Top Data Engineering Tools and How to Use Them

 
When looking into the plethora of data engineering tools available, it’s normal to feel overwhelmed. Some of the data engineering tools are free, while others charge a fee based on the features they offer.

Now, you don’t have to try each and every tool available. We’ve compiled this list to familiarize you with the top data engineering tools and how they’re utilized to assist you in making the best decision.

Raw data is transformed meaningfully into information by data engineers. Developing complicated models by manually engineering and managing datasets is no longer an option. With massive datasets, applications tend to get more complex. Data engineering tools are specialized applications where you can create data pipelines and design workable algorithms easily and automate more tasks.

Even the most seasoned data engineering teams require specialized applications, especially in frequently used software or programming languages, to organize, manipulate, and analyze massive datasets. However, a one-size-fits-all tool does not exist; hence it’s ideal to use one that aligns with your objectives.

Apache Kafka

Apache Kafka is mostly used for real-time data processing and pipeline construction. It’s typically used in businesses with a lot of data flow, such as website activity analysis, metrics collection, and log file monitoring.

Many app and website developers utilize Kafka because of its capacity to manage enormous volumes of the data stream in a non-stop manner. The platform will almost certainly be used for many years to come. While Apache Kafka isn’t easy to understand, it’s employed by more than 30% of Fortune 500 firms, making it a worthwhile investment in terms of time and money for data engineers.

Apache Airflow

Apache Airflow is a data engineering tool that is open-source. The capacity to manage complex workflows is the key benefit. Because it is open-source, Airflow is absolutely free to use and is regularly updated by the community. Airflow is unlikely to be replaced because it is used by more than 8,000 businesses, including Airbnb, Slack, and Robinhood.

Fortunately, it’s really simple to use. You can create a clever ML model to transfer data and manage a fluctuating workflow to demonstrate your talents and abilities.

Cloudera

Cloudera is a machine learning and data analytics platform that runs in the cloud. Among large-scale companies, Cloudera Data, in particular, is quite popular due to its dual nature, which allows data engineering and analytics teams to use the platform both on-premise and in the cloud.

Cloudera has an easy-to-use interface as well as a wealth of tutorials and documentation. Financial institutions such as Bank of America and the Federal Reserve Bank primarily use it.

Apache Hadoop

Hadoop is a collection of open-source technologies designed to manage large-scale data created by big computer networks rather than a single tool with a limited set of functionality. Its capacity to store data in an orderly manner, execute real-time data processing, and give precise and clean analytics has made it a household name for many organizations.

While Hadoop’s reliance on SQL for its databases makes it simple to get started, mastering the technology takes significant time and effort. Hadoop isn’t going away anytime soon, especially with firms like Netflix and Uber, as well as 60,000 others, demonstrating its value.

Apache Spark

Another open-source data engineering and analytics tool are Apache Spark. While it lacks a wide range of features and capabilities, it is one of the most efficient data management and stream processing frameworks. Spark can queue up to 100 tasks in memory, freeing up data scientists and engineers to focus on more critical tasks. Also, Apache Spark is compatible with a wide range of programming languages, including Python, Java, and Scala.

Apache Spark is simple to use and offers high-performance data processing in a variety of industries, from education and finance to healthcare and more, as long as you keep your work simple.

Ready to boost your data engineering career?

Data engineering is currently one of the fastest-growing industries in technology. Data engineers enjoy a high level of job satisfaction, a variety of creative challenges, and the opportunity to work with ever-changing technologies. Clairvoyant now provides lucrative careers in this space.

You will be mentored one-on-one to learn key aspects of data engineering, such as designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools such as MapReduce, Apache Hadoop, and Spark.

About Deny Smith

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