As the world goes gaga about data science, another related field is picking up a lot of attention: data engineering. A data engineer plays an important role in managing ETL processes, workflows, pipelines, and more.
Data engineers are increasingly in demand and offered attractive compensation packages because of the exciting and challenging work they get every day. Most data engineers earn well over six figures — and in some cities, senior data engineering positions can earn close to $200,000. Think of a data engineer’s job as somewhere between a data analyst and a data scientist.
But what exactly is data engineering, and what do data engineers do?
In this article, we take a closer look at the data engineering definition to figure out what it is, how it works, and why it is so important in today’s data-driven world.
Let’s start with the basics. How do we define data engineering?
What is Data Engineering?
Data engineering is a valuable field that deals with processing, storing, and delivering vast amounts of data. If data analysis is about modeling the data, and data science is about making decisions, data engineering enables these two fields with the necessary infrastructure.
A data engineer can build or design pipelines to transport, store, and transform data. The pipelines take data from various sources and store them in a single warehouse or a repository such as a data lake. The final role of a data engineer is to provide a robust and reliable infrastructure to support and verify big data.
Now, let’s take a closer look at the history of data engineering.
How Did Data Engineering Come About?
In its current iteration, data engineering has been around for about a decade. But some argue that data engineering has been a thing much longer, since ETL, databases, and SQL servers came into existence.
But how exactly did data engineering come about?
Have you ever heard of information engineering? This term described software engineering in data analysis and database design in the 1980s —the earliest form of data engineering.
But then, the internet rose in prominence in the 90s and the 00s. At that point, “big data” was starting to become a thing. IT professionals, SQL devs, and database admins existed, yet they weren’t called data engineers.
Then, in the early 2010s, Facebook and Airbnb started kicking around the term “data engineer.” As a result, data volumes massively escalated and became more varied.
Companies realized they were sitting on gold. The real-time data flowing through their systems had huge potential for better decisions and profits. Software engineers working for these data-driven companies then found themselves needing tools that could handle all of this data quickly, efficiently, and correctly. So they created them!
Now, data engineering is its own entity. It describes a type of software engineering that focuses thoroughly on gathering, managing, and storing data.
Why the Critical Need for Data Engineering Now?
There are a few reports floating around discussing shockingly high failure rates for big data projects. That’s no joke — one report by Gartner describes a failure rate of 85% for data projects in 2017 — massive. And one of the biggest reasons behind this was a lacking, unreliable data infrastructure. Much of the data companies had wasn’t trustworthy enough to base big decisions on.
But 2017 was five years ago, so maybe it’s since improved?
In 2019, IBM CTO for Data Science and AI Deborah Leff stated that 87% of organizations’ data science projects never quite make it to the production phase.
In the same year, Gartner predicted only 20% of data insights and analytics in 2023 would deliver the outcomes businesses seek. Other predictions included:
- By 2023, 90% of business strategies will mention information and data as critical assets and competencies.
- By 2023, data literacy will be a key driver of company value
Digital transformations continue to produce insurmountable quantities of data and new complex data types. Companies have known for a while that data scientists are key to interpreting and analyzing all this data. But these organizations didn’t realize right away that they needed data engineering — and data engineers — to ensure that the data is secure and reliable.
Back then, data scientists also performed the tasks and responsibilities of data engineers. However, it became increasingly clear that data scientists aren’t always equipped with the right skills and knowledge to handle data the right way. Thus, data became less reliable, and more data projects failed.
Today, data engineering is becoming increasingly vital as a foundational system for successful data science projects.
Where Does Data Engineering Fit in the Data Science Lifecycle?
Data engineering consists of the following steps:
- Data Collection: You can collectdata from logs, databases, external sources, user-generated content, sensors, instrumentation, and more.
- Movement and Storage of Data: This involves data flow, pipelines, storage of structured and unstructured data, ETL, and infrastructure.
- Data Preparation: Prep includes cleaning and processing to remove anomalies in data.
Why Does Data Need Processing Through Data Engineering?
You might be asking, “why data engineering for data processing?”
In the past, data engineers created data warehouses and organized data with indexes and data structures. These structures processed queries fast and provided adequate performance.
