Data Engineering Challenges | eSource Technology

It’s easy to overlook the amount of data that’s being generated every day — from your smartphone, your Zoom calls, to your Wi-Fi-connected dishwasher.

It is estimated that the world will have created and stored 200 Zettabytes of data by the year 2025. While storing this data is a challenge itself, it’s significantly more complex to derive value from this amount of data.

From 2020 to 2022, the total enterprise data volume will go from approximately one petabyte (PB) to 2.02 petabytes. This is a 42.2% average annual growth over these two years.

You’re likely familiar with the term “Big Data” — and the scale of this market is continuously growing. The big data analytics market is set to reach $103 billion by 2023, with poor data quality costing the US economy up to $3.1 trillion yearly. Fortune 1000 companies can gain more than $65 million additional net income, only by increasing their data accessibility by 10%.

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. In order to do this, businesses have to employ people with specific skill sets tailored to data governance and strategy, such as data engineers, data scientists, and machine learning engineers.

This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities. You’ll also walk away with a better understanding of the importance of data engineering and learn how to get started deriving more value from your data in 2022.

What is Data Engineering?

The key to understanding what data engineering lies in the “engineering” part. Engineers design and build things. “Data” engineers design and build pipelines that transform and transport data into a format wherein, by the time it reaches the Data Scientists or other end users, it is in a highly usable state. These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth.

Sounds simple enough but a lot of data literacy skills goes into this role. This is why Data Engineers are in such short supply and why there is confusion around the role. The figure below is one example of the activities involved in data engineering.

Overcoming Obstacles on the Way to State-of-the-Art Data Infrastructure

The first aspect of data engineering is related to software development as a whole. We’re talking about the intention of some development companies not to consider a real person who will use the app while designing it. Data architecture is a purely technical process that requires many ears to master. However, the person who will use the application may be even more important than the data engineer, despite the lack of tech expertise. Software is created to be used and not to just exist, and meeting users’ expectations must remain one of the major priorities.

Despite that data engineering is a part of the development process that is usually hidden from the user’s eyes, it’s the user who determines how the process must evolve. As a rule, if a person who uses the app works with easy-to-understand data, the underlying functionality requires significant effort. On the contrary, if software provides access to raw data, it’ll be easier for the data engineering team to make their job done. For example, data analysts can use such programming languages as R or Python to extract useful insights from raw data, while the company managers will feel comfortable with tables and graphs that BI solutions can provide.

What Skills do Data Engineers Need?

Data engineers must have specialized skills in creating software solutions around data. At the same time, it’s perhaps unrealistically expected that Data Engineers will be familiar with a breadth of tool and technologies – anywhere from 10 to 30 of them. And these tools are constantly changing. Furthermore, it varies by industry.

Some, such as SQL, have been around forever. Others such as Scala are falling out of favor over time. Still others such as AWS are in rapid ascent in terms of demand.

Jeff Hale, a published export author and instructor on data science and data engineering topics recently did an analysis of the most in-demand skills asked of Data Engineers on three job platforms. Below is his summary of the top 10 technology skills required.

This variety of skills needed and the complexity of some of them makes determining the right person for the job very very difficult.

The requirements to do the job of a data engineer have been accelerating over the last several years. That’s why we suggest, as, with data science, it’s best to think of a “Data Engineer” as a team of people with a portfolio of data engineering skills. Which ones you prioritize will depend on a lot of things.

With that said, important skill areas would be:

Foundation software engineering – Agile, devOps, architecture design, service oriented architecture.
Distributed systems – This would include software engineer skills and software architect skills.
Open Frameworks – Apache Spark, Hadoop, perhaps Hive, MapReduce, Kafka and others…
SQL – This is a database staple and remains that way.
Programming – Python has become the favored language for working with data. Java on the other hand, while still widely sought has fallen out of favor with most data scientists and engineers. Scala is another language that Apache Spark and Kafka are based on.
Pandas – a Python library for cleaning and manipulating data.
Visualization/dashboards
Cloud platforms – AWS is probably the most prevalent cloud skill set for Data Engineers to know. Google Cloud Data Engineering and Microsoft Azure are right behind.
Analytics – While mainly the realm of data scientists, statistical analysis skills or understanding of some of the different mathematical principles or probabilistic principles are necessary for being able to properly manipulate the data so that it is in a shape that is accessible for the people who are doing the end analysis on it.
Data modeling – Data modeling knowledge is quite important now in the sense that a Data Engineer needs to know how they are going to structure tables, partitions, where to normalize and denormalize data in the warehouse, etc. and how to think about retrieving certain attributes.

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