Best Data Pipeline Tools

Data pipelines are essential to successfully extracting, transforming, and loading data from one system to another. Effective data pipelines are made up of efficient tools that automate data tasks, increase data quality, and turn your raw data into revenue-generating insights.

A wide range of tools are available—from ELT tools like Fivetran to data orchestration platforms like Shipyard. But choosing the right data pipeline tool isn’t always easy. With so many options on the market, it can be hard to know which combination of features works best with your data initiatives.

We put this article together to help you choose from four of the best data pipeline tools available. We’ll cover what a data pipeline tool does, the benefits of using one, and outline some of the best data pipeline solutions.

Let’s start with a summary of what a data pipeline tool is.

What is a data pipeline tool?

A data pipeline tool is technology that moves data from source to destination. It can ingest data from multiple sources and move it to a cloud data warehouse to make raw data useful for your business. These tools are essential for keeping data organized, accessible, and usable. Data pipeline tools are crucial to data operations (DataOps) because they manage, monitor, and automate the flow of raw data as it moves from collection to processing and transformation to a final analytical result.

Data pipeline technology is constantly evolving, and new solutions are developed all the time. For instance, you might be familiar with extract, transform, and load (ETL) data tools, but did you know there are also reverse ETL data pipeline tools?

You might have a custom-built ETL process running at your company that does the job but is clunky and prone to error. This is the perfect use case to replace a manual data process with a data pipeline tool.

Chances are, you either need a data pipeline tool to replace clunky processes or your data analytics team needs a better tool—one that would increase efficiency, improve data quality across company datasets, and make your data more usable to every part of your business. For example, you might want to stop relying on Google Sheets for tracking your employee data and automatically move it to a central source that performs data visualization.

Here are the major benefits of choosing the right data pipeline tool for your business.

What are the benefits of data pipeline tools?

The main benefit of data pipeline tools is that you can move your data to where it needs to be in a usable format. The pipeline tools keep data sets up-to-date so you never have old or broken data.

Using a data pipeline tool also builds your data observability, showing your data scientists, data engineers, data analysts, and machine learning specialists where data flows are healthy and when data jobs break.

Here’s a short list of the significant benefits of using data pipeline tools instead of manual data processes:

Reduced data processing time: By using the right tools, organizations reduce the time it takes to process data.
Automate DataOps tasks: Create automated data flows from source to destination to provide real-time data for business intelligence, web products, and data science teams.
Improved data quality: Data pipelines improve data quality by ensuring that only clean and accurate data moves between systems.
Streamline data management processes and workflows: Reduce wasted time spent on tracking down raw data and moving it to the right place at the right time.
Better decision-making: With access to timely and accurate data, organizations can make better decisions about their business operations.

No matter the size of your company, you can benefit from adding a data pipeline tool to your data infrastructure. Data pipeline tools streamline your data management processes, make sure all your data is up-to-date and accurate, and give you insight into your company’s data trends.

Let’s walk through some different ways you might want to use data pipeline tools at your company.

Reasons to use data pipeline tools

Any time you want to move data from a data source—everything from your SaaS vendors to social media analytics APIs—a data pipeline tool ingests the data. So when you want data from your CRM software moved to your cloud data warehouse, you need a data pipeline tool to do the job.

Want to find out how many customers from Instagram convert after they visit your website? Set up a data ingestion pipeline to collect data from Instagram and your web analytics and move it to a place for analysis and data modeling.

Here are some of the most common data sources and destinations you’ll use your data pipeline tool to connect:

Common data sources

Website analytics
Email marketing software
CRM software
Social media platforms
Cloud storage
HTTP clients
SFTP and FTP
Business file management

Common data destinations

Cloud data warehouses
Data lakes
Relational databases
Apache Kafka
Snowflake
Amazon S3
Databricks

Your DataOps team can build as many data pipelines as you need to give your business accurate data in real-time, reliable reporting, and enhanced decision-making tools.

Now that you know more about what data pipeline tools can do for your business, let’s take a look at our four favorite solutions.

Best data pipeline tools

From Fivetran to the wide-open orchestration capabilities of Shipyard, here are our four favorite data pipeline tools.

Fivetran Data Pipelines

Fivetran is a popular data pipeline tool that replicates applications, databases, events, and files into high-performance cloud warehouses. Its ease of setup (connecting data sources with destinations) makes it one of the most intuitive and efficient data pipeline tools.

Fivetran pulls data from 5,000 cloud applications and allows you to add new data sources quickly. It supports advanced data warehouses like Snowflake, Azure, Amazon Redshift, BigQuery, and Google Cloud, so you can query your data easily.

Features like real-time monitoring, connectors, alerts, and system logs further empower data analysts and data engineers to build robust pipelines using Fivetran.

Top use case:

Fivetran is an ideal data pipeline tool for those who are just getting started and looking for a tool that’s quick to set up and easy to use. It’s also a compelling choice for enterprises that want to move data from dozens of data sources into warehouses without unnecessary hassle.