However, as big data and data engineering expand “data lakes” were on the rise. These lakes allow engineers to store all of their data in a centralized repository, even if the data isn’t structured. Thus, data lakes often have a wealth of unstructured and unformatted data mixed in.
Data engineering formats, structures, and “cleans” data to make it much easier and quicker to understand.
And because data is produced at a massive scale and an unthinkably rapid pace, data engineering algorithms are necessary for automating the processes involved in “cleaning” and preparing data for use in the data pipeline.
Data Engineering and Data Governance
Data governance comprises roles, processes, standards, policies, metrics, and standards to make sure data and information are used effectively and efficiently in helping organizations to achieve their goals.
Today, data governance is practically a requirement, especially in enterprise environments. Data governance maximizes data value, manages and mitigates risks, and even reduces costs.
But what does data governance have to do with data engineering
The answer is simple.
Data engineers are typically part of companies’ data governance strategies. This is because data engineers are usually responsible for implementing the data governance practices decided upon by data administrators.
What Do Data Engineers Do?
Earlier, a data scientist or analyst would have to write big SQL queries and use various tools to perform ETL. However, with the popularity of big data, the roles of data analysts, data engineers, and data scientists have further narrowed down.
Data engineers are key parts of a company’s data strategy, responsible for:
- Data acquisition, or gathering and collecting all the data available around a business
- Data cleaning, where data engineers locate any errors and anomalies
- Data conversion or transformation, where they convert all the data in a data lake or repository into a single, common format
Data engineers may also perform other tasks, such as disambiguation, where they interpret data. They remove any duplicated or redundant data.
A data engineer has expertise or experience in the following:
- Python and SQL
- Working with cloud services like AWS
- Java or Scala
- Understanding the differences between SQL and NoSQL databases
- Working with both SQL and NoSQL databases
- ETL tools like Informatica PowerCenter, Oracle Data Integrator, AWS Glue, etc
Since the responsibilities of a data engineer are clearly defined, a data scientist can focus more on the business aspects of the problems at hand. Most data engineers are technically sound and have at least 4/5 of the skills mentioned above.
What Skills Do Data Engineers Need?
Some companies do expect more from a data engineer. For example, the tech-giants Amazon, Facebook, and Google have much more data to work with, expecting the previously mentioned skills and the skills below:
- Experience with big data – Spark/Hadoop/Kafka
- Basic knowledge of data structures and distributed systems
- Understanding algorithms
- Knowledge of visualization tools like Excel, Tableau, or any other
Besides this, senior data engineers are expected to have some business intelligence experience and working knowledge of creating reports and dashboards.
Even with this clear distinction, some skills still overlap in the roles of data scientists and data engineers. Since data engineers are technical persons, they can often perform the tasks of a data scientist as well. However, the inverse is not always true.
Remember the two keywords for becoming a data engineer – “computer science” and “data.”
The tools and technical knowledge you need depend on the industry. For example, data engineering technologies like SQL, Sybase, Oracle, C++ and Java are more popular for financial services. In contrast, the consulting industry uses more modern tools like Hadoop, Spark, Java/Scala, and cloud platforms like AWS, Azure, or Google.
Here is all the technical knowledge you need to start a data engineer career:
1. Data Structures and Algorithms
Data structures are a set of methods that enable the organization and storage of data for easy access and manipulation of data. Some examples of data structures are ArrayList, LinkedList, queue, maps, trees, etc. Algorithms are sets of code written to solve a problem. Algorithms use data structures for faster data processing and problem-solving. Check out this list of cool data structures and algorithms tutorials.
SQL is the most critical skill, allowing you to better understand data. If you can write queries, you can fetch any kind of data from the database in minutes. As a data engineer, you should be able to create database schemas and tables, and perform operations like grouping, sorting, joins, ordering, and other data manipulations. SQL forms an essential step in preparing data for further analysis. Learn SQL through tutorials, or brush up with the SQL cheat sheet.
3. Python, Java
Java and Python are the two most popular languages used for data science. Python is popular for its rich set of libraries that can perform almost every statistical and mathematical operation and apply various algorithms without much need for actual coding. Python is also easy to learn and read. You can start learning Python through these free and paid tutorials.
