The amount of data being produced through our internet activity is simply unbelievable. In 2010, we produced 2 zettabytes of data. By 2025, that number is expected to reach over 180 zettabytes.
To put that in perspective, a zettabyte is 1 billion gigabytes. If we thought of a gigabyte as a brick, we would have to build 258 Great Walls of China to create a zettabyte.
That’s a lot of data.
And while your business likely isn’t dealing with zettabytes, all the data coming in from your website, CRM, advertisements, social media channels, email marketing, and other parts of your digital marketing mix is still way more than a single person could sift through on their own.
Fortunately, as big data keeps on growing, data pipelines are becoming more sophisticated and capable of extracting, processing, and transforming data into actionable business intelligence.
Trust me, if you want to make the most out of your data, save a bunch of time, and make a whole lot more money, then you need to check out my review of the best data pipeline services below. Your bottom line will thank you.
What are the Best Data Pipeline Tools?
Here is my list of the best data pipeline tools on the market right now.
An enterprise-grade code-free data integration platform that processes the entire data pipeline from extraction to transformation and loading. (Free Trial Available).
Astera Centerprise is an easy-to-use tool that can extract data from any cloud or on-premise source, clean and transform it according to the business requirements, and load the transformed data into the destination system. The best part, you can do all of this within a single platform!
The software also has built-in connectors for various systems, including SAP HANA, Redshift, Snowflake, PowerBI, and more. Plus, it offers connectivity to REST APIs, enabling integration with cloud data sources without any hassle.
The drag-and-drop mapping and point-and-click connector configuration make the software easy to use for people who have no technical background. Moreover, the automation features available in Centerprise, like job scheduling and workflow automation, ensure smooth execution of processes without any manual intervention.
Through Centerprise, you can ensure fast data processing for large data volume and faster data-to-insights. You can start using this software with a 14-day free trial.
Pros of Astera Centerprise:
· Zero-code platform
· Drag-and-drop mapping
· Preview data before processing
· Parallel processing ETL engine
· Built-in job scheduler and monitor
· Data quality and cleansing features
· On-call support available
· Includes 50+ pre-built connectors
2. Hevo Data
A data pipeline as a service that requires no-coding skills (Starts from Free).
Hevo Data is an excellent data pipeline tool because it allows you to load data from other sources into your own data warehouse such as Snowflake, Redshift, BigQuery, etc. in real-time.
Out of the box, Hevo Data has pre-built integrations with over 100 data sources and these integrations cover data from sources related to SaaS applications, SDK’s, Streaming, Cloud Storage, Databases, etc.
You can get started using this fully managed, automated data pipeline solution for free and replicate all your data at scale, in real-time, and have it ready for analysis instantly.
Pros of Hevo Data:
- Easy to set up
- No-code platform
- Fully automated
- Schema management
- Zero maintenance
- Can scale with ease
- Has over 100+ connectors pre-built into the platform
3. Apache Spark
A unified analytics engine for large-scale data processing (Free).
Apache Spark is one of the top technologies you can use to build a real-time data pipeline. It’s an analytics engine designed specifically for larger-scale data processing.
The data pipeline tool performs processing tasks on huge sets of data before distributing it across various sources.
Data is distributed using the software’s own solutions or through collaboration with different distributed computing tools.
- Lightning Fast Solution: The software collects big data sets and then processes them through distribution to individual executors.
- Supports Multiple Languages: Apache Spark supports man languages via built-in APIs in Python, Scala, and Java.
- Robust Support: The platform supports SQL queries, streaming data, and machine learning.
Pros of Apache Spark:
- Free and open source software (FOSS)
- Allows flexibility in customization of functions or codes
- Vast community support on StackOverflow and other channels
- Supports graphics processing
- Software is fast and developer-friendly
Apache Spark is free and open-source software, which means that there are no vendor costs and no contractual obligations.
What Are People Saying?
