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Top 15 Data Engineering Tools to Use in 2024

Top 15 Data Engineering Tools to Use in 2024

The data engineers construct, monitor, and improve sophisticated data models to help organizations enhance their business outcomes by leveraging the power of data.

In order to run this data-driven world, specialized technologies are needed. Consequently, it is vital to know about the different Data Engineering tools required for the same.

In this blog, we will learn about the most popular Data Engineering tools used today and their characteristics.

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What is Data Engineering?

All the organizations in the world have huge quantities of data. This data, if not worked upon and analyzed, does not amount to anything. Data engineers are the ones who make this data worthy of consideration. 

Data Engineering can be termed as the process of developing, operating, and maintaining software systems that collect, analyze, and store the organization’s data. In order to support current data analytics, data engineers create data pipelines, which are essentially the infrastructure architecture.

Data Engineering makes use of a wide variety of languages and tools to accomplish its objectives. These tools allow data engineers to implement tasks like creating pipelines and algorithms in a much easier and more efficient manner.

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Best Data Engineering Tools in 2024

Sit tight as we navigate through the best Data Engineering tools that are used today and see how each one differs from the rest.

1. Python

  • One of the first languages that comes to mind when you think of Data Engineering, is Python.
  • Python is a widely used programming language. It is an object-oriented, high level and easy-to-learn language, preferred by a lot of developers. It is generally used for the development of software and applications.
  • Python is considered the principal programming language when it comes to solving complex data science problems as well as when building machine learning algorithms.
  • Python is used by data engineers to program ETL frameworks, API interfaces, automation, and data munging operations including reshaping, aggregating, merging different sources, etc.
  • It is an extremely easy language to use, has a lot of third-party libraries, and helps in decreasing the development time, which makes it a must-know programming language in the field of Data Engineering.

2. Apache Spark

Apache Spark
  • Apache Spark is an amazing tool for stream processing of data.
  • You can query continuous data streams in real-time using stream processing, including data from IoT devices, financial trade data, user activity on websites, sensor data, and more.
  • It is open-source and one of the fastest tools for the management of data.
  • Apache Spark is one of the best tools for Data Engineering due to its ability to handle and analyze large data sets so efficiently.
  • Apache Spark supports graph processing.
    • It is highly flexible and can easily manage both structured and unstructured data.

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3. Airflow

  • It has become a challenge today to manage the data and make use of it to its full potential. Airflow helps in this case.
  • Apache Airflow is a management platform wherein users can design and implement data pipeline tasks and schedules.
  • It tracks the progress and helps in troubleshooting the issues.
  • This Data Engineering tool makes the workflow easier.
  • Apache Airflow helps in automating repetitive tasks. This makes things relatively easier and smoother for the IT departments.
  • In addition, Airflow can be used to minimize the data silos.

4. Snowflake

  • Snowflake’s ability to store and compute data, makes it one of the leading Data Engineering tools.
  • It is a cloud-based program that provides a variety of tools for data engineers, such as cloning tools, computing tools, and data storage tools.
  • Snowflake is the perfect platform for data warehousing, data lakes, Data Engineering, data science, and creating data applications since its data workloads scale independently of one another.
  • One prominent feature of Snowflake that makes it such a great tool is its shared data architecture.
  • Snowflake can be used to integrate both structured and semi-structured data, without the need for other tools such as Hive.
    • It is highly scalable and offers notable security features.
  • It supports an automated query optimization system. This way the users do not have to worry about managing the settings themselves.

5. Apache Hive

Apache Hive
  • Another important tool for Data Engineering is Apache Hive.
  • It is built on top of Apache Hadoop.
  • It acts as a data warehouse and management tool.
  • Hive provides an interface similar to SQL for querying data held in a variety of Hadoop-integrated databases and file systems.
  • Because its interface and structure resemble that of SQL, it is easy for users with basic knowledge of SQL to use Apache Hive.
  • The query language that is supported by Apache Hive is HiveQL. HiveQl is used to convert SQL-like queries into MapReduce jobs. This is then used for the deployment on Hadoop.
  • Three main functions that are performed by Apache Hive can be:
    • Data Query
    • Data Summarization
    • Data Analysis

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6. Tableau

  • Tableau is one of the most popular as well as the oldest Data Engineering tool.
  • Tableau supports a drag-and-drop interface. Using this tool, data engineers can easily create dashboards by gathering data from several different sources.
  • Data engineers can also use Tableau for compiling data reports.
  • It is compatible with both structures as well as unstructured data.
  • Tableau is a data visualization tool. It is highly interactive and offers amazing visualization features to data engineers. Because of this, users can build visually appealing dashboards in no time.
  • The reason for Tableau’s popularity is that it is an extremely easy tool to use. It provides a great user experience and anyone can use the tool, even without having any coding or technical knowledge.
  • An important feature of Tableau is its ability to easily handle and work with large datasets, without affecting performance or speed.
  • Tableau supports various languages.
  • It can also be known as a Business Intelligence that enables business teams to make data-driven decisions and performs functions such as:
    • Data modeling
    • Building live dashboards
    • Assembling data reports

7. Apache Cassandra

Apache Cassandra
  • Apache Cassandra is a NoSQL database solution.
  • It is an open source and is a schema-free database.
  • To use Cassandra, the user needs to be familiar with its architecture.
  • It enables the user to simultaneously scale and handle data from many sources.
  • It is highly scalable. The clusters in Apache Cassandra can be easily scaled up or down as and when required.
  • In addition to that, Cassandra is also fault-tolerant.
  • Apache Cassandra is a preferable tool for data engineers if they want to achieve scalable and efficient data analysis.

