Key Data Engineering Skills, Tools and resources

Learn about data engineering, how it's used, common skills and careers that implement data engineering.


It's possible to develop the skills you need to get an entry-level role as a data engineer in a matter of months...

Getting a job doesn’t mean your learning should stop.

Top 9 Data Engineering Skills 

In data engineering, you’ll continue improving your skills.

>>> You should build these skills to become a successful data engineer:


>>> 01 Programming

Some popular programing languages in big data engineering are Python, Java, Scala, and Go.

>>> It is critical to learn all the basic and different data types.

If you are a beginner, we very much recommend learning Python.

It is flexible, and able to handle many data types.


>>> 02 Relational & Non-Relational Databases

A strong understanding of SQL and NoSQL databases is essential for working in data warehousing and data modeling.

>>> SQL Learning resource links in last slide

Learn database architectures and build a working knowledge of both relational and non-relational-databases.


>>> 03 Learn Regular Expressions (RegEx)

Learn to perform advanced data cleaning with regular expressions (RegEx) on datasets.

>>> It will surprise you by how much you can accomplish with as little as 15 mins a day.

Learn regular expressions to perform powerful string manipulation

Learn regex components like character classes and quantifiers

Learn character classes, quantifiers, positional anchors, capture groups and more


>>> 04 Learn ETL

ETL stands for "extract, transform, and load" and these are three unique processes.

>>Accuracy is absolutely critical in data engineering

Learn popular ETL tools like Xplenty, Stitch, Alooma, and Talend.


ETL allows data engineers to unify data from multiple databases and other sources into a single repository with data that is formatted and qualified for analysis.

>>> 05 Automation

Data engineers mostly dive into data and identify tasks for automation to eliminate manual participation.

>>Learn to write scripts to automate repetitive tasks.


Take a do-it-yourself approach by designing your own automation projects using free, open-source data sets.

Automation improves the quality of work, accelerates productivity, and increases decision-making agility.

>>> 06 Data Storage

Data Engineers build data storage and processing systems.

>>> Learn to determine when to use a data lake vs a data warehouse for designing data solutions


There are complex challenges, as we should store not all types of data in the same way.

Data engineers maintain data so that it is highly available and usable by other people to dig the actionable insights out of it.

>>> 07 Machine Learning and Algorithms

Data Scientists and ML Engineers work in close collaboration with Data Engineers.

>>> Learn the basics of Machine Learning and algorithms as you go along.

You don't need to work with ML models, but the data science and research teams rely heavily on the work of data engineering teams.

You will need to learn mathematics and ML algorithms to enable the work of multi-dimensional data in a dynamic environment.


>>> 08 Big Data Tools and Frameworks

Big data tools are evolving and some popular tools which you need to master are:

Apache Spark: Learn about Apache Spark — an open-source analytics engine for data processing.

Apache Hadoop: It is an open source framework used to store and process large datasets.

Apache Kafka: It is an open-source distributed event store and stream-processing platform.

Apache Flink: It is a big data processing tool, a distributed processing engine and a scalable data analytics framework.

>>> Learn Distributed File Systems like HDFS, Amazon EMR and AWS S3.


>>> 09 Cloud Computing

Data Engineers have an excellent working knowledge and rich experience with cloud platforms like Amazon Web Services, Azure, GCP and DigitalOcean.

>>> Learn basics of cloud data engineering


A Data Engineer needs to have a solid understanding of cloud computing and working knowledge of IaaS, PaaS, and SaaS implementations.

>>> Closing Notes

Data engineering is quite an advanced field and requires learning a lot of skills.

Since it is rather hard to find a university program that teaches data engineering, a better option is learning yourself via an online learning program that specializes in data engineering.

>>> learning resources (next slide) 

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