- “We do use Apache Spark for cluster computing for our ETL environment, data and analytics as well as machine learning. It is mainly used by our data engineering team to support the entire Data Lake foundation. As we have huge amounts of information coming from multiple sources, we needed an effective cluster management system to handle capacity and deliver the performance and throughput we needed.” Trustradius Reviewer.
A platform for the entire data pipeline operational cycle (Starts from Free).
Keboola is a SaaS (software as a service) data operation platform. It covers the whole data pipeline operational cycle and provides a holistic data management platform – from ETL (Extract Transform Load), to orchestration, monitoring, and more.
The plug-and-play architecture allows for greater customization and the platform has advanced features such as machine learning features, one-click deployment of digital sandboxes, and much more.
- Complete Solution: Keboola provides complete solutions to help your business manage all its data.
- Granular Control: The platform gives you total control over every step in the ETL process that your business can use to develop opportunities.
- Customizable Solutions: One of the key features of the software is that it allows businesses to design workflows according to their needs
Pros of Keboola:
- Flexible flow of data solutions for effective business expansions
- Advanced security techniques for securing data
- 130+ extractor components for automating data collection
- Leaner data teams can cover more tasks
- One-stop shop for data extraction, modeling, and storage
You can try Keboola for free by signing up for a free trial on the website. You can pay as you go for the features you need, and if you require an enterprise plan, you can contact the website for pricing.
What Are People Saying?
- “Easy to use tool that integrates all functions of a standard data-stack. The thing I like the most about Keboola is the fact that just by signing in I can start doing my work even on a brand new client without worrying about installations, infrastructure discussions etc. Me and my team can be productive on day one.” Giuliano G. – CEO.
The perfect, analyst-friendly, and maintenance-free ETL solution.
Etleap is a Redshift data pipeline tool designed to make it easy for businesses to move data from disparate sources to a Redshift data warehouse.
Data analysts and engineers can add or modify data sources with a single click. They can also apply custom transformations in just a few clicks.
This is a cloud-based SaaS solution, which means no installation or maintenance, making this the perfect tool for organizations that generate huge amounts of data and are looking for more effective ways to leverage that data for modeling, reporting, and decision-making.
- Simplify Complex Pipelines: Etleap helps to break down complex data pipelines to make it easier for users to understand that data.
- Modeling Feature: The platform’s modeling feature allows users to glean advanced intelligence from their data.
- Effortless Integration: With this data pipeline tool, you get effortless integration for all your data sources.
Pros of Etleap:
- Strong security features and transformations
- Code-free transformations
- VPC offering
- Monitors collected data for businesses
- Free demo from a sales engineer
Etleap doesn’t disclose pricing, but you can sign up for a free trial after sitting for a demo with a sales engineer.
What Are People Saying?
- “With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.” Ben Fischer, Senior Director – BI and Strategy.
The leading customer data platform to collect, clean, and control customer data (Free).
Segment is a powerful data platform for collecting data about customers by tracking user events from business websites and mobile apps.
It provides a complete data solution for all types of teams in a business. This tool unifies all the digital customer touch points of a business across different channels to help you understand the customer journey and personalize customer interactions.
- Robust Data Management Solution: Segment has powerful management solutions to help businesses make better sense of customer data from various sources.
- Segment Persona: This feature helps to increase efficiency in ads by analyzing the data for sales and support teams.
- Accelerates A/B Test Practices: The platform also helps to refine updates and lets users share their feedback.
Pros of Segment:
- Retention Analysis feature to increase conversions
- “Destinations” feature for real-time updates on websites and apps
- Ability to archive and replay historical data on servers
- Provides solutions for complying with GDPR and the CCPA
- Offers a free plan for under 1,000 visitors/month
Segment offers a free plan where you can collect data from two sources, send data to unlimited destinations, and add up to 300 integrations. If you need to unlock more features, you can sign up for the Team or Business plans (pricing not provided).
What Are People Saying?
- As a business grows, it has become increasingly important to understand how online spend influences off-line behavior, which Facebook and Segment have made possible.” Micky Onvural – Co President, Bonobos.
