with feature parity and updates.
3. Data transformation and integration: Evaluate the tool’s capabilities for data cleaning, transformation, and integration. If your team writes SQL or Python, a lightweight pipeline tool with code hooks might suffice. Otherwise, look for a platform with features that simplify complex data mapping, merging, and handling different data types.
4. Real-time vs. batch processing: Assess whether real-time streaming or batch processing is suitable for your pipeline needs. Then, determine if the data pipeline tool supports your preferred data processing mode.
5. Ease of use and learning curve: If your team largely comprises of non-technical users, consider the tool’s user interface, configuration simplicity, and usability. Look for intuitive interfaces, visual workflows, and drag-and-drop functionalities to streamline pipeline development and management.
6. Monitoring and alerting: Check if the data pipeline tool provides comprehensive monitoring and alerting features. It should offer visibility into pipeline health, performance, and status, including logs, metrics, error handling, and notifications for efficient troubleshooting.
7. Security and compliance: Ensure the tool provides robust security measures such as encryption, access controls, and compliance with relevant regulations (e.g., GDPR, HIPAA) when handling sensitive or regulated data.
8. Integration with your existing infrastructure: Evaluate how well the data pipeline tool integrates with your current infrastructure, including data storage systems and analytics platforms. Seamless integration can save time and effort in pipeline setup and maintenance.
9. Support and documentation: Assess the level of support and availability of documentation from the tool’s vendor. Look for comprehensive documentation, user forums, and responsive support channels to assist with troubleshooting.
10. Total cost of ownership (TCO): Consider the overall cost of the data pipeline tool, including licensing, maintenance, and additional resources required for implementation and support. Evaluate if the tool provides good value based on its features and capabilities.

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What Business Challenges do Data Pipeline Tools Overcome?

Businesses rely on automation and advanced technologies, such as artificial intelligence (AI) and machine learning (ML), to manage and use extremely high volumes of data to their advantage. Handling high volume data is just one of many challenges data pipeline tools enable businesses to overcome—these tools address a spectrum of challenges that organizations face in navigating the complexities of data processing.

Data Integration and Consolidation

• Challenge: Businesses often have data scattered across various systems and sources, making it challenging to integrate and consolidate for a unified view.
• Solution: Data pipeline tools facilitate the extraction, transformation, and loading processes, enabling seamless integration and consolidation of data from diverse sources into a central repository.

Real-Time Decision-Making

• Challenge: Traditional batch processing methods result in delayed insights, hindering real-time decision-making.
• Solution: Real-time data processing enables businesses to analyze and act on data as it is generated, supporting timely decision-making.

Data Quality and Consistency

• Challenge: Inaccuracies, inconsistencies, and poor data quality can lead to unreliable insights and decision-making.
• Solution: Modern data pipeline tools, like Astera, offer data quality features, allowing businesses to clean, validate, and enhance data, ensuring accuracy and consistency.

Scalability and Performance

• Challenge: Handling growing volumes of data can strain traditional systems, leading to performance issues and scalability challenges.
• Solution: Cloud-based data pipeline tools provide scalable infrastructure, allowing businesses to dynamically adjust resources based on workload demands, ensuring optimal performance.

Operational Efficiency

• Challenge: Manually managing and orchestrating complex data workflows can be time-consuming and prone to error.
• Solution: Workflow orchestration tools automate and streamline data processing tasks, improving operational efficiency, and reducing the risk of human errors.

Final Thoughts: Which Data Pipeline Tool is Best for Your Organization?

Data pipeline tools have become an essential component of the modern data stack. As the amount of data continues to rise, these tools become even more important for managing the flow of information from ever-growing sources.

However, no two tools are created equal. Choosing the right tool depends on several factors. Some tools excel at handling real-time data streams, while others are better suited for batch processing of large datasets. Similarly, some solutions offer user-friendly interfaces with drag-and-drop functionalities, while others require coding experience for customization. Ultimately, the best data pipeline tool will be the one that satisfies the business requirements.

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