Java is essential for big data processing. The Map-Reduce algorithm in Apache Hadoop uses Java. Java is easy; however, not as easy as Python. If you have some programming background, you can pick up Java easily; however, if you are new to programming, start with Python and then move to Java. Here are Java tutorials that will help you!
4. Big Data
Big data can store humongous amounts of data, both structured, semi-structured, and unstructured. The demand for data has increased due to data science and AI; hence big data tools and techniques have become more critical than ever. Learning big data will help you understand how data is stored, processed, cleaned, and information extracted from huge datasets. Big data works on three main concepts: Volume, Velocity, and Variety.
Many data processing frameworks help process huge datasets in no time and perform distributed computing either on their own or with other tools. Some popular frameworks are Apache Spark, Hadoop, and Apache Kafka. Checkout tutorials listed by Hackr.io for these 3:
5. Cloud Platforms
Cloud systems make resources available on-demand, accessible by any user over the internet. Businesses can dedicate efforts to core business use cases and issues and not bother about infrastructure or other IT issues.
Cloud systems are cheap and easily maintained. A cloud client can be a web browser, a mobile app, a terminal, etc. You might be familiar with these three services provided by a cloud platform:
- SaaS (Software as a Service): example, email, games, CRM, virtual desktop, etc.
- PaaS (Platform as a Service): example, database, a webserver
- IaaS (Infrastructure as a Service): e.g., servers, storage, virtual machines, network, etc.
Google, Microsoft, and Amazon are the three major cloud providers. Hackr.io has consolidated all the right tutorials in one place:
6. Distributed Systems
Distributed systems are a group of computers that work together but appear as a single computer for the end-user. Each computer is independent of the other; if one fails, it won’t impact the others. Additionally, distributed systems allow horizontal scaling, which enhances the overall performance and fault tolerance of the application. Learn more about distributed systems through this freecodecamp article.
7. Data Pipelines
Cloud platforms like AWS offer data pipelines, which is the core of data engineering. AWS data pipeline is simply a web service that can be used to automate the transformation and movement of data. A pipeline can schedule daily and weekly tasks and run them by creating separate instances. Learn about the AWS data pipeline.
Related: If you’d like to know more about starting a career in this field, check out our in-depth article on how to become a data engineer.
Data Engineering Versus Data Science: What’s the Difference?
Once upon a time, data scientists wore two hats — they performed data engineer and data scientist roles. However, over the years, data as a whole and as an industry has continued to grow and evolve.
One result of this evolution is that gathering and managing data has become much more complicated. Organizations have grown to expect more insights, answers, and actionable information from all analyzed data. So, we leave gathering and managing data to data engineering, and analysis to the data scientists.
In a way, data engineering and data science complement each other. Data scientists come to rely upon engineers in a way because data engineers gather and manage data that scientists then analyze, interpret, and report on. In simpler terms:
- Data engineers build, maintain, and optimize the systems that data scientists depend on to do their job. They create systems that gather, aggregate, store, and secure raw data from various sources. Data engineers also build the architecture and infrastructure necessary for data generation and provide easy access for real-time analyses.
- Data scientists use the data given to them by data engineers. They analyze, interpret, and find new conclusions and insights from the raw data, thus turning it into easily understandable information through methods like data visualization. Data scientists provide valuable insight that can help organizational or business leadership make better data-driven decisions. Some data scientists also work closely with machine learning engineers for the purpose of artificial intelligence and automation.
In conclusion, data engineering deals with processing, storing, and delivering vast amounts of data by creating infrastructure and data pipelines to store, transform, and transport this data. Data science uses scientific methods, algorithms, systems, and processes to analyze data and extract insights from it.
The demand for data engineers will only continue to grow, especially as their roles become more clearly defined.
Most real-time data is unstructured and needs a lot of processing to be useful, making data engineering a challenging field. But you can master data engineering with practice and knowledge of as many tools, algorithms, and data structures as possible.
As you move to senior levels, you can catch some AI aspects too, which are essential for data engineering. But if data engineering isn’t for you, consider the closely related data scientist career!
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