8. Microsoft Power BI

Microsoft Power BI
  • Microsoft Power BI is yet another great tool used by data engineers.
  • Its main aim is to provide users with a way to create simple data reports for analysis.
  • Power BI may be used to build business dashboards and share data insights within an organization by data engineers and business analysts.
  • When processing data sets to create live dashboards and analysis findings, data engineers use Power BI to create dynamic representations.
  • Another feature of Power BI that makes it so favorable is that it is extremely cost-effective. It supports a free version for users that enables them to create reports and dashboards on their systems.
  • It is an easy-to-use tool, wherein users are able to effortlessly create graphs, charts, tables, etc., without having any prior experience in Business Intelligence.

9. Amazon Redshift

Amazon Redshift
  • Amazon Redshift stands tall as one of the leading data warehousing solutions available in 2024.
  • It easily adjusts to changing data needs with resizable clusters, ensuring top-notch performance as your data grows.
  • It effortlessly connects with various data sources and other AWS services, offering flexibility for diverse platforms.
  • It ensures data integrity with solid encryption and smooth access controls, meeting compliance standards.
  • The pay-as-you-go pricing model and efficient resource utilization make it budget-friendly.
  • It supports complex queries and integrates with machine learning and business intelligence tools for useful data analysis.

10. BigQuery

  • BigQuery, a Google Cloud data warehouse, is a powerful tool for managing and analyzing large datasets.
  • Its architecture enables fast query execution, allowing users to gain information from massive datasets in seconds.
  • With a serverless infrastructure, users can focus on analysis rather than infrastructure management, saving time and resources.
  • It easily integrates with various Google Cloud services and other tools, offering an extensive ecosystem for data analytics.
  • Its intuitive interface makes it accessible even for non-technical users, reducing the learning curve significantly.
  • Pay-as-you-go and the ability to query data without the need for massive initial investments make it budget-friendly.


  • MATLAB is a powerful tool that intersects data engineering and analysis, offering an ideal environment for numerical computing and data visualization.
  • Its environment supports the development of complex algorithms, helping in data modeling, simulation, and optimization.
  • MATLAB’s visualization capabilities allow users to create helpful graphs, charts, and plots, enhancing data representation for better understanding.
  • It effortlessly integrates with various data sources, making it versatile for different types of data engineering tasks.
  • MATLAB finds applications across different fields of engineering, scientific research, finance, and higher education, helping in data-driven decision-making and research.

12. MongoDB

  • MongoDB stands out as a leading NoSQL database, known for its flexibility and scalability in managing diverse data types.
  • MongoDB stores data in flexible, JSON-like documents, making it easier to handle changing models and complex structures.
  • Its distributed architecture allows smooth scaling horizontally, accommodating data growth without sacrificing performance.
  • With features like splitting and efficient indexing, MongoDB delivers high performance even with large-scale data operations.
  • Its intuitive interface and support for various programming languages make it accessible to developers.

13. Amazon Kafka

Amazon Kafka
  • Amazon Managed Streaming for Apache Kafka (Amazon MSK) offers a powerful and scalable solution for handling real-time data streams.
  • Amazon Kafka scales smoothly to handle varying workloads and data throughput, ensuring efficient processing of streaming data.
  • It ensures data durability by replicating data across multiple nodes, reducing the risk of data loss in case of failures.
  • Its architecture allows for real-time processing and analysis of streaming data, enabling immediate insights and actions.
  • Amazon Kafka easily integrates with other AWS services, facilitating smooth data transfer and compatibility across the AWS ecosystem.

14. Amazon Athena

Amazon Athena
  • Amazon Athena, an interactive query service, allows for querying data in Amazon S3 using SQL without requiring a complex infrastructure setup.
  • Amazon Athena operates on a pay-per-query model with no infrastructure to manage, allowing users to run ad-hoc queries on data stored in Amazon S3.
  • It supports various file formats like CSV, JSON, and Parquet, making it versatile for different types of data stored in S3.
  • Athena scales automatically to handle large datasets, ensuring quick and efficient query processing.
  • Seamlessly integrates with other AWS services, allowing for easy data transfer and analysis within the AWS ecosystem.

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15. Apache Hadoop

Apache Hadoop
  • Apache Hadoop remains a foundational tool in the field of big data, offering an efficient structure for distributed storage and processing of large datasets across groups of computers.
  • Hadoop’s HDFS (Hadoop Distributed File System) breaks data into blocks and distributes them across multiple nodes, ensuring fault tolerance and scalability.
  • Hadoop’s ability to scale horizontally allows for smooth expansion to handle growing data volumes and processing requirements.
  • Hadoop’s open-source nature eliminates heavy licensing fees, making it a cost-effective solution for handling big data workloads.
  • It supports diverse data types and formats, accommodating structured and unstructured data, and offering flexibility in data processing.

Wondering how to prepare for a Data Engineering interview?? Refer to these Data Engineer Interview Questions For Experienced.


It is known that the contemporary world is a data-driven one, where there is a huge demand for Data Engineers, and for handling this data, specific tools are required.

Data engineers use a broad range of tools in order to process the data and prepare a strong architecture that lays the foundation for the success of businesses.

For anyone aspiring to become a prosperous data engineer, mastering the above-mentioned data engineering tools will provide a competitive edge.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.