Top Data Pipeline Tools for 2025: Types, Features, Pros, and Cons

Data in the digital world is like oil in the fossil fuel for vehicles. Similar to how transportation means need fuel to ride, companies need data to move forward.

Obviously, cars can’t work with raw oil to drive since this material needs to be transformed into petrol first. The same goes for businesses with information systems – they need to process data before use.

Data pipeline tools collect all the required data and make it usable. So, this article explores popular solutions along with their features, limitations, and pricing. It also compares different kinds of data pipeline tools and provides real-world examples of how and when to use them.

Table of contents

1. What Are Data Pipelines
2. Types of Pipelining Software
3. Top 10 Data Pipeline Tools
   • Skyvia
   • Fivetran
   • Apache Airflow
   • Airbyte
   • Stitch
   • Talend
   • Integrate.io
   • Matillion
   • StreamSets
   • Apache Spark
4. How to Build Data Pipelines Using Skyvia Data Flow
5. Case Studies of Data Pipelines in Action
6. How to Choose a Data Pipeline Tool
7. Conclusion

What Are Data Pipelines

“A data pipeline is a method in which raw data is ingested from various data sources, transformed and then ported to a data store, such as a data lake or data warehouse, for analysis.” – IBM.

Data pipeline tools are software applications that enable users to build data pipelines. The primary objective of these solutions is to automate data collection and flow by connecting source and destination tools.

Many such tools also offer simple and sophisticated options to transform data on the go. Data pipeline solutions allow businesses to automate various data-related processes, manage large data sets effectively, and enhance reporting.

Components of a Data Pipeline

The structure of a data flow differs from one company to another. Anyway, each data pipeline contains more or less the same set of components:

1. Data sources. Each business has its toolset (SaaS apps, databases, IoT systems, etc.), which constitutes a starting point of a data pipeline. 
2. Data ingestion tools. They extract raw data from the source systems either in a batch or streaming mode. The first option is better for periodical data retrieval, while the second one is suitable for real-time processing.
3. Transformation features. Cleansing and normalizing data after ingestion is crucial to ensure quality in further pipeline stages.
4. Storage destinations. After transformations, data goes to the destination database, data warehouse, or another storage system.
5. Delivery and consumption systems. These are usually BI and analytics tools, machine learning platforms, AI apps, and other systems that consume and utilize data.

Data Pipelines vs. ETL Processes

Data pipelines are often interchangeably used as ETL pipelines to describe automated data integration processes. However, these notions aren’t identical even though they share some processes in common.

In a nutshell, an ETL pipeline is just a part of an entire data pipeline, which takes care of batch collection, transformation, and loading of structure data. Meanwhile, typical data pipelines can handle real-time workflows, unstructured data, and complex integration scenarios.

Feel free to check the key differences between ETL and data pipelines.

Benefits of Data Pipelines: Why You Need Them

Apart from automating and streamlining workflows, data pipelines bring a number of other benefits to businesses.

1. Centralized data management. Since these solutions can consolidate data in a single central location, they enable the creation of the SSOT (single source of truth). This provides a centralized data repository that anyone on the team can access.
2. Flexibility. Modern data pipelines are easily scalable and elastic, which means they can adapt to changing data loads and volumes.
3. Data quality. Due to the transformation options, data is cleansed and standardized, which improves its quality. At this stage, raw data is converted into a uniform format and becomes suitable for analysis and other operations.
4. Enhanced decision-making. Data pipelines make data flow through various stages and transformation processes, making it usable for reporting, analytics, predictions, etc. This promotes a data-driven approach to deriving insights and evaluating business performance outcomes.

Types of Pipelining Software

There are many data pipeline tools available on the market. They are categorized according to their licensing type, purpose of use, and operational environment.

Open-source vs. Licensed Tools

Open-source solutions are available at no cost and allow users to modify the source code. Anyone can install and use them on their systems. 

Here are some examples of the open-source services: 

• Apache Airflow
• Airbyte
• Dagster

The licensed data pipeline tools require a valid subscription for accessing and using them. Some companies offer a trial version for users to explore the functionality of the chosen service.

Here are some examples of the licensed solutions:

• Skyvia
• Hevodata
• Fivetran 

Cloud vs. On-premise Tools

Cloud data pipeline platforms are fully managed, meaning that users don’t have to install them and take care of the underlying infrastructure. All the data transfer and processing happens over the cloud servers. These tools can also scale easily when required.

