Top 10 data Engineering Tools to Use in 2021

Data engineering is the process of collecting and preparing data for practical applications and analysis. It’s a technique used to modify raw data into valuable information. Data engineers typically collaborate with data scientists and analysts to manage and optimize the flow of data from one point to another.

In a modern system architecture, many applications require data input to function. And as the number of applications increases, the complexity of managing and organizing data also grows. This makes the process of manually managing and engineering datasets quite tedious and time-consuming.

Data engineers use special tools called data engineering tools to handle all data. These tools help automate and simplify development, allowing engineers to work on algorithms and data pipelines. They also reduce development time and are a cost-effective way to generate analytics.

Important Data Engineering Tools Used in the Market Today

Here are some of the most popular data engineering tools engineers use today to manage data and work on ETL pipelines.

1. Tableau

Tableau is one of the best data visualization and business intelligence tools in the market today. It helps engineers understand, visualize, and organize data for end-user consumption. It’s extremely easy to understand, and many major companies worldwide use it.

Tableau converts numerical and textual information to visual dashboards allowing you to comprehend data and generate insights. It also enables you to discover patterns in your data. In addition, it’s a low code tool and can easily integrate with existing architectures and applications. 

2. Python

Python is one of the most popular scripting languages worldwide. It’s heavily used for machine learning and data science projects but it can serve a lot of other purposes. Many organizations prefer using it to their counterparts for data engineering projects. Python is very user-friendly and helps companies reduce development time.

Data engineers use Python to code APIs, automate pipelines, and work on ETL frameworks. They also use it for reshaping datasets from different data sources through joins and aggregates. Furthermore, Python has many libraries for statistical modeling.

3. Amazon Redshift

Redshift is a cloud-based data warehousing and management tool that leverages Amazon Web Services (AWS) to analyze and store data. It uses SQL to manipulate semi-structured and structured data across operational databases, data warehouses, and data lakes.

It allows data scientists to query and integrate new data sources into their datasets, including live data. You can also use it with Sagemaker to create and deploy ML models and optimize your business intelligence. It also connects with Tableau for data visualization. 


MATLAB is a complex programming and computing environment for data visualization, mathematical modeling, and statistical computing. Many organizations worldwide use MATLAB to design algorithms, analyze industrial data, and develop organizational systems. It also boasts features to work on advanced machine learning, deep learning, computer vision, and big data projects.

MATLAB is easy to use and contains add-on toolboxes with hundreds of built-in functions. You can also create custom functions/algorithms for your project. It also helps you automate tests through test harnesses. 

5. Apache Kafka

Apache Kafka is an open-source, event-streaming platform with several applications, such as messaging, data streaming, and data synchronization. Companies use Kafka to manage their real-time data flow, collect metrics, and monitor logs.

Many engineers prefer Apache Kafka due to its ability to handle massive data volume. According to its official website, 80% of Fortune 100 companies use it to manage their data flow. That’s because it can be easily implemented onto client ecosystems and has many out-of-the-box features for scalability and performance.

The user community is large and eager to help while there are multiple online resources to help any kind of project move forward. 

6. D3.js

D3.js is a non-proprietary JavaScript tool that allows engineers to manipulate data and develop custom visualizations through a web browser. It uses CSS, Scalable Vector Graphics (SVG), and HTML to document data and create visualizations.

It uses a document object model (DOM) for data-driven manipulation and transformation. Through this, you can perform qualitative analysis, organize nodes, and create animated transitions for large datasets. 

7. SQL

SQL (Structured Query Language) is a time-tested, domain-specific query language that allows you to handle data schemas and execute queries on relational databases. You can use it to manipulate, modify, update, and insert data in structured datasets. 

SQL can understand and manage datasets, and integrate with scripting languages. It uses standard operations such as select, update, alter, and delete. As a result, it’s easy to learn and implement. You can also set up triggers and transaction controls for datasets, along with stored procedures and views.

8. Apache Cassandra

Apache Cassandra is an open-source, NoSQL database used for managing huge amounts of data. It promises high scalability and availability without compromising performance. Many companies use it due to its low latency and distributed nature.

You can also use Cassandra to create a custom data infrastructure that can handle scalability requirements. In addition, it’s highly fault-tolerant and ensures data accuracy and reliability.

Cassandra works on wide-column stores, and tables can be updated without dropping queries. However, you can’t perform joins and subqueries.

9. Apache Hadoop

Hadoop is a collection of open-source tools and libraries for handling big data. It provides a distributed computing option for big data on computer clusters. It also provides detailed analytics and real-time data processing information to its users.

Hadoop uses MapReduce programming for parallel processing and has different development modules such as yarn and HDFS. It can work on both unstructured and structured data.  You can also perform machine learning operations on Hadoop using Apache Mahout.  

10. MongoDB

MongoDB is a highly flexible and easy-to-use NoSQL database. It’s one of the most popular tools in the market, and many companies use MongoDB to cleanse and analyze data.

MongoDB is document-oriented and works on key-value stores. It allows you to query both structured and unstructured datasets. In addition, it’s highly efficient at storing and processing large volumes of data due to its MapReduce functions.


These are some of the most popular data engineering tools available in the market. Each of these tools has its own pros and cons. It’s a data engineer’s responsibility to understand the available choices and select the best tool for their business. They should also consider factors such as ease of implementation, use cases, and organizational standards while making their decision.