Pros:

Automated pipelines with standardized schemas
No training or custom coding required
Add new data sources easily
Complete replication by default
Customer support via a ticket system

Cons:

No option to use/deploy services on-premise
Tricky to figure out the final cost of the platform

Pricing:

Fivetran offers a 14-day free trial for each paid plan.
It has four paid pricing plans. You can also request a custom quote if you’re an enterprise that needs access for unlimited users and usage.

Apache Airflow

Apache Airflow is a popular open-source data pipeline tool. It lets you monitor, schedule, and manage your workflows using a modern web application.

The core concept of Apache airflow is a DAG (Directed Acyclic Graph), in which you need to arrange tasks with upstream and downstream dependencies that define the logical flow of how they should run.

Airflow pipelines are defined in Python, meaning users must use standard Python features to create workflows and dynamically generate tasks. As a seasoned data engineer, this is great news—Python allows users to maintain full flexibility when building workflows.

Top use case:

Apache Airflow is a good option for data engineers and data analysts who prefer to work only in Python.

Pros:

Excellent functionality for building pipelines
Support via Slack

Cons:

Slow to set up and learn to use
Requires advanced knowledge of Python
Modifying pipelines is difficult once they've been created

Pricing:

Apache Airflow ETL is an open-source platform, licensed under Apache License Version 2.0.

Stitch

Stitch is a cloud-based ETL platform that ingests data from multiple SaaS applications and databases and moves it into data warehouses and data lakes. There, it’s analyzed using BI tools. It’s an easy-to-set-up ETL tool with minimal requirements and effort—teams can quickly get their data projects off the ground and start moving data.

Stitch offers connectors for more than 100 databases and SaaS integrations, including data warehouses, data sources, and data lake destinations. Plus, users have the flexibility to build and add new data sources to Stitch.

Top use case:

Stitch is simple and easy to use, making it a great option for both DataOps teams and non-engineering teams like marketing. Users manage their ETL system from their UI easily. Stitch’s broad range of integrations makes for a suitable ETL tool for enterprises that need to ingest data from multiple sources.

Pros:

Easy-to-use and quick setup for non-technical teams
Scheduling feature loads tables on predefined time
Allows users to add new data sources by themselves
In-app chat support to all customers and phone support is available for enterprise users
Comprehensive documentation and support SLAs are available

Cons:

Lacks data transformation options
Large datasets may impact performance
No option to use or deploy services on-premise

Pricing:

Stitch offers a 14-day free trial and custom paid plans that depend on your scale.

Shipyard Data Orchestration

Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. It gives data engineers the tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds (without the headache).

Shipyard’s integration with GitHub offers continuous version control, easy deployments, and up-to-date code. Shipyard also offers reliable monitoring with instant notifications to ensure that you can identify and fix critical data pipeline issues before they impact your business. Its integration with dozens of data sources lets you easily create data pipelines.

Top Shipyard use case:

Shipyard gives you data pipeline flexibility and scalability. It’s a powerful data pipeline tool that aligns data teams and ensures they can scale and customize their data pipelines. Shipyard has a long list of integrations, super-easy data transformations, visual interface, and customer support, making it one of our favorite tools for data orchestration. Of course, we’re biased. Let's walk through the features we offer to see for yourself.

Pros:

Simple and intuitive UI makes it easy for experienced and new users to adopt the tool
Build advanced workflow automations with low-code templates and visual interface or fully code using Python, Bash, or Node
Integrates with a variety of data sources—e.g., Snowflake, Fivetran, dbt Cloud, Airtable, Amazon S3, spreadsheets, and more
Robust reporting capabilities to track inefficiencies, update processes, or make improvements instantly
Real-time notifications about critical breakages
Secure data handling with zero data loss
Modify your data pipelines with new logic immediately and scale as your data load grows
Deployment takes days, not weeks or months, and is flexible enough for the most technical data engineers to less technical data analysts

Cons:

Limited integrations for loading data from SaaS to Database

Pricing:

Shipyard offers a free plan for users to test out the platform.
Basic plan starts at $50/month and works on a pay-per-use model. Price varies by usage. You can calculate custom pricing for your team here.

Any of these data pipeline tools might be the solution you need—it just depends on your current data infrastructure and business needs.

How to choose the right data pipeline tool for your organization

Once you have clear requirements for your business, it’s much easier to choose the right data pipeline tool. Take time to consider this short list and map them to your organizational capabilities to choose the right one:

Needs of the organization: It's important to choose a data pipeline solution that meets your organization's specific needs.
Type of data being processed: Data pipeline solutions come in a variety of shapes and sizes, and some are better suited for certain types of data than others.
Amount of data being processed: Data pipeline solutions need to handle the volume of data with which your organization is dealing.
Level of data science expertise required: Some data pipeline solutions require more technical expertise than others. Organizations must ensure they have the resources necessary to use the solution effectively.

Ready to see a data pipeline tool in action?

Get started right now

We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems. If you want to see a data pipeline tool for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required.

Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.