Here are some examples of cloud solutions:

• Skyvia
• Google Dataflow
• StreamSets

Many organizations don’t want their data stored or processed on the cloud to remain coherent with privacy policies. On-premise tools are installed on servers maintained by a dedicated team within the organization. These solutions aren’t as scaleable as their cloud alternatives.

Here are some examples of the on-premise systems:

• Apache Airflow
• Oracle Data Integrator
• Informatica

Stream vs. Batch Tools

Stream services process data as soon as it arrives on a real-time basis. Here are some of the tools supporting stream data flows:

• Apache Spark
• Apache Nifi
• Google Dataflow

Batch data pipeline tools run at regular intervals and extract data in chunks. Here are the examples of such platforms that support batch processes:

• Skyvia
• Apache Airflow
• Talend

Top 10 Data Pipeline Tools

Skyvia

G2 Rating: 4.8 out of 5 (based on 200+ reviews).

Skyvia is a cloud-based platform for data integration with a no-coding approach. It supports ELT and ETL scenarios, allowing users to connect to a wide range of data sources and build integration pipelines visually. 

Skyvia provides several products for building integrations of various complexity, creating data endpoints, automating backups, and many more. Since this tool runs in the cloud, it can be accessed via any web browser without any added software installations.

Pros

• Requires no coding.
• More than 200 connectors to cloud sources, databases, and data warehouses.
• Provides SSH and SSL connection support.
• Has a free trial in addition to flexible pricing plans.
• Responsive support team.

Cons

• Limited transformation options in the Free plan.
• Basic error handling.

Pricing

Skyvia provides five pricing plans.

	Free	Basic	Standard	Professional	Enterprise
Starting price	$0	$79/month	$159/month	$199/month	Custom
Records per month	10k	5M+	500k+	5M+	Custom
Scheduled integrations	2	5	50	Unlimited	Unlimited
Integration scenarios	Simple			Simple and advanced

2. Fivetran

G2 Rating: 4.2 out of 5 (based on 380+ reviews).

Fivetran is a web-based platform that allows users to create data pipelines in the cloud. It enables data replication from various SaaS (Software as a Service) sources and databases with ELT pipelines

Fivetran offers no-code connectors to databases or data warehouses that can be used to build integrations. This tool relies on automation to effectively handle schema changes, significantly minimizing manual input.

Pros

• Support of over 150+ connectors for better connectivity.
• 24/7 technical support for quick resolutions.
• A fully managed approach minimizes coding and customization in building data pipelines.

Cons

• Fivetran supports only ELT pipelines but not ETL pipelines. This means data transformation isn’t supported before it achieves the destination.
• Minimal scope for customization of the code.

Pricing

Fivetran provides four pricing plans.

	Free	Starter	Standard	Enterprise
Users	10	10	Unlimited	Unlimited
Sync frequency	Hourly	Hourly	15 minutes	1 minute
Monthly active rows (MAR)	up to 500,000	Flexible
Starting price	$0	Depends on MAR

3. Apache Airflow

G2 rating: 4.5 out of 5 (based on nearly 100 reviews).

Airflow is an open-source data pipeline orchestration tool. It uses Python programming to create and schedule workflows, also known as DAGs (Directed Acyclic Graphs). You can also monitor and orchestrate these workflows with the Python-based interface.

Along with creating data pipelines, Airflow can help you with other tasks. For example, it allows you to manage infrastructure, build machine learning models, or run Python code. What’s more, it provides logs of the completed and running workflow through its UI.

Pros

• Free to use as it’s an open-source solution.
• Airflow can be easily integrated with version control systems like Git.
• Users can customize the existing operators or define them depending on a use case.

Cons

• Building data pipelines requires Python knowledge.
• Airflow doesn’t provide any dedicated technical support, so users need to rely on community support in case of issues.

Pricing
Since Apache Airflow is an open-source solution, it’s available for free.

4. Airbyte

G2 rating: 4.5 out of 5 (based on nearly 50 reviews).

Airbyte is an open-source data integration platform. It easily builds ELT data pipelines with the help of no-code pre-built connectors for both data sources and destinations.

This tool also provides CDK (Connector Development Kit) for creating custom connectors. You can also use this kit to edit the existing connectors to match your particular workflows.

