Data engineers are the unsung heroes of the data analytics industry. Their work is essential to the success of a company's data analytics efforts.

Data engineers build pipelines that help companies collect, merge, and transform data to facilitate seamless analytics. They're responsible for creating an infrastructure design that enables modern data analytics.

Data engineers' needs are divided into various sets of requirements that they must meet to build a pipeline. These requirements include collecting and merging data from numerous sources, transforming it into a format that other applications can use, and storing it in various forms so that the appropriate users can easily access it.

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What Is Data Engineering?

Data engineering is the process of extracting, transforming, and loading data into a data warehouse or data lake. Data engineering is typically performed by data scientists or engineers who are experts at using analytical tools to solve problems using big data

A data engineer may use various tools and technologies to extract data from multiple sources, including relational databases, NoSQL databases, log files, and other sources. The extracted data can then be transformed into a different format to be loaded into a database.

Top Data Engineering Tools

1. Python

Python has been gaining popularity as a language for data engineers because of its flexibility, ease of use, and ability to adapt to any situation.

Python also has built-in libraries that make it easy to write code with fewer lines than in other languages. It means less time writing code and more time focusing on the actual work of being a data engineer!

2. SQL

SQL stands for Structured Query Language. It is a language used to access relational databases. It is the most common, popular, and widely used language for managing data.

3. PostgreSQL

PostgreSQL is the most reliable, secure, and high-performance open-source relational database. It has all the features you need to do your job, focusing on data integrity, security, and performance.

It is one of the open-source databases that offer a full range of enterprise capabilities, including sophisticated authentication, replication, backup/restore, web client libraries, and language APIs.

4. MongoDB

MongoDB is a free, open-source database that makes it easy to build and scale applications in the cloud.

MongoDB automatically indexes and maps data, so you never have to tell it how to do that. It's built around JSON documents, so you can use it to store and query data using your favorite programming language. It's also incredibly fast—so fast that you can build apps without worrying about performance bottlenecks.

5. Apache Spark

Apache Spark is an open-source cluster computing framework designed to process big data. It's used by major companies and organizations worldwide, including Netflix, Spotify, and Yahoo!

Spark was designed to handle batch and stream processing methods and machine learning algorithms. It can run in Hadoop clusters or on its own.

It has a strong community behind it, and it's backed by major companies like Intel, IBM, and Microsoft, investing heavily in its development.

6. Apache Kafka

Apache Kafka is a technology that can help you to build a data pipeline that can handle massive amounts of data. Many financial companies and large corporations use it, but it's also an excellent fit for smaller businesses.

Kafka allows you to ingest and process any type of message in real-time. It stores messages in topics so they can be retrieved later, and it offers built-in high-availability features, so your data is always available when needed.

7. Amazon Redshift

Amazon Redshift is today's most powerful, scalable, cost-effective data warehouse solution. It's easy to use, fast and reliable.

With Amazon Redshift, you can analyze all your data from multiple sources in a single place. You can query hundreds of billions of rows in seconds with parallel SQL queries that simultaneously process data on all nodes. And you don't have to worry about backup and recovery because Redshift automatically manages your clusters for you.

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8. Snowflake

Snowflake is a cloud data warehouse that allows you to store, manage, and analyze all your data. You can set up clusters automatically and scale out or in at any time. Snowflake works with famous programming languages such as Javascript and Python, which makes it a very flexible product for engineers.

9. Amazon Athena

Amazon Athena is a fully-managed data service that allows users to query data in Amazon S3 using standard SQL. It's easy to use and offers a robust set of features that make it ideal for ad hoc analysis, interactive queries, and simple visualizations.

Athena is the best choice for anyone who wants to run SQL queries on their data stored in Amazon S3 without managing infrastructure or worrying about scaling up as requirements change.

10. Apache Airflow

Apache Airflow is a tool created to help you manage your data pipelines. A workflow scheduler makes building, monitoring, and optimizing data pipelines easy.

You can use Apache Airflow to perform any task you need to run repeatedly on large datasets. It includes functions like ETL, data analysis, and machine learning. You can also create more complex workflows than simple scripts or tasks (like webhooks).

11. Big Query

BigQuery is a powerful tool that lets you analyze massive datasets without worrying about the infrastructure.

BigQuery's speed and scalability make it ideal for leveraging machine learning and AI to extract insights from your data. You can also use it to store and query your data in real time, making it an excellent option for applications like ETL (Extract, Transform, Load) processes or real-time dashboards.

12. Tableau

Tableau is a powerful business intelligence tool that allows you to visualize the data in your organization.

The platform uses drag-and-drop features and a wide range of visualization options to create stunning, informative dashboards for teams across your organization. Tableau's intuitive interface and easy-to-use features make it an ideal choice for users new to data visualization, analytics, and data engineering.

13. Looker

Looker offers its users a variety of features that allow them to create reports with data visualization. LookML is an SQL-based analytics tool that displays dimensions, aggregates, and calculations in a database while allowing users to create visualizations and graphs for each data set. It will enable engineers to communicate and share information effectively with their coworkers and customers.

