Harnessing data can enable a lot of things, from personalizing marketing campaigns to powering self-driving cars. Data scientists are responsible for analyzing data and using it for various purposes. However, they need good quality data to accomplish complex tasks, such as forecasting trends for business. That's where data engineers come in. Data engineering is the science of collecting and validating information (data) such that data scientists can use it.
A data engineer, on average, can earn $117,000 a year. Sometimes, they can even earn as much as $160,000, a year. According to Dice, businesses are hungrier than ever to hire data engineers. In 2019, data engineering was the hottest tech job, with the number of open positions growing by 50%, year-on-year.
With an excellent pay scale and high demand, data engineering can be a lucrative career option. However, you might want to know the following before committing to a career as a data engineer:
Table of Contents:
- Data Engineering: What Are the Responsibilities?
- Data Engineers vs Data Scientists: What's the Difference?
- What Skills Should a Data Engineer Have?
- How Do I Learn to Be a Data Engineer?
- The Perfect ETL Tool for Data Engineers
Data Engineering: What Are the Responsibilities?
Data engineers set up and maintain the data infrastructures that support business information systems and applications. They might work with something small, like a relational database for a mom-and-pop business—or something big, like a petabyte-scale data lake for a Fortune 500 company.
As part of their responsibilities, data engineers design, build and install the data systems. These systems fuel machine learning and AI analytics. They also develop information processes for a whole host of data tasks. These include data acquisition, data transformation, and data modeling, among others.
Whether it's a one-person show or a larger team, the field of data engineering includes the following positions:
- The Data Architect: Data architects design data management systems for an entire organization, or specific parts of it. Their work allows data systems to ingest, integrate, and manage all the required sources of data for business insights and reporting. The work of a data architect may need in-depth knowledge of SQL, NoSQL, and XML, among other systems and tools.
- The Database Administrator: Database administrators help design and maintain database systems. They ensure that database systems function seamlessly for all users in an organization. Database administrators optimize databases for speed. They also ensure that updates don't interfere with workflow, and sensitive information is secure.
- The Data Engineer: Data engineers understand several programming languages used in data science. These include the likes of Java, Python, and R. They know the ins-and-outs of SQL and NoSQL database systems. They also understand how to use distributed systems such as Hadoop. Having such a wide expanse of knowledge allows them to work with data architects, database administrators, and data scientists. In fact, sometimes, they can perform all those roles themselves. Essentially, data engineers are responsible for building a robust, integrated data infrastructure for an organization.
Data Scientists vs Data Engineers: What's the Difference?
- Data scientists use statistical modeling and other tools to analyze data. Data scientists focus on building the required infrastructure for generating and preparing data for analysis
- Data scientists work closely with key decision-makers for carving out a data strategy. Data engineers work closely with data scientists to make high-quality data available to them
- Data scientists are responsible for generating insights. Data engineers are responsible for building and maintaining pipelines that feed data to the data scientists
Data scientists carry out many responsibilities in modern enterprises. For instance, helping Facebook show you targeted ads, teaching robotic vehicles to drive themselves, and helping Netflix recommend the perfect movies. Their work gives companies tremendous competitive advantages. For example, Netflix is saving $1 billion a year due to better customer retention through data analytics.
Data scientists specialize in statistical modeling and machine learning technology. They develop graphical displays, dashboards, and other methods to share vital business intelligence with decision-makers in an organization. However, every data scientist needs access to quality data, and hence, the need for data engineers.
Data engineers create data pipelines that connect data from one system to another. They are also responsible for transforming data from one format to another so that a data scientist can pull data from different systems for analysis. Even though data engineers aren't as visible as data scientists, they're just as important (if not more so), when it comes to data analysis.
As a simple analogy, if data scientists are train conductors, data engineers are the builders of the railway network that gets the trains from A to B.
Now, let's say the train conductor wants to deliver a payload somewhere that doesn't have an established railway line. The conductor needs the railway network builders to connect the train to the new destination. The railway builders architects will study the terrain. They'll decide if it's better to go around, over, or tunnel through any mountains in the way. They'll, probably, build bridges over rivers. They'll use all the tools available to them to build a railway line that connects the train to the new destination.
To put it simply, data scientists interact with data by writing queries. They are responsible for creating dashboards for insights and developing machine-learning strategies. They also work directly with decision-makers to understand their information needs and develop strategies for meeting these needs. Data engineers build and maintain the data infrastructures that connect an organization’s data ecosystems. These infrastructures make the data scientist's work possible.
What Skills Should a Data Engineer Have?