Pros

• Airbyte provides an open-source version as well as licensed ones that help users manage all the operational processes.
• Supports integrations with other stacks such as Kubernetes, Airflow, Prefect, etc.
• The licensed edition provides 24/7 technical support for any debugging.

Cons

• Airbyte supports only ELT pipelines.
• Creating connectors with CDK requires coding knowledge.

Pricing

The open-source version of Airbyte comes for free and can be installed on your servers. A paid enterprise version can also be hosted on your infrastructure. Otherwise, you may choose a cloud-based paid version at a custom price, which offers support, extra security features, and other benefits.

5. Stitch

G2 Rating: 4.4 out of 5 (based on 60+ reviews).

Stitch is a cloud-based data pipeline tool. It provides connectors to 130+ sources that can be configured in a visual interface, which makes it easy to ingest data into a warehouse.

This tool also ensures orchestration, embedding, data transformation, and other features for pipeline management. You can also use Stitch API to push any data to the destination system in a programmatic way.

Pros

• Includes a dashboard for data pipeline tracking and monitoring.
• Provides community-driven development and integration with different tools through the Singer project.
• Has scheduling options to run jobs at predefined intervals.

Cons

• No on-premise version.
• Some connectors are accessed only with the Enterprise version. 
• Limited customer support.

Pricing

Stitch has three pricing plans.

	Standard	Advanced	Premium
Starting price	$100/month	$1250/month	$2500/month
Rows per month	5-300 M	100 M	1 B
Destinations	1	3	5
Sources	10 (standard)	Unlimited	Unlimited
Users	5	Unlimited	Unlimited

6. Talend

G2 Rating: 4.0 out of 5 (based on 65 reviews).

Talend offers multiple products and services, both open-source and paid ones. Talend Data Studio is a free, open-source solution, while Talend Data Fabric is a paid version, which includes Talend Big Data, Management Console, API Services, Data Inventory, Pipeline Designer, etc.

In particular, the Talend Pipeline Designer tool is dedicated to constructing data pipelines. It’s a web-based tool that builds complex ETL dataflows and processes data during the transit.

Pros

• Supports both on-premises and cloud data pipelines.
• Provides the ability to design and test APIs for data sharing.
• Handles unstructured data along with structured and semistructured data.

Cons

• No transparent pricing – you need to contact the sales team for a quote.
• The complex installation process for on-premise versions.
• Limited features and connectors for Talend Open Studio.

Pricing

The price for Talend solutions is discussed with their sales managers.

7. Integrate.io

G2 Rating: 4.3 out of 5 (based on 200+ reviews).

Integrate.io is a cloud-based data integration platform. It allows users to build ETL, reverse ETL, and ELT data pipelines. This tool also supports API generation and CDC technology.

Integrate.io allows users to create and manage data pipelines with minimal coding requirements. Before ingestion, it’s also possible to apply filters to extract data based on the specified condition.

Pros

• Ability to pull data from any source that offers a REST API.
• Creates dependencies between multiple data pipelines.
• Offers API generation for multiple databases, security, network data sources, etc.

Cons

• A limited number of connectors. The available connectors are focused on e-commerce use cases.
• Doesn’t offer an on-premise version.

Pricing

The price for Integrate.io is discussed with their sales managers.

8. Matillion

G2 rating: 4.4 out of 5 (based on 80 reviews).

Matillion is a cloud-native data integration tool that provides an intuitive user interface to develop data pipelines. There are two products Matillion offers: Data Loader for moving data from any service to the cloud and Matillion ETL to define data transformations and build data pipelines on the cloud.

Matillion ETL is a fully-featured data integration solution for creating ETL and ELT pipelines within a drag-and-drop interface. This tool can be deployed on your preferred cloud provider.

Pros

• Provides connectors to both cloud and on-premises data systems.
• Contains features for data orchestration and management.
• Data transformations can be performed either with SQL queries or via GUI by creating transformation components.

Cons

• Lack of documentation describing features and instructions for their configuration.
• There is no option to restart tasks from the point of failure. The job needs to be restarted from the beginning.

Pricing

Matillion offers four pricing plans.

	Developer	Basic	Advanced	Enterprise
For whom	individuals	growing teams	scaling businesses	large organizations
Starting price	$0/month	$1000/month	$2000/month	Custom
Users	2	5	Unlimited	Unlimited
Support	Community	Standard	Standard	Premium

9. StreamSets

G2 rating: 4.0 out of 5 (based on nearly 50 reviews).

