It’s natural to feel overwhelmed when looking into the countless data engineering tools on the market. Some are free, while others vary in their price points depending on available features.

Fortunately, you don’t have to try every single tool out there. To help make the right decision, we have curated this list to familiarize you with the top 10 data engineering tools and how they’re used.

What Are Data Engineering Tools?

Data engineers transform raw data into useful information. But as large datasets grow in volume and applications continue to increase in complexity, manually engineering and managing datasets to create complex models is no longer an option. Data engineering tools are specialized apps that simplify and automate the process of building data pipelines and developing working algorithms.

10 Essential Data Engineering Tools and Steps To Use Them

Even the most skilled data engineering teams need specialized tools. Often, those are software or programming languages that allow data engineers to organize, manipulate, and analyze large datasets. But there isn’t a one-size-fits-all tool—it’s best to utilize a tool that’s in sync with your goals.

1. Apache Kafka

Apache Kafka is mainly used for processing and building data pipelines in real-time. It’s mostly utilized in industries with a heavy and constant data flow that involves analyzing website activity, collecting metrics, and monitoring log files.

Kafka’s ability to handle massive volumes of data stream non-stop is the reason a lot of app and website developers use it. The platform will most likely remain in use for years to come. While Apache Kafka isn’t easy to learn, it’s used by more than 30% of Fortune 500 companies, making it a great time and money investment for data engineers.

2. Apache Airflow

Apache Airflow is an open-source data engineering tool. The main advantage is its ability to manage complex workflows. Being open-source, Airflow is completely free to use and constantly receives community upgrades. With more than 8,000 companies using Airflow to some degree in their operations—like Airbnb, Slack, and Robinhood—it isn’t likely to be replaced.

Luckily, it’s extremely easy to use. To showcase your skills and abilities, you can build a smart ML model to transfer data and manage a fluctuating workflow.

3. Cloudera Data

Cloudera is a cloud-based platform for machine learning and data analytics. Cloudera Data in particular is popular among large-scale companies thanks to its dual nature, allowing data engineering and analytics teams to use the platform through the cloud and on-premise.

Cloudera has a user-friendly interface and a plethora of tutorials and documentation. It’s mostly used by financial institutions like the Bank of America and the Federal Reserve Bank.

4. Apache Hadoop

Hadoop, instead of being a single tool with a limited number of features, is a collection of open-source tools made to manage large-scale data often produced by large computer networks. What makes it a household name for many corporations is its ability to store data in an orderly manner, perform real-time data processing, and provide detailed and clean analytics.

While Hadoop’s dependence on SQL for its databases makes it easy for anyone with a background in SQL to break in, mastering the tool would require a lot of time and effort. Hadoop isn’t going anywhere soon, especially with companies like Netflix and Uber—alongside 60,000 others—showcasing why it’s an invaluable tool.

5. Apache Spark

Apache Spark is another open-source data engineering and analytics tool. While it doesn’t have a wide variety of features and capabilities, it’s one of the fastest data managing and stream processing frameworks. Spark can queue more than 100 tasks in-memory, leaving data scientists and engineers free to accomplish more critical tasks. It’s also compatible with numerous programming languages such as Python, Java, and Scala.

As long as you’re keeping your work simple, Apache Spark is easy to use and offers high-performance data processing in a variety of industries ranging from retail and finance to healthcare and media. 

However, for more complicated tasks, Spark can add an unnecessary layer of complexity and difficulty. Spark’s work model is still finding its way into a lot of useful ecosystems, such as Hadoop, and doesn’t seem to be going away anytime soon.

6. Amazon Redshift

Redshift is a cloud-based data warehousing and managing tool that takes advantage of Amazon Web Services (AWS) to the fullest. But instead of engineering the data to create new tools, Redshift is mainly an analytics tool that collects and segments datasets, looking for trends and anomalies, and produces insights.

While there’s a learning curve to using Redshift, it’s worth the trouble, as more than 10,000 companies use it for their data, including McDonald’s, Lyft, and Pfizer. Your best chance at showcasing your Redshift skills is by importing a rich set of data and analyzing it, using the tool for information.

7. Apache Cassandra

Cassandra is a scalable, NoSQL database that allows you to process data across multiple centers, both on-premise and on the cloud simultaneously. It’s a popular choice for many enterprise-level companies thanks to its speed and capacity alongside operational simplicity and continuous processing abilities.

While a ready-to-use Cassandra database is easy to use, to make the most of it, you need to understand the basics of Cassandra data architecture. That’s because Cassandra can be used to build custom data infrastructures that handle the average data influx and future scalability needs. It’s also worth noting that thousands of companies such as Staples and Zendesk use Apache Cassandra.

8. Apache Kudu

Apache Kudu is another free and open-source data management tool that’s compatible with the Apache Hadoop ecosystem. But mainly, it provides column-oriented data storage for fast analytics, thanks to its internally organized data structures.

Due to its lack of support for features that other tools include like foreign keys and multi-row transactions, Kudu might not be around for much longer. But since its features are numbered, Kudu doesn’t take a lot of time and effort to break into, making it the perfect data tool for a beginner data engineer or a professional data engineer looking to enrich their resume.

9. Apache Hive

Apache Hive is a data warehouse and management tool that’s an extension of Apache Hadoop. It works using a SQL-like mechanism and user interface that you can use for processing data queries and extracting analytics.

It’s mostly used by companies in the retail industry such as Walmart, Roku, and Nike, allowing them to store and keep track of numerous items online and offline in multiple locations. Similar to other Hadoop tools, Hive is easy to use and learn as long as you have a strong grip on SQL and MySQL.

10. Apache Turbine

Unlike the majority of tools on this list, Apache Turbine is Java-based, granting experienced developers the upper hand at using it. It’s mainly used for in-app UI and webpage design and development, making it a great option for online businesses and SaaS companies.

While Java experience is a plus, it’s not essential to working with Turbine. In fact, Turbine contains a lot of designs and templates, making it a popular option among non-programming designers, developers, and software engineers

However, while Turbine was heavily used in the early 2000s, it’s been steadily falling in popularity ever since due to various compatibility issues. It’s best to treat it as a side skill instead of something you rely on for a career.

Ready to switch careers to data engineering?

Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.

You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.

Check out Springboard’s Data Engineering Career Track to see if you qualify.