What Tools Do Data Engineers Use

Data has now become the powerhouse of every organization, irrespective of its domains. Thus, this significance has shed new light upon the profession of Data Engineers. Are you wondering who are Data Engineers and what tools do Data Engineers use?

Well, you’re exactly at the right place. Stay tuned. 

Data Engineers design, manage and maintain the information infrastructure. They are also responsible for developing data pipelines based on the ETL (Extract, Transform, and Load) model. 

To be honest; Data Engineers are versatile and can adapt and take on any role in the Data ecosystem. This versatility might help you get an idea about the scope of the Data Engineering field.

In that case, how do you know what tools do Data Engineers use? And which Data Engineering frameworks you need to acquaint with? 

Let’s dive in and explore some of the best Data Engineering tools use.

  1. Python

How do Data Engineers use Python?

Almost all Data Engineering job listings will have the requirement of Python programming.

Python is undoubtedly the programming language that most Data Engineers prefer. 

Data Engineers use Python to code ETL pipelines, integrate APIs, Automate Workflows and Data pre-processing. Python is easy to understand and a robust programming language, having many use cases.

Python has a simple syntax and minimizes the development time of a Data Engineer. If you plan on taking Data Engineering as a career, Python programming is an absolute must. 

  1. SQL

It wouldn’t prove wrong if we say that SQL is the essence of Data Engineering. Data Engineers use queries to insert, update, alter and manipulate the data in the Data architecture.

 They also use SQL to extract KPIs (Key performance indicators), develop models using business logic, and create reusable data structures.

 Data Engineers also have a good grasp of Advanced modeling SQL. It helps in modeling complex data transformations.

  1. Postgres SQL

Postgres is an open-source relational database popularly used for its extensive collection of built-in functions and user-defined functions. Furthermore, it is lightweight, flexible, and data engineers can use it with large datasets. 

Postgres SQL provides greater work agility. Data Engineers use Postgres to process complex queries and requests.

Moreover, it has high compliance with the SQL standards, and it extends multi-environment (Cloud or on-premises) support.

All these features make Postgres SQL a crucial tool when it comes to Data Engineering.

  1. MongoDB

Do you need to handle unstructured data?

 There’s nothing handier than the NoSQL Database, MongoDB. You don’t need to stick with rigid schemas anymore.

 MongoDB is a document-oriented NoSQL that offers distributed key-value store and MapReduce calculation features.

  1. Apache Spark and Hadoop

Often Data Engineers have to work with massive datasets on clusters of machines. In such a scenario, Apache Spark and Hadoop frameworks allow you to apply the power of multiple computers on a single computer to get the Data job done. 

Moreover, Apache Spark allows you to query the real-time data stream and supports multiple programming languages, including Java, Scala, Python, and R.

  1. Amazon Redshift or Big Query

Over 60% of Data Engineers use Amazon Redshift. Amazon Redshift is a completely managed cloud warehouse offered by Amazon. You can easily build your data warehouse and scale it according to your requirement. Big Query by Google is an alternative to Amazon Redshift.

  1. Apache Airflow

The best choice for the workflow management platform is Apache Airflow. It helps Data Engineers in easy scheduling and monitoring workflows using the Airflow user interface. It also helps data engineers to build modern data pipelines and streamline the workflow.

  1. HDFS and Amazon S3

Data Engineers prefer HDFS or Amazon S3 to store the Data when it is being processed. These frameworks are specialized file systems capable of storing any amount of Data. Furthermore, these frameworks are reasonably priced, integrate with the environment effortlessly, and help in managing data.

The field of Data Engineering is continuously evolving. Thus, new tools and frameworks may emerge in the future that may provide significant performance over current ones. As a Data Engineer, you need to adapt to these changes and upgrade yourself continuously.

How to master these tools and frameworks?

The beginning of any learning process is a cardinal phase, as it lays the foundation for the advanced concepts. We suggest you follow the Knowledge, Certification, and Expertise (KCE) process to learn how to use these tools. 

KCE Framework First, understand the application and use of each tool and framework. Then move on to certifications. Enroll in online courses and training and get certified.

Once you acquire theory knowledge about each tool, focus on the last step, Expertise. Apply what you’ve learned to real-life applications. Take up projects or internships and handle these tools and frameworks to master them.

If you google, ”What tools do Data Engineers use’‘, you will come across tons of tools and search results. Make sure it doesn’t intimidate you.

The tools and frameworks differ according to applications and the organization’s preference. To get started with Data Engineering, we have listed the most basic toolkit that you will need to use. 

Each tool listed here will have its own pro and con. As a Data engineer, you need to manage these cons and build efficient infrastructure that will require minimum modifications for years to come.