What Tools Do Data Engineers Use?

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Data Engineers

Data engineering is one of the most sought-after career domains these days. Though the data engineer role is not as hyped as that of the data scientist, many surveys have found that there is, in fact, more demand for the former.

In line with this trend, many professionals are seeking data engineering courses online to build a strong foundation in this domain.

Further, those who want to learn the advanced concepts also pursue credentials like MongoDB certification to upskill themselves.

Many people can argue that online courses and certifications are not a necessity; one can do independent study as well.

Indeed, it is not compulsory, yet data engineering is quite a new subject for many professionals, and it is recommended that one starts learning the concepts under the guidance of an industry expert.

A number of skills are required to be developed for the role of a data engineer. Additionally, knowledge of data engineering tools is a must for those embarking on a career in this field.

With a number of tools available in the market, you may not know which one to start with. So, this article aims to make you familiar with the top tools that data engineers use in their day-to-day lives.

Table of Contents

Top Tools Used by Data Engineers

A number of tools come in handy for data engineers while performing their tasks. Here we have mentioned the most popular ones that even employers expect you to know. 

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Python

Python programming is one of the most important skills required for a data engineer. This programming language is quite useful for data pipelines and ETL (Extract, Transform, Load) tasks and to build robust, cost-efficient, and reliable data solutions.

Data engineers use Python when performing many tasks like acquiring data from various sources, processing data, and converting it into a usable format to be used by data scientists, business analysts, or others.

Python also has a rich set of libraries and packages that help in data engineering processes, namely SciPy, NumPy, Pandas, petl, beautiful soup, and pygrametl.

Apache Spark

As described on the official website, Apache Spark is a multi-language engine for executing data science, machine learning, and data engineering on single-node machines or clusters.

Whether you are comfortable using SQL, Python, Java, Scala, or R, you can easily unify the processing of your data in batches and real-time streaming with Spark.

No wonder several companies, including 80% of Fortune 500 companies, use Apache Spark for their data engineering tasks.

The tool is built on an advanced distributed SQL engine for large-scale data. One can also join the thriving open source community of Spark with contributors from across the world.  

MongoDB

MongoDB is a popular NoSQL database that supports rich and adaptable querying for diving into complex documents.

It is the data foundation for any industry and can handle many workloads like transactional, full-text search, time series, analytical, and more.

The tool promotes faster and more flexible application development with features like sharding, built-in replication, indexing, and performance tools.

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It has high flexibility for indexing data so as to accomplish the processing of huge amounts of data in very little time.

Data engineers also prefer using MongoDB as it stores multiple copies of data across different servers, enabling instant data recovery in case of a hardware failure.

Amazon Redshift

AWS is a renowned cloud services provider, and Amazon RedShift is its dedicated service for the data engineering category.

It accelerates your time too valuable insights with easy, fast, and secure cloud data warehousing at scale.

Data engineers use Redshift to analyze their data across operational databases, data lakes, data warehouses, and external data sets.

The platform uses SQL to analyze structured and semi-structured data using AWS-designated hardware and machine learning to offer optimum performance at any scale. It is one of the most widely used cloud data warehouses.

Snowflake

These are the phrases used to describe Snowflake in a few words – one platform, many workloads, no data silos. Using the tool, you get simple, reliable data pipelines in the language of your choice.

The Data Cloud offered by Snowflake involves several organizations that mobilize data seamlessly across public clouds as data providers, data consumers, and data service providers.

Data engineers rely on Snowflake to unify their data warehouses, data lakes, and other siloed data so as to comply with data privacy regulations like CCPA and GDPR.

The tool also allows you to develop new revenue streams based on data to help drive your business forward. 

Google Cloud BigQuery

BigQuery is a highly scalable, serverless, and cost-efficient multi-cloud data warehouse that helps organizations get answers to their business problems with no infrastructure management.

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With built-in features like geospatial analysis, machine learning, and business intelligence, BigQuery is your one-stop solution to analyze massive data and achieve business agility.

Data engineers use this tool to query streaming data in real-time and get the most up-to-date information on their business processes.

You can also use the robust security, reliability, and governance controls of BigQuery to protect your data.

Now that you are familiar with the tools, spend time learning more about them and start a rewarding career as a data engineer.  

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