StreamSets is a fully managed cloud platform for building and managing data pipelines. It offers two licensed editions – a professional edition with a limited set of features and an enterprise edition with extensive support and full functionality. 

This tool supports 100+ connectors to databases and SaaS apps for easy creation and management of dataflows. It supports two types of engines to run data pipelines:

• Data collector that supports batch, streaming, and CDC modes.
• Transformer engine that applies transformations on the entire dataset.

Pros

• Supports integration with multiple SaaS apps and on-premise solutions.
• Handles batch and streaming data pipelines.

Cons

• No on-premise solution.
• The users might require Kubernetes knowledge as the data pipelines run on top of it.

Pricing

The price for IBM StreamSets is discussed with their sales managers.

10. Apache Spark

G2 rating: 4.3 out of 5 (based on 50+ reviews).

Apache Spark is an open-source data transformation engine. It can be integrated with a wide range of frameworks, supporting a wide variety of use cases.

Users can build data pipelines that process real-time as well as batch data with Apache Spark. They can also perform Exploratory Data Analysis (EDA) on large data volumes and run SQL queries by connecting to different storage services.

Pros

• It’s free to use as it is open-source.
• Offers support for multiple languages such as Python, Scala, Java, R, and SQL.

Cons

• It requires extensive coding experience to implement data pipelines.
• Debugging is challenging as there is no dedicated technical support, though there is an extensive community that can help address issues.

Pricing

Since Apache Spark is an open-source solution, it’s available for free.

How to Build Data Pipelines Using Skyvia Data Flow

If you decide to select Skyvia for building data pipelines, you can do that with one of the available solutions:

• Import tool
• Replication tool
• Synchronization tool
• Data Flow tool

Here, we’ll explore how to build and set up pipelines with Data Flow in Skyiva. This solution allows you to create a diagram of connected components in a visual drag-and-drop interface.

Sample Task Description

Let’s assume you have to create and configure an integration scenario involving three data systems:

1. A source database. It has the Customers table, which includes the CompanyName and ContactName fields.
2. A CRM. In our case, it’s Hubspot CRM that stores all the deals-related information and has the Number Of Opened Deals field in the Companies table.
3. A target database. It contains the Contact table that should store CompanyName, ContactName, with the Number Of Opened Deals in it.

Prerequisites

Make sure you have the Skyvia account or create a new one. Note that you can use this tool for free to try out all the fundamental features.

The next step is to configure connections for the databases and HubSpot.

1. In your Skyvia account, go to + Create New -> Connection.
2. Choose the required connector from the list and click on it.
3. Fill out the required credentials. See the instructions provided next to the setup form.
4. Click Create.

Solution

Once you’ve created all the connections, you can start building a data pipeline with the help of Data Flow. To do so, go to + Create New -> Data Integration -> Data Flow in your Skyvia account.

Add components to the board by dragging them from the panel on the left. Then, link the components by connecting the input and output circles on the diagram.

Source Setup

Source extracts data from the selected system and makes up a starting flow of the Data Flow diagram.

1. Drag the Source components from the panel to the diagram and click on it.
2. Select the system from the Connection dropdown in the right panel.

3. Select Execute Command from the Actions drop-down list.
4. In the Command Text box, create an SQL query.

SELECT CompanyName, ContactName FROM Customers

5. Check the output results by clicking on the output arrow.

Lookup Setup

The Lookup component aims to match the records in different data systems. It adds columns of the matched records to the scope.

In our case, the source output consists of two columns: CustomerName and ContactName. Let’s add the Number Of Opened Deals column from the Companies table in HubSpot to the scope with the help of the Lookup component.

1. Click the Lookup component on the left panel and drag it to the diagram.
2. Choose HubSpot from the Connection dropdown list.

3. Choose Lookup from the Actions dropdown list.
4. Select the table under the Table dropdown list. The Number Of Opened Deals column is stored in the Companies table.

5. Select the Company name in the Keys field.

6. Select the Number Of Opened Deals column from the Result Columns dropdown list. It will be added to the output results.
7. Open Parameters to map keys. In this example, we map the Company name in HubSpot to the CompanyName in the source database.

8.  Click on the output arrow to check the changes in the output results.

Target Setup

The target component defines the system to where you want to load your data.

