9 Must-Have Skills to Become a Data Engineer!
Overview
- Know which are the top 9 skills required to be a data engineer
- Find suitable resources to learn about these tools
- By no means is this list exhaustive. Feel free to add more in the comments
Introduction
Data Engineering is one of the fastest-growing fields with a heterogeneity of job opportunities. From Google, Facebook, Quora, Twitter, Zomato everybody is generating data at an unprecedented pace and scale right now. Organizations with such a large amount of data are trying to tackle this data by rapidly adopting Big Data Technology so that this data can be stored properly and efficiently and used when needed.
Data Engineers are responsible for storing, pre-processing, and making this data usable for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.
According to the report by datanami, the demand for data engineers is up by 50% in 2020 and there is a massive shortage of skilled data engineers right now. So, in this article, I am mentioning 9 skills that you will require to become a successful data engineer and a few resources to start with.
Table of contents
Top 9 Skills to Become a Data Engineer
- Programming Languages
- SQL Databases
- NoSQL Databases
- Apache Airflow
- Apache Spark
- ELK Stack
- Hadoop Ecosystem
- Apache Kafka
- Amazon Redshift
Programming Languages
Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it. You will be required to code the ETL process and build data pipelines.
The following programming languages are the most popular among data engineers:
Python: It is one of the easiest to learn a programming language and has the richest library. I have found Python to be a lot easier to perform machine learning tasks, web scraping, pre-process big data using spark, and also is the default language of airflow.
If you want to learn Python, here is a great free course you can refer to:
Scala: When it comes to data engineering, the spark is one of the most widely used tools and it is written as Scala. Scala is an extension of the Java language. If you are working on a Spark project and want to get the maximum out of the spark framework, Scala is the language you should learn. Some of the spark APIs like GraphX is only available in the Scala language.
Here are some of the recommended resources to get started with:
SQL Databases
You can’t get away from learning about databases when you are aspiring to become a data engineer. In fact, we need to become quite familiar with how to handle databases, how to quickly execute queries, etc. as professionals. There’s just no way around it!
SQL databases are relational databases that store data in multiple related tables SQL is a must-have skill for every data professional. Whether you are a data engineer, a Business Intelligence Professional, or a data scientist – you will need Structured Query Language (SQL) in your day to day work.
You should know
- How to insert, update, and delete records from your database?
- How to create reports and perform basic analysis using SQL’s aggregate functions?
- How to perform efficient joins to fetch your data from multiple tables?
Want to get answers to all these questions? Here are some of the best resources to clear your doubts –
- Structured Query Language (SQL) for Data Science
- 8 SQL Techniques to Perform Data Analysis for Analytics and Data Science
- 42 Questions on SQL for all aspiring Data Scientists
NoSQL Databases
The sheer fact that more than 8,500 Tweets and 900 photos on Instagram are uploaded in just one second blows my mind. we are generating data at an unprecedented pace and scale right now in the form of text, images, logs, videos, etc.
To handle this much amount of data, we need a more advanced database system that can run multiple nodes and can store as well as query a huge amount of data. Now, there are multiple types of NoSQL databases, some of them are highly available and some of them are highly consistent. Some are column-based, some are document-based and some are graph-based.
As a data engineer, you should know, how to select the appropriate database for your use-case and how to write optimized queries for these databases. Here are some of the resources that will help you get started with the NoSQL databases.
- A Beginner’s Guide to CAP Theorem for Data Engineering
- 5 Popular NoSQL Databases Every Data Science Professional Should Know About
Apache Airflow
Automation of work plays a key role in any industry and it is one of the quickest ways to reach functional efficiency. Apache Airflow is a must-have tool to automate some tasks so that we do not end in the loop of manually doing the same things again and again.
Mostly data engineers have to deal with different workflows like collecting data from multiple databases, pre-process it, and upload it. Consequently, it would be great if our daily tasks just automatically trigger on defined time, and all the processes get executed in order. Apache Airflow is one such tool that can be very helpful for you. Whether you are Data Scientist, Data Engineer, or Software Engineer you will definitely find this tool useful.
You can deep dive into some of these concepts with these clear articles and their examples –
- Data Engineering 101 – Getting Started with Apache Airflow
- Data Engineering 101 – Getting Started with Python Operator in Apache Airflow
Apache Spark
It is the most effective data processing framework in enterprises today. It’s true that the cost of Spark is high as it requires a lot of RAM for in-memory computation but is still a hot favorite among Data Scientists and Big Data Engineers.
Organizations that typically relied on Map Reduce-like frameworks are now shifting to the Apache Spark framework. Spark not only performs in-memory computing but it’s 100 times faster than Map Reduce frameworks like Hadoop.
It provides support in multiple languages like R, Python, Java & Scala. It also provides a framework to process structures data, streaming data, graph data. You can also train machine learning models on big data and create ML pipelines.