A platform to help you unlock faster time to insight ($1/credit).
Fivetran automated data integration offers a fully managed ELT architecture for ready-to-query schemas and zero maintenance pipelines.
The platform was built to give analysts access to any data they need, at any given time.
Businesses can replicate applications a lot faster and maintain a high-performance cloud warehouse.
Data mappings make it easy for businesses to link their data sources with destinations. And that’s not even scratching the surface of what Fivetran can do.
- Robust Security: The platform has extensive security measures to keep your data pipeline safe and secure from prying eyes.
- Supports Event Data Flow: This feature is ideal for streaming services as well as unstructured data pipelines.
- Custom Code: Access your data using custom code, including Java, Python, etc. so you can build your connections.
Pros of Fivetran:
- Robust solutions with standardized schemas
- Automated data pipelines for easier focus on analysis
- Faster analysis of newly added sources for data
- Solution includes defined schemas and ERDs
- Easy data replication for businesses with no IT skill sets
Fivetran has flexible, consumption-based price models that scale with your needs. Plans start at $1/credit for the Starter plan and you only pay for the rows you consume.
What Are People Saying?
- “Fivetran is excellent for pulling data from troublesome APIs that are constantly changing into a staging database with minimal effort required. It allows us to bring in lots of data for our own customers without needing to build 100% custom connections for things that may only be used for a single client.” Alex D. – Product Manager.
A simple, extensible ETL built to enhance productivity for data teams ($100/month).
Stitch is a developer-focused platform designed to help you rapidly move data in your business.
This cloud-first platform is an ideal solution for businesses that want to increase their sales and customer databases as it can rapidly move data to analysts and various other teams within minutes.
The data pipeline tool connects sources like Mongol DB and MySQL. It also links other tools like Salesforce, Zendesk, and more to help with the replication of relevant data to warehouses.
- Secure Data: Stitch establishes a private network connection to a database to help secure data without any firewall infiltrations.
- Flexibility: Depending on your requirements, your business can configure the platform to route multiple data sources from a variety of destinations.
- Real-Time Evaluation: Stitch offers real time data pipelines evaluation of user experience, providing businesses with insight they can use to their benefit.
Pros of Stitch:
- Automate data ingestion
- Easy integration with various other sources
- Affordably priced with advanced features
- Easy replication of relational databases
- Simple, user-friendly UI
Whether you have analytics teams of one or 100, you can sign up for a free unlimited trial to try out Stitch for 14 days. After that, you can upgrade to the Standard plan which starts at $100/month for 5 million rows.
What Are People Saying?
- “With Stitch we spend more time surfacing valuable insights and less time managing the data pipeline.” Caitlin Moorman, Insights and Analytics Lead – Indiegogo.
A cloud-based platform for ETL – extract, transform, and load (Pricing not disclosed).
Xplenty is a scalable ETL platform to help businesses integrate and process their data, and prepare it for analytics.
The data pipeline tool gives businesses immediate access to multiple data sources and a large data set for them to analyze.
With this platform, businesses can load their data into the database and build pipelines, automate and transform the data to help analyze it.
- Simplified ETL Processes: One of the key features of this platform is that it uses low-code to simplify ETL and ELT processes.
- REST API Connector: This tool makes Xplenty agile for users connecting and extracting data.
- Robust Integrations: The platform offers 120+ integrations for different sources such as databases and BI servers
Pros of Xplenty:
- Dedicated and responsive customer service team
- 14-day free trial for businesses
- Beginner-friendly, no prior coding experience needed
Xplenty doesn’t disclose pricing, but the company does offer a free 7-day trial if you request a product demo.
What Are People Saying?
- “They really have provided an interface to this world of data transformation that works. It’s intuitive, it’s easy to deal with and when it gets a little too confusing for us, Xplenty’s customer support team will work for an entire day sometimes on just trying to help us solve our problem, and they never give up until it’s solved.” Dave Schuman – CTO and Co-Founder, Raise.me.