1. Click Target on the left panel and drag it to the diagram.
2. Select the data system from the Connection dropdown on the right panel.

3. Select the DML operation (INSERT, UPDATE, DELETE, UPSERT) from the Actions dropdown list.
4. Select the Contact table to load data.
5. In the Parameters field, map the Lookup output columns with the Contact table columns.

Case Studies of Data Pipelines in Action

In general, data pipelines can be of various complexity. While some may contain 2-3 elements, others may comprise 10+ components. Skyvia is the data integration tool that effectively interconnects all these components. In fact, Skyvia appears in the center of the pipelines and coordinates data flows in a pipeline like a heart coordinates blood fluxes in the cardiovascular system.

Let’s look at several practical use cases where Skyvia was chosen as a data pipeline tool for dataflow management.

Enhanced Inventory Management at Redmond Inc.

The main challenge of Redmond Inc. was to synchronize the information on inventory stocks between Shopify and their internal ERP system. What’s more, they needed to obtain a unified view of orders and inventory stocks.

Thanks to Skyvia’s implementation, the company has improved operational management. They also enhanced customer satisfaction since the Customer Service department obtained a complete overview of stock items.

Optimized Workflow at FieldAx

This company is a leading provider of training services, which faced the challenge of managing a geographically dispersed workforce one day. It became demanding to coordinate assignments and track the progress of employees in different counties. What’s more, it was rather expensive to buy licenses for each technician, which often caused budget constraints.

In response to these challenges, FieldAx imported installation details to a local database (MySQL) instead of purchasing individual licenses. Once the data was in place, Skyvia seamlessly synchronized it with the FieldAx software’s corresponding fields, such as technician names, job numbers, and job statuses. Overall, the collaboration between FieldAx and Skyvia has yielded transformative results for the company, enabling them to streamline job management processes while reducing costs significantly.

Automated Data Analytics Pipeline at TitanHQ

This company is a leading SaaS cybersecurity platform delivering a layered security solution to prevent user data vulnerability. Their management team wanted to obtain a 360-degree view of their customers, but that wasn’t easy since data was dispersed across different systems (data warehouse, Sugar CRM, Maxio, ticketing and payment services, etc.

With Skyvia, TitanHQ engineers built data pipelines to gather data from CRM, payment, and ticketing systems into the Snowflake data warehouse. Then, this data was prepared and sent to Power BI to generate dashboards that provided the management team with valuable insights into their customer base.

How to Choose a Data Pipeline Tool

Choosing the right solution for building pipelines is similar to choosing other services for a business tool kit. You first need to evaluate how the pricing of the selected platform matches your budget. Then, pay attention to its scalability and usability to make a choice. To help you make a weighted decision, we’ll go into detail on the factors to consider for that.

1. Ease of use. Make sure a data pipeline tool has an intuitive interface, allowing you to set up dataflows visually. This aspect is particularly important if non-tech specialists with no coding experience are going to work on the data integration processes for their needs. The intuitive interface will help them organize dataflows on the go without the intervention of IT experts.
2. Scalability. Make sure that the selected service can handle fluctuating volumes of data. This is important when a company grows and experiences large volumes of data. It’s also essential when data amounts tend to decrease.
3. Integration capabilities. Explore the range of pre-built connectors provided by the data pipeline tool. Make sure it supports systems that you need to include in your data pipelines.
4. Processing speed. If your organization primarily works with live data, the chosen solution needs to be able to process a continuous flow of data and support real-time analytics. In other cases, batch data extraction will work fine.
5. Security. The ability of a data pipeline solution to handle sensitive information properly is fundamental. Check for encryption, authentication, role-based access control, and support of safety protocols is granted by the tool providers. Verify whether it also complies with contemporary regulations for user data protection, such as GDPR, HIPAA, etc.

Conclusion

Data pipeline tools can be of different types and for different purposes. In this article, we have explained the differences between them and have given some hints on when each of them will be the most appropriate. To help you choose the right solution for your data needs, we have also presented popular services for data pipeline management along with their features, advantages, and drawbacks.

We have also presented some real-life examples of how to use such tools to increase the effectiveness of data management within an organization with Skyvia. This platform provides various data integration scenarios suitable for various cases. What’s more, it offers regular scheduling options for automated data flows. Feel free to use Skyvia to organize your data and extract its value.

F.A.Q.