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.
Table of Content
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.
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 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.
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.
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5. 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
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 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 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 is one of the data warehousing solutions from the top in 2025.
- It easily adjusts to changing data needs with resizable clusters, ensuring top-notch performance as your data grows.
- It seamlessly integrates with a variety of data sources and other AWS services, allowing for flexibility across different platforms.
- It offers data integrity through strong encryption and smooth access controls, with all compliance standards.
- The pay-as-you-go pricing model and efficient resource utilization make it budget-friendly.
- It supports complex queries and easily interoperates with machine learning and business intelligence tools for useful analysis of data.
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10. BigQuery
- BigQuery is a powerful data warehouse from Google Cloud that oversees and analyzes huge data sets.
- Its architecture thus supports fast query execution, allowing individuals to extract information from even massive datasets in mere seconds.
- With a serverless infrastructure, users can focus on analysis rather than infrastructure management, saving time and resources.
- It easily integrates with a wide variety of Google Cloud services and third-party tools, giving it a humongous universe of data analytics.
- Its intuitive interface makes it highly accessible to even the non-technical user, which minimizes the learning curve.
- Being in a pay-as-you-go model with the opportunity of querying data without large, upfront investments, it’s pretty budget-friendly.
11. MATLAB
- MATLAB is an enormously powerful tool that combines data engineering and analysis, thereby offering an ideal environment for numerical computing and data visualization.
- The environment enables the generation of complex algorithms and supports data modeling, simulation, and optimization.
- MATLAB’s visualization capabilities enable users to craft useful graphs, charts, and plots, thereby enhancing the representation of data for improved comprehension.
- It so combines lots of sources of data, thus making it ideal for numerous data engineering tasks.
- MATLAB is used in all fields, from engineering and scientific research to finance and higher education, for data-driven decision-making and exploration.
12. MongoDB
- MongoDB is amongst the most popular NoSQL database, famous for its ability to handle all types of data in flexible and highly scalable ways.
- MongoDB stores the data in flexible, JSON-like documents that might make it easier to handle evolving models and structures.
- Its distributed architecture enables smooth horizontal scaling, with data growth that does not compromise on performance.
- MongoDB provides remarkable performance, even with the most demanding data operations, by using features like data splitting and efficient indexing.
- Intuitive by nature, it supports many languages so is accessible to developers also.
13. Amazon Kafka
- Amazon Managed Streaming for Apache Kafka (Amazon MSK) that provides a robust and scalable solution for managing real-time data streams.
- Amazon Kafka naturally adjusts to accommodate fluctuating workloads and data throughput; it ensures the effective handling of streaming data.
- It ensures data durability by duplicating it on multiple nodes, which reduces the risk of loss in case of failures.
- Its architecture allows for real-time processing and analysis of streaming data, enabling immediate insights and actions.
- Amazon Kafka seamlessly integrates with other AWS services, ensuring smooth data transfer and compatibility throughout the AWS ecosystem.
14. Amazon Athena
- Amazon Athena is an interactive service for querying data in S3 using SQL without building any complex infrastructure.
- It does not need any infrastructure and is a pay-per-query basis. Therefore, they can run ad-hoc queries on data stored in Amazon S3.
- It supports multiple file formats, including CSV, JSON, and Parquet files, making it very flexible for the different types of data stored in S3.
- Athens adapts automatically to deal with heavy data, thus providing quicker and efficient query processes.
- It seamlessly integrates with other AWS services, facilitating effortless data transfer and analysis within the AWS ecosystem.
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15. Apache Hadoop
- Apache Hadoop is one of the foundational tools in big data, providing an efficient structure for distributed storage and processing of large datasets across groups of computers.
- Hadoop’s HDFS (Hadoop Distributed File System) divides data into blocks and disperses them across multiple nodes, which provides fault tolerance and scalability.
- Hadoop scales horizontally, making it easy to expand to keep up with the growing amount of data and processing demands.
- Hadoop is open source and does not incur the cost of heavy licensing fees.
- It supports a wide range of data types and formats, allowing both structured and unstructured data and flexibility in data processing.
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Conclusion
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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