If you want to learn spark, here are some resources you can refer to –
- PySpark for Beginners – Take your First Steps into Big Data Analytics
- How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark
- Build Machine Learning Pipelines using PySpark
ELK Stack
It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana.
ElasticSearch: It is another kind of NoSQL database. It allows you to store, search, and analyze a big volume of data. If the full-text search is a part of your use case, ElasticSearch will be the best fit for your tech stack. It even allows search with fuzzy matching.
Logstash: It is a data collection pipeline tool. It can collect data from almost every resource and makes it available for further use.
Kibana: It is a data visualization tool that can be used to visualize the elasticsearch documents in a variety of charts, tables, and maps.
More than 3000 companies are using ELK stack in their tech stack, including Slack, Udemy, Medium, and Stackoverflow. Here are some of the free resources from where you can start learning ELK Stack.
- Hands-on tutorial to perform Data Exploration using Elastic Search and Kibana
- Getting Started with Logstash
- Getting Started with Kibana
Hadoop Ecosystem
Hadoop is a complete eco-system of open source projects that provide us the framework to deal with big data.
We know that we are generating data at a ferocious pace and in all kinds of formats is what we call today as Big data. But it is not feasible storing this data on the traditional systems that we have been using for over 40 years. To handle this massive data we need a much more complex framework consisting of not just one, but multiple components handling different operations.
We refer to this framework as Hadoop and together with all its components, we call it the Hadoop Ecosystem. Here are some of the free resources to know more about Hadoop.
- What is Hadoop? – Simplified!
- Introduction to the Hadoop Ecosystem for Big Data and Data Engineering
Apache Kafka
Tracking, analyzing, and processing real-time data has become a necessity for many businesses these days. Needless to say, handling streaming data sets is becoming one of the most crucial and sought skills for Data Engineers and Scientists.
We know that some insights are more valuable just after an event happened and they tend to lose their value with time. Think of any sporting event for example – we want to see instant analysis, instant statistical insights, to truly enjoy the game at that moment, right?
For example, let’s say you’re watching a thrilling tennis match between Roger Federer v Novak Djokovic.
The game is tied at 2 sets all and you want to understand the percentages of serves Federer has returned on his backhand as compared to his career average. Would it make sense to see that a few days later or at that moment before the deciding set begins?
Kafka is a much-needed skill in the industry and will help you land your next data engineer role if you can master it. Here are some of the resources that you can refer to:
- Say Hello World to Event Streaming
- A Metaphorical Introduction to Event Streaming for Data Scientists and Data Engineers
Amazon Redshift
AWS is Amazon’s cloud computing platform. It has the largest market share of any cloud platform. Redshift is a data warehouse system, a relational database designed for query and analysis. You can query petabytes of structured and semi-structured data easily with Redshift.
Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Most of the data engineering job descriptions specifically list Redshift as a requirement.
Here are some of the resources to get started with the Amazon Redshift.
Frequently Asked Questions
A. Yes, data engineering typically requires coding skills. Data engineers work with large datasets and are responsible for designing, implementing, and managing data pipelines, data infrastructure, and data workflows. They need to be proficient in programming languages like Python, SQL, or Scala, as well as have a strong understanding of data manipulation, data storage systems, and distributed computing frameworks.
A. The roles of a data engineer typically include:
1. Data Pipeline Design and Development: Designing and building robust data pipelines to efficiently extract, transform, and load (ETL) data from various sources into data storage systems.
2. Data Infrastructure Management: Managing and optimizing data storage systems, such as databases, data lakes, and data warehouses, to ensure scalability, reliability, and performance.
3. Data Integration: Integrating and harmonizing data from different sources to create a unified and consistent view of data for analysis and decision-making.
4. Data Quality Assurance: Implementing data validation and quality checks to ensure data accuracy, completeness, and consistency.
5. Data Transformation and Modeling: Transforming raw data into structured and usable formats suitable for analysis, and creating data models to facilitate efficient data querying and retrieval.
6. Performance Tuning: Optimizing data processing and query performance by fine-tuning the underlying infrastructure, indexing strategies, and data partitioning techniques.
7. Data Governance and Security: Ensuring data privacy, security, and compliance with relevant regulations and policies, and establishing data governance frameworks.
8. Collaboration with Data Scientists and Analysts: Collaborating with data scientists and analysts to understand their data requirements, provide them with clean and reliable data, and support their analytical workflows.
9. Monitoring and Maintenance: Monitoring data pipelines and systems, identifying and resolving issues or bottlenecks, and performing routine maintenance and upgrades.
Data engineers play a crucial role in managing the data lifecycle, enabling data-driven decision-making, and ensuring the availability and reliability of high-quality data for an organization.
End Notes
It is exciting to be a data engineer. A lot of advancements await in the future. In this article, we discussed the 9 most important skills needed to become a successful data engineer.
I recommend you go through the above mentioned resources to build the said skills to be a professional data engineer.
Do you have any other skills that you wish were on this list to become a data engineer? Let me know in the comments!
