How to Become a Data Engineer in 2020?

Data engineers build massive reservoirs for data and are key in managing those reservoirs as well as the data churned out by our digital activities. They develop, construct, test, and maintain data-storing architecture — like databases and large-scale data processing systems. Much like constructing a physical building, a big data engineer installs continuous pipelines that run to and from huge pools of filtered information from which data scientists can pull relevant data sets for their analyses.

Data engineers typically have an undergraduate degree in math, science, or a business-related field. The expertise gained from this kind of degree allows them to use programming languages to mine and query data, and in some cases use big data SQL engines. Depending on their job or industry, most data engineers get their first entry-level job after earning their bachelor’s degrees. Here are five steps to keep in mind if you are planning on becoming a data engineer:

Most of us have an idea about who a data engineer is, but we are confused about the roles & responsibilities of Big Data Engineer. This ambiguity increases once we start mapping those roles & responsibilities with apt skill sets and finding the most effective and efficient learning path. But, don’t worry, you have landed at the right place. This “Big Data Engineer Skills” blog will help you understand the different responsibilities of a data engineer. Henceforward, I will map those responsibilities with proper skill set.

Let’s start by understanding who is a Data Engineer.

Who is a Data Engineer?

In simple words, Data Engineers are the ones who develops, constructs, tests & maintains the complete architecture of the large-scale processing system.

Next, let’s further drill down the job role of a Data Engineer.

What does a Data Engineer do?

The crucial tasks included in Data Engineer’s job role are:

• Designing, developing, constructing, installing, testing and maintaining the complete data management & processing systems.
• Building highly scalable, robust & fault-tolerant systems.
• Taking care of the complete ETL(Extract, Transform & Load) process.
• Ensuring architecture is planned in such a way that it meets all the business requirements.
• Discovering various opportunities for data acquisitions and exploring new ways of using existing data.
• Proposing ways to improve data quality, reliability & efficiency of the whole system.
• Creating a complete solution by integrating a variety of programming languages & tools together.
• Creating data models to reduce system complexity and hence increase efficiency & reduce cost.
• Deploying Disaster Recovery Techniques
• Introducing new data management tools & technologies into the existing system to make it more efficient.

Next, I would like to address a very common confusion i.e., the difference between the data & big data engineer.

Difference Between Data Engineer & Big Data Engineer

We are in the age of data revolution, where data is the fuel of the 21st century. Various data sources & numerous technologies have evolved over the last two decades, & the major ones are NoSQL databases & Big Data frameworks.

With the advent of Big Data in data management system, the Data Engineer now has to handle & manage Big Data, and their role has been upgraded to Big Data Engineer. Due to Big Data, the whole data management system is becoming more & more complex. So, now Big Data Engineer has to learn multiple Big Data frameworks & NoSQL databases, to create, design & manage the processing systems.

Advancing in this Big Data Engineer Skills blog, lets us know the responsibilities of a Big Data Engineer. This would help us to map the Data Engineer responsibilities with the required skill sets.

Summarizing the responsibilities of a Big Data Engineer:

• Design, create, build & maintain data pipelines
• Aggregate & Transform raw data coming from a variety of data sources to fulfill the functional & non-functional business needs
• Performance optimization: Automating processes, optimizing data delivery & re-designing the complete architecture to improve performance.
• Handling, transforming & managing Big Data using Big Data Frameworks & NoSQL databases.
• Building complete infrastructure to ingest, transform & store data for further analysis & business requirement. 

If you’ll look & compare different Big Data Data Engineer job descriptions, you’ll find most of the job description are based on modern tools & technologies. Moving ahead in this Big Data Engineer skills blog, let’s look at the required skills that will get you hired as a Big Data Engineer.

Big Data Engineer Skills: Required Skills To Become A Big Data Engineer

• Big Data Frameworks/Hadoop-based technologies: With the rise of Big Data in the early 21st century, a new framework was born. That is Hadoop! All thanks to Doug Cutting, for introducing a framework which not only stores Big Data in a distributed manner but also processes the data parallelly.

• Real-time processing Framework (Apache Spark): Real-time processing with quick actions is the need of the hour. Either it is a credit card fraud detection system or it is a recommendation system, each and every one of them needs real-time processing. It is very important for a Data Engineer to have knowledge of real-time processing framework. Apache Spark is a distributed real-time processing framework. It can be easily integrated with Hadoop leveraging

• Database architectures: One of the most prominent data sources are databases. It is critically important for a Data Engineer to understand database design & database architecture like 1-tier, 2-tier, 3-tier and n-tier. Data Models & Data Schema are also amongst the key skills which a Data Engineer should possess.