14. Apache Hive

Apache Hive is an open-source data warehouse software project developed by Facebook and Hortonworks. It provides a SQL-like language called HiveQL for querying data stored in Hadoop.

Hive enables users to query large datasets stored in HDFS using SQL. It can query and analyze data at any scale, from gigabytes to petabytes and beyond.

15. Segment

Segment is a tool for collecting and analyzing user data. The company collects data from users, translates it into actionable information, and stores the information in an automated man

It enables data engineers to use machine learning and data automation more efficiently in their processes.

16. Dbt

data is a data engineering tool that allows you to model, transform, and deploy your data warehouse. 

It provides a safe development environment for ETL (Extract, Transform and Load) tasks. You can use SQL to build models, test them, document them, and then deploy using Git. dbt promotes git-enabled version control and team collaboration.

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17. Redash

Data engineers can use Redash to query, visualize and share data from multiple sources. The tools and interface of this system provide communication and understanding of data across all levels and departments.

By creating an environment where everyone can access the correct information at the right time, Redash allows for more informed decisions. It ultimately leads to better business outcomes.

18. Fivetran

Fivetran is a data integration tool that allows you to consolidate your business processes and customer data collected from related applications, websites, and servers. The collected data can then be transferred to other analytics, marketing, and warehousing tools.

With Fivetran, Data engineers can transform your business processes and customer data collection into an efficient process by collecting all the information you need in one place. Then you can transfer that information to other analytics, marketing, and data warehousing tools.

19. Power BI

Power BI is a business analytics platform for data discovery, visualization, and reporting. Leading companies have used it worldwide to make better decisions, deliver faster insights, and optimize business performance. 

It helps you analyze data from any source—including SAP, Salesforce, SQL Server, Oracle Database, MongoDB, and other on-premises or cloud-based sources.

20. Periscope 

Periscope Data is a data analytics platform that helps you find insights into your data. With Periscope, you can effortlessly search and analyze your company's data, including customer information, employee information, and sales data. You can also use the platform to collaborate with your team on projects and get real-time updates on trends within your company.

21. Prefect

Prefect is a dataflow automation platform that helps you create, manage and run workflows. Prefect makes it easy to connect and manage your data so that you can focus on your business.

Prefect's workflow engine lets you define tasks and dependencies between them, then automatically executes the workflow based on events or triggers. It allows you to create custom workflows with no coding required quickly. 

22. Presto

Presto is a query engine that allows users to perform large-scale, distributed queries on the cloud. It's built on top of Apache Hadoop and uses SQL syntax to allow users to run queries on their data. Presto can perform complex queries, join multiple tables and files, and easily handle massive amounts of data—all while ensuring that your information remains secure.

23. Metabase

Metabase's BI tool allows you to connect all of your data—from any source—and make it easily accessible and understandable. You can create custom dashboards that pull in the data you need, allowing you to make informed decisions quickly and with confidence.

You'll also be able to use Metabase's visualization tools to create reports and charts that will help you communicate with stakeholders, investors, and anyone else who needs a clear picture of what's going on at your company.


1. What are data engineering tools?

Data engineering tools make tasks like building data pipelines and designing algorithms more efficient. These tools are the reason why the work of a data engineer is accessible during day-to-day tasks.

2. Who is a data engineer?

Data engineers are responsible for building systems that can handle large amounts of data to be used for analysis purposes. They work closely with software developers to create programs to manipulate and organize data for analysis. They also use their understanding of business needs to help develop solutions that meet those needs.

3. What are some of the standard tools used in data engineering?

Following are some of the most used data engineering tools:

  • Python.
  • SQL.
  • PostgreSQL. 
  • MongoDB.
  • Apache Spark.
  • Apache Kafka.
  • Amazon Redshift.
  • Snowflake.

4. Do data engineers use ETL?

ETL stands for Extract, Transform, Load. It is a software system that moves data from a source database to a target database in real-time or batches.

Data engineers are focused on building systems that use the data they access and are responsible for managing the data pipeline from beginning to end.

5. Is Python used in data engineering?

Yes, Python is used in data engineering.

Data engineering is a process that involves collecting and organizing data, as well as analyzing it to make decisions. Python can be used to perform all of these tasks.

6. How many ETL tools are there?

Striim, Matillion, AWS Glue, Panoply, Alooma, and Hevo Data are some of the top ETL tools to consider.

7. How is SQL used in data engineer?

Data engineers use SQL to retrieve data from databases, manipulate and transform it, and then store it back into a database. Data engineers use SQL to create scripts that perform these functions efficiently.

Data engineers use SQL to retrieve data from different databases because, unlike other languages such as Java or Python, SQL has built-in support for accessing multiple types of databases.

8. What is the best language for data engineering?

The best language for data engineering is Python.

Python is one of the most widely used languages in data science, but it's also great for data engineering. It has a wide range of libraries that can help you streamline your workflows and manage your data more efficiently. It's easy to read and understand, so it's a good choice if you want to share your code with others.

9. Is Kafka a data engineering tool?

Yes. Kafka is a data engineering tool because it manages the stream of data that flows through your system. It also helps you store and process data in real time.


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