In this article, we’ll look at some of the top data pipeline tools on the market and explore their benefits and specifications. Operating a healthy data pipeline is at the core of operating needs for businesses of all sizes.
Even if you’re a small, emerging company, having an effective process for storing, sorting, evaluating, and deploying new and existing data is paramount. Without the right tools and people to manage these tools, you won’t be able to run your business efficiently or effectively.
What are data pipeline tools?
To get real insights from your organization’s data, you need to extract data from multiple sources, clean and transform it, and load it into a single source of analysis—before anything else. Some things can go wrong: the code can yield errors, the data can go missing, or it could be loaded incorrectly, and so on.
Having a healthy data pipeline process ensures smooth delivery from origin to analysis. An efficient and secure data pipeline process guarantees consistent migration from various data sources to a destination like a data lake or data warehouse.
What are some of the best data pipeline tools on the market?
Today’s businesses have plenty of options for working with great data pipeline tools, and new software and updates are hitting the market every month. Here are a few of the best tools available.
Apache Airflow
Apache Airflow is a workflow management system originally designed by Airbnb which was open-sourced in early 2019. It’s designed to author, time, facilitate, and monitor data flow in an integrated format.
Airflow is written in Python, with workflows designed by Python scripts. While other “configuration as code” workflow platforms utilize languages like XML, developers can use Python to import their own libraries and classes to help them build their own workflows.
Apache Airflow can help author workflows as Directed Acyclic Graphs of project requirements. One can manage task scheduling in code form and break down data pipelines’ needs, status, logs, code, trigger tasks, etc.
Airbnb developed Airflow to author the company’s complex workflows, but it became an Apache Software Foundation Top-Level Project in early 2019 after being open-sourced at the same time Airbnb released it.
In summary, Apache Airflow is an open-source framework for programmatically creating and building automation for parallel and distributed workflows. Its focus on configuration as code has made it increasingly popular among developers. Apache Airflow’s top users often remark that it’s balanced, able to scale, versatile, and well-suited to handle the orchestration of complex business logic.
Apache Airflow is still in use by more than 10,000 organizations, including Applied Materials, Disney, and Airbnb itself. Amazon and Google offer AWS Managed Workflows on Apache Airflow as a managed service. Astronomer.io also offers managed Airflow services.
Astera Centerprise
If you’re looking for a fast, easy-to-use tool that can extract complex data from any cloud or on-premise source and load the transformed data into your destination system, you’ll love Astera Centerprise.
The software offers the ability to connect to REST APIs, which offers integration with cloud data sources without difficulty. It has built-in connectors for various systems, including SAP HANA, Snowflake, and several others. Its drag/drop mapping and point/click connection configuration make it easy to use for non-technical users.
Centerprise allows fast data processing for large data volumes and even quicker data-to-insights operations. The automating functions available in Centerprise — including things like job schedules and automation of workflows — ensure smooth execution of processes without any manual help.
Hevo Data
Hevo Data is an entirely self-managed, automation-ready data pipeline solution.
Hevo Data allows you to load data from other sources into sources such as Snowflake, Redshift, and others at real-time speed. It has pre-built integration options with over 100 data sources, covering data from SaaS programs, SDKs, Streaming, Cloud, and so on.
You can get going with this automated data solution at zero cost and rebuild all your data on a real-time scale, and have it ready as fast as you can say “pipeline.”
Keboola
Keboola, a global leader in data analytics software, has provided businesses with the best data solutions for over a decade.
The company provides versatile solutions for businesses, from small-scale operations to large corporations. It offers a wide range of products, including data management tools and solutions, business intelligence solutions, and data integration services.
Keboola’s platform allows you to automate your processes through its flexible flow of data solutions, which can help your business expand efficiently. The platform also provides granular control over every step in the ETL process that your business uses to develop opportunities.
With Keboola’s customizable solutions, Keboola also offers advanced security techniques for securing data and 130+ extractor components that automate data collection processes and accelerate overall performance levels within organizations.
Etleap
Etleap is a Redshift data pipeline tool that makes it easy for businesses to move data from detached sources to a Redshift data warehouse. It’s a cloud-based SaaS solution that takes the hassle out of setting up and maintaining your own pipeline.
Etleap can help you break down complex pipelines and make them easier to understand so you can derive advanced intelligence from your data. Etleap’s modeling feature can help you glean insights from your data without having to build custom analytics yourself!
Etleap simplifies complex pipelines by allowing users to add or modify sources with just one click. It also allows users to apply custom transformations. This makes it the perfect tool for organizations that generate large amounts of data and need more effective ways to use it for modeling, reporting, decision-making, and so on.
Segment
Segment is a tool for gathering customer data by keeping track of user events from business sites, mobile applications, and so on. It provides a complete, accessible data solution for multiple types of teams. This tool unifies multiple digital customer touchpoints across different channels to help you make sense of the customer journey and create more personal, customized customer interactions.
Segment has deep managing solutions to help businesses better sense customer data from various sources. It also helps accelerate A/B testing practices by analyzing data schemes for sales and support teams. It helps to increase efficiency in ads by analyzing data for sales and support teams and creating suggestions. Finally, it enables you to refine updates and let users share their feedback along the way.
AWS Glue
AWS Glue (formerly known as Amazon EMR’s Hive & Presto) is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. Using Glue is especially easy for those who already have some data pipeline built through AWS.
You can build an ETL job quickly if you understand how to navigate the AWS Console. Because AWS Glue is built in across most AWS functions, the onboarding process is easy. It natively supports data in Amazon S3 containers, Redshift, and all other RDS engines.
The integration options allow you to query and search your data by leveraging other services such as Amazon Elasticsearch Service, Amazon QuickSight, and others! Not only is it quite simple, but it’s also intelligent. All you have to do is put AWS Glue to your data stored on AWS and find it, understand it, and keep any associated metadata, such as the table definition/table schema.
Commonly asked questions
How often should I do data pulls?
The real answer to this is it depends on your needs. What does your business infrastructure demand? How long does it take to get your data and check for errors? Is it inefficient for you to pull data more than once a day or once an hour? How you answer questions like these determines how you use your data pipeline tools, and this means being aligned as an organization to understand how you’re using your data and how you need it. In some cases, you might need real-time data. In other cases you may not need data any more frequently than once every 48 hours.
What if my data is invalid?
If you’re uncertain about your data integrity, it’s important to check with your analytics teams or actuarial teams to make sure that figures and tables are operating smoothly and in the right way. A data pipeline tool isn’t a substitute for careful data analytics, and in many ways the integrity of your data pipeline completely depends on the integrity of your data itself.
How do I decide whether to do Table Pulls, Incremental, Total Extract, or Historical Updates?
Generally, data is usually extracted in a few ways.
You can use 100% table pulls, meaning you don’t need to know how data has changed. This is easiest, but can be expensive and consuming.
Incremental data loads are when data is purely appended. You can also do a historical merge, where you only pull newly inserted or updated data and in turn merge it with the old data.
Each of these methods have varying levels of difficulty; a complete table pull will be easier and a historical merge will be harder. In order to know which you should do, you will need to understand how your data is working inside your source system, how much data you have, and so on.
Is my pipeline self-managing?
The short answer is, no. You need someone to keep an eye on what’s going on. Pipelines don’t run perfectly, as they can sometimes crash or accommodate bad days. There needs to be a clear system of ownership and authority over the pipelines to make sure they’re running smoothly and to fix errors.