Data engineers need to acquire a variety of skills related to programming languages, databases, and operating systems. As a data engineer, it is important to keep in mind that you'll never feel like you know everything, but you will know "enough." More importantly, you'll know how to find information and acquire new skills when needed.
Ultimately, the acquisition of skills and knowledge is a career-long process. Yes, you'll need to be an expert in certain topics and programming languages (as your job requires). But you also need to be an expert at looking up information. For example, you might need an SQL statement to perform a specific action. SQLZoo might be a good place to look for that information. Similarly, you might need to brush up on MapReduce when analyzing a large data set featuring a parallel, distributed algorithm on a cluster.
Broadly speaking, here are 11 knowledge areas you'll develop during the course of your career as a data engineer:
1) Programming Languages Used in Data Science
Data engineers need expertise in the following programming languages as a bare minimum:
- SQL: To set up, query, and manage database systems. SQL is not a "data engineering" language per se, but data engineers will need to work with SQL databases frequently.
- Python: To create data pipelines, write ETL scripts, and to set up statistical models and perform analysis. Like R, this is an important language for data science and data engineering. It's particularly important for ETL, data analysis, and machine learning applications.
- R: To analyze data, and set up statistical models, dashboards, and visual displays. Like Python, this is an important language for data science and data engineering. It's especially useful for data analysis and machine learning applications.
Knowledge of these scripting languages allows data engineers to troubleshoot and improve the database systems. It also allows them to optimize business insights tools, and machine-learning systems they’re working with. Data engineers could also benefit from being familiar with Java, NoSQL, Julia, Scala, MATLAB, and TensorFlow.
2) Relational And Non-Relational Database Systems
Data engineers need to know how to work with a wide variety of data platforms. SQL-based relational database systems (RDBMSs) like MySQL, PostgreSQL (a hybrid SQL and NoSQL database), and Microsoft SQL Server are particularly important For example, they should feel comfortable using SQL to build and set up database systems. Data engineers should also develop skills working with NoSQL databases such as MongoDB, Cassandra, Couchbase, and others.
3) ETL Solutions
Data engineers should be comfortable using ETL (extract, transform, load) systems, like Xplenty. ETL tools assist with extracting, transforming, and loading data into data warehouses. They should also understand how to use ETL solutions to assist with the transformation and migration of data from one storage system or application to another.
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4) Data Warehouses
After extracting information from various business systems, data engineers may need to prepare the information for integrating it with a data warehouse system. Data integration is crucial if they want to query it for deep insights. This could involve transforming the data with an ETL tool like Xplenty.
Cloud-based data warehouses form the backbone of most advanced business intelligence data systems. Data engineers should understand how to set up a cloud-based data warehouse. They should be adept at connecting a wide variety of data types to it, and optimizing those connections for speed and efficiency.
5) Data Lakes
Data warehouses can only work with structured information, such as information in a relational database. Relational database systems store data in clearly-identified columns and rows. Meanwhile, data lakes can work with any type of data. This includes unstructured information, such as streaming data. BI solutions can hook up to data lakes to derive valuable insights. For this reason, many companies are incorporating data lakes into their information infrastructures.
For applying machine learning algorithms to unstructured data, it is important to know how to integrate data and connect it to a business intelligence platform.
Data engineers develop essential data pathways that connect various information systems. Therefore, data engineers should have a good understanding of data pipelines. They should know how they help different parts of an information network communicate with each other. For example, they should be able to work with REST, SOAP, FTP, HTTP, and ODBC—and understand strategies for connecting one information system or application to another as efficiently as possible.
7) Data Ingests
A data ingest refers to the extraction of data from different sources. During the extraction process, the data engineer needs to pay close attention to the formats and protocols that apply to the situation—all while extracting the data swiftly and seamlessly.
8) Configuring Business Intelligence Systems
After storing the data, data scientists establish the important connections between information sources. These sources could be data warehouses, data marts, data lakes, and applications. Establishing connections between sources could involve exposing the company’s data to advanced machine-learning algorithms for business intelligence. Data engineers must understand how this process works to support data scientists in their jobs.
9) Building Dashboards to Display Insights and Analytics
Many business intelligence and machine learning platforms allow users to develop beautiful, interactive dashboards. These dashboards showcase the results of queries, AI forecasting, and more. Creating dashboards is, usually, the responsibility of data scientists. However, data engineers may assist the data scientists in this process. Many BI platforms and RDBMS solutions allow users to create dashboards via a drag-and-drop interface. Knowledge of SQL, R, and Python can come in handy, though. It allows a data engineer to assist the data scientist in setting up dashboards that fit their needs.