• SQL-based technologies (e.g. MySQL): Structured Query Language is used to structure, manipulate & manage data stored in databases. As Data Engineers work closely with the relational databases, they need to have a strong command on SQL. PL/SQL is also prominently used in the industry. PL/SQL provides procedural programming features on top of SQL.

• NoSQL technologies (e.g. Cassandra and MongoDB): As the requirements of organizations has grown beyond structured data, so NoSQL databases were introduced. It can store large volumes of structured, semi-structured & unstructured data with quick iteration and agile structure as per application requirements.
• Hence, for any data-driven organization, it is vital to employ data engineer to be on the top.

What does a data engineer do?

With the advent of “big data,” the area of responsibility has changed dramatically. If earlier these experts wrote large SQL queries and overtook data using tools such as Informatica ETL, Pentaho ETL, Talend, now the requirements for data engineers have advanced.

Most companies with open positions for the Data Engineer role have the following requirements:

• Excellent knowledge of SQL and Python
• Experience with cloud platforms, in particular, Amazon Web Services
• Preferred knowledge of Java / Scala
• Good understanding of SQL and NoSQL databases (data modeling, data warehousing)

Keep in mind, it’s only essentials. From this list, we can assume the data engineers are specialists from the field of software engineering and backend development.

For example, if a company starts generating a large amount of data from different sources, your task, as a Data Engineer, is to organize the collection of information, it’s processing and storage.

The list of tools used in this case may differ, everything depends on the volume of this data, the speed of their arrival and heterogeneity. Majority of companies have no big data at all, therefore, as a centralized repository, that is so-called Data Warehouse, you can use SQL database (PostgreSQL, MySQL, etc.) with a small number of scripts that drive data into the repository.

IT giants like Google, Amazon, Facebook or Dropbox have higher requirements:

• Knowledge of Python, Java or Scala
• Experience with big data: Hadoop, Spark, Kafka
• Knowledge of algorithms and data structures
• Understanding the basics of distributed systems
• Experience with data visualization tools like Tableau or ElasticSearch will be a big plus

That is, there is clearly a bias in the big data, namely their processing under high loads. These companies have increased requirements for system resiliency.

Big Data Tools

Here is a list of the most popular tools in the big data world:

• Apache Spark
• Apache Kafka
• Apache Hadoop (HDFS, HBase, Hive)
• Apache Cassandra

More information on big data building blocks you can find in this awesomeinteractive environment. The most popular tools are Spark and Kafka. They are definitely worth exploring, preferably understanding how they work from the inside. Jay Kreps (co-author Kafka) in 2013 published a monumental work of The Log: What every software engineer should know about real-time data’s unifying abstraction, core ideas from this boob, by the way, was used for the creation of Apache Kafka.

• An introduction to Hadoop can be A Complete Guide to Mastering Hadoop (free).
• The most comprehensive guide to Apache Spark for me is Spark: The Definitive Guide.

Cloud Platforms

Knowledge of at least one cloud platform is in the nest requirements for the position of Data Engineer. Employers give preference to Amazon Web Services, in the second place is the Google Cloud Platform, and ends with the top three Microsoft Azure leaders.

You should be well-oriented in Amazon EC2, AWS Lambda, Amazon S3, DynamoDB.

6. Distributed Systems

Working with big data implies the presence of clusters of independently working computers, the communication between which takes place over the network. The larger the cluster, the greater the likelihood of failure of its member nodes. To become a cool data expert, you need to understand the problems and existing solutions for distributed systems. This area is old and complex.

Andrew Tanenbaum is considered to be a pioneer in this realm. For those who don’t afraid theory, I recommend his book Distributed Systems, for beginners it may seem difficult, but it will really help you to brush your skills up.

I consider Designing Data-Intensive Applications from Martin Kleppmann to be the best introductory book. By the way, Martin has a wonderful blog. His work will help to systematize knowledge about building a modern infrastructure for storing and processing big data.

Data Pipelines

The Data Engineering Cookbook Mastering The Plumbing Of Data Science  Guide Book Please Download