- Kafka: A leading technology that streams real time data pipelines.
- Storm: An open-source computational system for processing data streams.
- Airflow: A platform to programmatically author, schedule, and monitor workflows.
- AWS Glue: A fully managed extract, transform, and load (ETL) service.
- Data Build Tool: Anyone comfortable with SQL can own the entire data pipeline.
- Dataform: Dataform lets you manage all data operations in Panoply, Redshift, BigQuery.
- Matillion: Matillion ETL software is purpose-built for cloud data warehouses.
- Alteryx: Alteryx is a self-service data analytics platform with multiple products.
- Panoply: Panoply is a fully integrated data management platform.
Types of Data Pipeline Tools
There are different types of data pipeline tools, each with a different purpose. Listed below are some of the most popular types:
1. Open Source Data Pipeline Tools: Open source means that the underlying technology for the tool is available publicly and therefore requires customization for every use case. These types of tools are typically free of charge or offered at a very nominal price.
However, it also means that you need the expertise to develop the tool and extend its functionalities to fit your needs.
Examples of open-source tools include:
- Apache Airflow
- Apache Kafka
2. Proprietary Data Pipeline Tools: Unlike open-source, proprietary data pipeline tools are those that are tailored to suit specific business uses. They require no customizations or expertise for use and mostly have plug-and-play architecture.
- Hevo Data
- Fly Data
3. Batch Data Pipeline Tools: These types of tools let you move a large volume of data in batches or at regular intervals, which is at the expense of real-time operation. They can also be used in instances where there are limited resources and real-time processing of data can constrain regular business operation.
Examples of batch tools include:
- IBM InfoSphere DataStage
- Informatica PowerCenter
4. Real-Time Data Pipeline Tools: These types of tools process data in real-time and are ideal for teams that need analysis ready at their fingertips at all hours of the day. They are particularly useful for extracting data from streaming sources, such as user interactions that happen on a website or mobile application.
Examples of real-time data pipeline tools include:
- Hevo Data
5. On-Premise Data Pipeline Tools: When a business has its data stored on-premise, data lakes or a data warehouse also have to be set up in the same location. On-premise data pipeline tools offer enhanced security since they are deployed on the business’s local infrastructure.
Some of the top platforms that support on-premise data pipelines include:
- Informatica Powercenter
- Oracle Data Integrator
6. Cloud-Native Data Pipeline Tools: These types of tools allow businesses to transfer and process cloud-based data to warehouses that are hosted in the cloud. In this instance, the data pipeline is hosted by the vendor, allowing customers to save resources on infrastructure. This type of system focuses heavily on security, and examples of cloud-native platforms include:
- Hevo Data
What Features to Look For in Data Pipeline Tools
Every data pipeline service has certain nuances with regards to how it works. Though they’re similar to data integration tools, they’re slightly different. When evaluating which one to choose for your business, you must look at the criteria that are specific to your particular needs.
If you’re still unsure of which products to choose from, here is a list of the features to look for in when selecting a data pipeline tool.
1. Data Sources Supported: Choose a tool that will let you connect with numerous sources for your data. You also need to consider support for the various sources you might need in the future.
2. Easy Data Replication: Your chosen pipeline should make it easy for you to intuitively build your business pipeline and create your infrastructure in the shortest time possible.
3. Data Reliability: The tool should accurately transfer and load data with no errors or dropped packets.
4. Maintenance Overhead: Maintenance overhead must be minimal on your chosen platform and it should work straight out of the box.
5. Real-Time Data Availability: Consider your use case and decide whether or not you need real-time data or if batches of data will work just fine.
6. Pricing: Why pay premium prices when you can get things done for a nominal amount or even for free? Take the time to consider pricing options and choose a platform that makes the most budget sense for your business.
7. Customer Support: If you encounter issues while using the pipeline tool, you need to be able to get them resolved as quickly and as efficiently as possible. So make sure you choose a platform with a customer support team that is responsive and knowledgeable.
