Data engineering is fast becoming one of the top in-demand tech roles across a wide range of industries worldwide. LinkedIn lists expertise in Artificial Intelligence as one of the jobs on the rise in 2021. The more companies see the need for harnessing cloud-based data, the more they need the expertise of data engineering consultants and teams in their organizations.

Every craft has special tools used for them. Just as a carpenter uses a hammer and nails and a doctor uses a stethoscope and other instruments, a data engineer has unique tools to build, integrate and design data systems. Data Engineers in the Data2Bots Academy and Data2Bots Talent Pipeline use these tools to build data pipeline infrastructures, develop algorithms, and produce data visualization reports.

In this article will discuss the 10 data engineering tools that are a must-have for every Data Engineer.

1. Python

Python is a popular programming language and one of the most important tools a data engineer can use. It is regarded as the most popular programming language compared to others. This makes it fundamental to Data Engineering. Python is a handy tool for data engineering because it is supported by three leading cloud computing platforms: Azure, AWS, and GCP.

This makes Python platform agnostic. It is also a straightforward language and has a high readability level. Python for Data engineering makes it easy to quickly process many data sets. Python works seamlessly with every other data engineering tool on this list and many more, which makes it indispensable to a data engineer.

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2. SQL

SQL, short for Structured Query Language, is a programming language that helps the data engineer build data structures in a relational database. With the aid of SQL, the data engineer can access and modify data and transform that data into information that shows trends and other important metrics that will be useful in making crucial decisions for the company.

3. Microsoft Power BI

Microsoft Power BI is a business intelligence platform that a data engineer can use to create data visualization reports. It’s a great platform to show the results from the data to non-tech people within the organization. With Microsoft Power BI, one can import data sets from Excel or JSON files, clean the data and make them tell a story using graphs and charts. Furthermore, it’s also reasonably easy to use.

4. Snowflake

Snowflake is a data warehouse solution that helps the data engineer build cloud-based data architecture. The platform offers a blend of shared disk architecture which uses a centralized data repository, and shared nothing database architecture which uses massively parallel architecture (MPP).

5. Apache Spark

Apache Spark is an open-source data processing and management tool. The data engineer needs to process much data faster and in real-time. A huge advantage of Apache Spark is its ability to keep up to 100 tasks in memory, which helps the data engineer save only what is necessary to disc and speed up data processing time. It also works well with Python.

6. Apache Hadoop

Apache Hadoop is a collection of open-source tools that can process large datasets across a large computer network. It’s a favorite of companies like Netflix and Uber.

7. Google BigQuery

BigQuery is a data warehousing platform by Google that helps data engineers and analysts efficiently analyze data for machine learning and business intelligence purposes. With BigQuery, the data engineer can use SQL to build the data architecture with its serverless, cloud-based solutions.

8. Amazon Athena

Amazon Athena is a cloud-based data warehousing solution that analyzes large datasets on the Amazon Simple Storage Service (Amazon S3). The data engineer uses SQL queries to run fast and complex tasks such as data storage, application hosting, data backup, and recovery.

Amazon Athena for data warehousing runs on a serverless infrastructure, so the data engineer does not need to spend time setting up and managing a server. It also integrates with Apache Spark to process information and schema across unstructured, structured, and semi-structured datasets.

9. Amazon Redshift

Amazon Redshift is another cloud-based data warehousing solution by AWS that analyses SQL queries through clusters in the form of tables. It’s used in conjunction with business intelligence tools to quickly visualize data. Amazon Redshift for data warehousing can integrate with various other tools like Microstrategy, Tableau, and SAS. It is excellent for a large set of structured data.

10. Tableau

Tableau is a data visualization software used to import and manage huge metadata, create no-code data queries, analyze data in real-time, and create easy-to-comprehend visualizations. Its versatility has made it a favorite for business intelligence processes.

Using Tableau for data visualization, the data engineer can extract data from various sources like PDF, AWS, Excel, etc. It is also easy to use, not requiring prior coding experience. It can access programming languages like Python, R, SAS, and more.

Final Words

There you have it! These 10 data engineering tools are vital in helping any data engineer transform raw, seemingly meaningless data into business solutions that can help an organization improve processes, reduce waste, improve sales, and many more benefits. At the Data2Bots Academy, we train our students with data engineering courses and help them move from zero technical skills to becoming data engineers by giving them in-depth knowledge of these tools and more. 

We aim to train at least 100 students in the School of Data Engineering. Register here for the early bird discount, and get a 25% discount to join the first cohort. We also upskill Data Engineers through the Data2Bots Talent Pipeline. They are shown how to harness these tools to build a data engineering roadmap for businesses and improve their expertise.