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Data Engineering is one of the ‘big three’ data jobs which power modern businesses and technology
The importance of data in the world today cannot be overstated. In fact, some companies’ business model is based on the sale of data to those who need it. Data is very important to businesses and organizations as it allows them to understand their operations and business processes better. It gives them the ability to make good decisions, see how they are performing and where they can improve, and presents them with opportunities for new initiatives.
For example, Netflix, a video streaming service, has proven to be very good at recommending new movies and TV shows to its users. 80 percent of the content watched on Netflix is as a result of their recommendation system. Netflix is able to achieve this because it collects a lot of user data such as users’ previously watched shows, the time and date they watch a show, whether they complete a series, and so on from its users to build a detailed profile for its users.
As important as data is, it is quite useless without being processed or analyzed. Analyzing data involves a series of steps carried out by a team of data science professionals. You’re probably familiar with data scientists and data analysts. But there is one member of this team that doesn’t get as mentioned as the two above ― data engineers. So who exactly are data engineers and what do they do?
A Data Engineer is a data science professional whose job is to structure data in a way that makes it easy to use or analyze. Data Engineers usually work with data scientists and analysts and are rarely found without either or both of them. Before a data scientist or analyst sets out to analyze data, the data has to be structured in a particular way first. This is necessary because the data to be analyzed isn’t always stored in the same place or the same format. They usually come from different sources. But a data scientist or analyst needs to work with the data from a single source.
It is the job of a data engineer to ensure that the data needed by the data scientist is in one place and in the proper format. To achieve this, data engineers go through a process called ETL ― Extract, Transform, Load.
Extraction: The first thing a data engineer does in structuring data is to extract the data from the various sources they reside in. The data engineer then stores the data in a temporary database to be transformed.
Transformation: The next thing is to structure the data in a way the data scientist or analyst can use it. This could be anything from combining columns to summing the values in several columns to converting from one data format to another. The actions they perform at this stage depends on what the data scientist or analyst wants.
Loading: This is the final step and the easiest of the three. Here the data engineer loads the data into a data warehouse. It is from the data warehouse anyone in need of the data accesses it.
If you have been reading this article from the beginning, you probably already have an inkling of the differences between a data engineer and a data scientist. But let’s look at the differences more clearly. A data scientist’s job is to derive insights and look for patterns from data.
They do this by analyzing data available to them using statistical methods, machine learning, and analytical software applications. A data engineer, on the other hand, is more concerned with the quality of the data a data scientist is working on. They ensure the data being used by the data scientist is reliable and well-architected.
There are certain skills a data engineer should possess to effectively carry out their job. Data engineers should have a good grasp of:
Asides these skills, there are several certification exams that data engineers can take. Here are some of them.
Data engineers require several tools to carry out their job effectively. These tools play different roles in the ETL process. Some of them are:
Apache Hadoop – Used for storing and analyzing data in a distributed processing environment.
Apache Spark – Used for data processing (batch and real-time stream processing).
Apache Kafka – Used for data collection and ingestion.
SQL and NoSQL – Used for manipulating data in relational and non-relational databases.
Amazon Redshift – A Cloud data warehouse used for storing transformed data.
A data engineer can work in any company that collects large amounts of data and need to extract useful information from it, which is the case with most tech companies, anyway. So there are several job opportunities available to them.
According to a job report by Dice, data engineering was the fastest-growing tech occupation in 2019, with an average time-to-fill of 46 days. You can check out a list of job openings, categorized by states here. On average, data engineers earn $100,000 annually. This figure can go higher or lower depending on the size of the company. In the job listing linked above, some offers go as high as $162,000.
Data engineering is a fledgling field that only became known around 2011. Before then, the job of a data engineer was done by other data science professionals. This means data engineering jobs will only get more as more companies scale or pop up. You can join the train of future data engineers today and get employed almost immediately. What’s more, it’s an interesting job where you get to solve challenging problems while constructing information systems for companies