Data Pipeline vs. Data Warehouse
A common term used by data engineers and scientists is ETL, which stands for “Extract, Transform, Load.” Basically, this is the process of taking data from multiple sources and moving them to one location through a data pipeline, transforming it into a universal format, and loading it into a server in a data warehouse or other facility.
Thus, ETL connects a data pipeline and warehouse in a symbiotic manner that allows data to be transformed into actionable business intelligence. To make things simpler, let’s take at each part of this process:
- Extract Data: The first step to using a data pipeline is by extracting data from many sources. To extract the data means moving data from one source and prepare it to be moved through a data pipeline to another source.
- Transform Data: The incoming data must also be transformed in order to analyzed and used effectively. For example, comparing likes on a single Facebook post to the total traffic visited on your site would be difficult, if not impossible. Tus, to prepare data to be analyzed effectively, transformation of the data is required.
- Load Data: The transformed data can be loaded into a data warehouse where it can be subjected to analysis and methodological testing. Data scientists and engineers use this data to help marketers generate actionable business intelligence.
This explains how a data pipeline and warehouse work together. But they are still technically independent parts of a cohesive system. So let’s take a look at each in more detail:
How Do Data Pipeline Tools Work?
To understand the data pipeline process, simply visualize a pipe that receives information from one or more sources and then carries it to a specific destination.
The data “source” can include data from SaaS applications, relational databases, etc.
The “destination” could be a data warehouse, a Business Intelligence (BI) server, analytics application, or another place.
Depending on the destination and business use case, various things can be done to change the data along the way. These operations are called transformations and may include:
- Data standardization
- Verification, etc.
The ultimate goal of all this is to make it possible for businesses to easily visualize and analyze the data to gain helpful insights from it.
What Are Data Warehouses?
You can’t analyze data without having proper data storage. If you just extract and transform data, but don’t load it anywhere, then you can never access it. Thus, analyzing data requires a data warehouse or other similar facility.
A data warehouse includes rows of supercomputers that are constantly bringing in new data and storing it for data scientists to examine as needed. While smaller amounts of data could be stored within a single PC or external hard drive, these tools are not satisfactory for most business operations.
Larger and more extremely profitable businesses and enterprises often have their own on-premise data sources and warehouses. However, off-premise warehouses are also available in the cloud.
Storage space on a cloud data warehouse is purchased from a third-party vendor, which saves costs but also weakens the overall security of your data.
Data analytics can be performed on data stored within a warehouse. Analysis of these analytics will produce the actionable business intelligence you need to increase conversions and build brand ambassadors.
How Can Data Pipelines Help You?
You can’t have good analytics with bad data. A data pipeline helps your business create a clean and efficient ELT pipeline with accurate data so you can place your main focus on analytics to extract helpful insights from the data you’ve collected.
With the right tools, you no longer have to wonder whether or not your analysis is valid because of infrequently updated, poorly modeled, or missing data.
Choosing a data pipeline tool also saves you from having to build your own ELT pipeline from scratch – which is often a recipe for disaster.
Best Data Pipeline Tools— Summary and Top Picks
Some big companies like Netflix build their own data pipelines, but for emerging or non-technical businesses, you’ll likely need to use a third-party tool to help you create your own data pipeline.
Fortunately, you have a great list of the top 8+ data pipeline services that can help you to extra, transform, and load your data quickly and at a very low cost.
Here are my Top Picks:
- Hevo Data: A data pipeline as a service that requires no-coding skills
- Apache Spark: A free and open source software, this is one of the top technologies for building a real-time data pipeline.
- Fivetran: an automated data integration platform with a fully managed ELT architecture so you can focus on innovation and insights.
Now that you have made it this far, I’m sure that you have found a perfect tool to help you extract, transform, and load data.
Which data pipeline services do you prefer? What questions do you have? Let us know your thoughts in the comments below, and I’ll be sure to reply.