10) Machine Learning
Machine learning is, primarily, the domain of data scientists. However, because data engineers are the ones who build the data infrastructures that support machine learning systems, it’s important that they feel comfortable with statistics and data modeling. Moreover, not all organizations will have a data scientist. Therefore, it’s good to understand how to set up BI dashboards, deploy machine learning algorithms, and extract deep insights independently.
11) UNIX, Solaris, and Linux Systems
The machine learning systems of the future will likely be UNIX-based. It is due to requirements for hardware root access and the need for additional functionality that Windows and Mac OS don’t provide. Therefore, data engineers will want to get familiar with these operating systems now if they haven’t done so, already.
How Do I Learn to Be a Data Engineer?
There's no clear path to becoming a data engineer. Although most data engineers learn by developing their skills on the job, you can acquire many of the skills you need through self-study, university education, and project-based learning.
Whether you learn to be a data engineer at a university or on your own, there are many ways to reach your goal. Let's take a look at four ways people develop data engineering skills:
1) University Degrees
A University education isn't necessary to become a data engineer. Nevertheless, getting the right kind of degree will help. For a data engineer, a bachelor's degree in engineering, computer science, physics, or applied mathematics is sufficient. However, you might want to spring for a master's degree in computer engineering or computer science. It will help you compete against other job applicants—even if you don't have prior work experience as a data engineer.
2) Free and Inexpensive Online Coursework
Some of the best data engineers are self-taught via free and inexpensive online-learning programs. Believe it or not, You could, probably, learn most of what you need to know by watching videos on YouTube. This article highlights several excellent YouTube videos that help lay the groundwork for becoming a data engineer.
Here are some free online courses to learn the basics of data engineering:
- A Beginner’s Guide to Data Engineering (Part 1), (Part 2), (Part 3): These articles on Medium will help you understand the basics of data engineering and data science. They will also help you understand data modeling, data partitioning, and strategies for extracting, transforming, and loading (ETL) data. If you want to go deeper than we have time for in this article, this guide is the best place to start.
- Free Data Engineering E-Books: These e-books from O'Reilly are another great resource for developing the foundation you need to become a data engineer.
- Udacity's Data Engineering Nanodegree: Udacity is a company that offers high-quality, free, online education around mathematics and technology. They have an entire track dedicated to teaching data engineering.
As you get deeper into your learning you'll need to master a variety of coding languages, operating systems, and information systems. Here is a list of free resources for learning the following skills:
- How to use Linux, CS401
- How to code in Python, SQL, and NoSQL
- How to use Hadoop, MapReduce, Apache Spark, and Machine Learning
3) Project-Based Learning
Finding the motivation to complete online data engineering coursework can be difficult. Many would-be data scientists quit before getting their feet wet. If that happens to you, consider the project-based learning approach.
Pick a project that sounds interesting to you. Learn the skills that you need to go along with completing the project. Project-based learning can be more fun and practical way of learning data engineering.
To add a lot more fuel to the project-based learning approach, consider writing about your work and research. Open a Medium account and devote some time to creating a few "how-to" articles on the topic of data engineering. You could also post your personal projects to Github, and contribute to open projects there on Github. These actions Doing so will boost your data engineering street cred to potential employers.
4) Professional Certifications
There are many professional certification courses for data science and data engineering. Here is a list of the most popular certificate courses in data engineering:
- Vendor-Specific Certifications: Oracle, Microsoft, IBM, Cloudera, and many other data science technology companies provide training for valuable certifications in their products.
- Certified Data Management Professional (CDMP): Data Management Association International (DAMA) developed the CDMP program as a credential for being a general database professional.
- Cloudera Certified Professional (CCP) Data Engineer: The Cloudera CCP designation is a certification for professional data engineers. It covers topics like data transformations, staging and storing information, data ingestions, and a lot more.
- Google Cloud Certified Professional Data Engineer: Applicants can receive the Google Cloud data engineer certification after successfully passing a two-hour exam.
However, these courses may not be as valuable as you think. Data engineering is something you learn by doing. Companies hiring data engineers know this.
If your employer is sponsoring you to get one of these certifications, excellent. If you're learning on your own, though, remember that learning by doing is infinitely more valuable than a certification.
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Xplenty: The Perfect ETL Tool for Data Engineers
As you move forward in this field, you'll discover how important data integration (ETL) tools are to your job. You'll also learn that not all ETL tools are the same. Compared to others, some are vastly easier to use and more powerful, like Xplenty.
Xplenty is a cloud-based ETL platform that allows you to create visual data pipelines within minutes. Our visual, drag-and-drop interface is so easy to use, you might feel like you're cheating! Schedule an intro call to book a risk-free pilot and see it for yourself.