Blog
{Blog}  MongoDB named as a leader in the Forrester Wave™: Translytical Data Platforms, Q4 2022 - learn more

Data Engineering Explained

Data is the new oil in today’s world, and gone are the days when quantification of data was in GBs. Now, it’s terabytes, soon to be petabytes. With the IoT devices coming into play, raw data is in abundance, and we need engineering skills to extract meaningful information.

Data engineering is the foundation stone in unraveling that information. It’s the science that deals with the collection, transportation, transformation, and secure storing of data so that meaningful information can be derived at scale. Organizations have a massive ability to collect raw data through various systems, and these information streams are aggregated by data engineers to convert them into a usable form for enabling other teams to do analysis at scale.

Data engineering, and data science in general, is considered very important in any organization. They help to make informed decisions by helping decision-makers understand the user behavior with the data points captured at different stages. Not only that, but it also enables them to validate the outcome of decisions taken and identify new business opportunities.

What does a data engineer do?

It’s very important to understand what data engineers actually do. They’re often confused with data scientists, as it's a very niche domain and finding the right talent is hard. Data engineers are primarily responsible for collecting and aggregating data into logical blocks. But the ultimate goal of any exercise performed by data engineers is to make data accessible for other teams, which can be used to understand the business's key metrics performance.

Depending on the size of the organization and data, there is huge variation in the day to day work of data engineers. Some of their key responsibilities include:

  • Finding and collecting data from various sources required for business needs.
  • Cleaning up data.
    • Removing errors.
    • Standardization of the data into a common format and types for fields, like dates and prices.
    • Removing any sensitive data present in raw data.
  • Developing architecture for building data pipelines to ensure continuous data flow into various systems.
  • Developing methodology or algorithms to improve/maintain data quality and reliability.
  • Storing data into a normalized format in the data lake or warehouse.

As mentioned earlier, a lack of awareness coupled with high demand for these highly paid roles have often confused data engineers with data scientists.

Data engineers vs data scientists

Data engineers' and scientists' roles are complementary to each other. The former deals with data extraction whereas the latter is involved in extracting information from it. Data scientists often rely on data engineers to provide them with reliable and consistent data, which they feed into machine learning models and other analytical tools to understand the user behavior impacting business decisions. If not done correctly, this can impact the result of the analysis.

In a very simple example, if you want to understand the sales pattern of your product across different parameters (like age of the customers, frequency of repeated orders, and gender pattern), data engineers would aggregate data from various sources and using different ETL (extract, transform, and load) techniques to create a big data warehouse for data scientists to run, analyze, and report.

This difference can also be seen in the skillset required.

Skillsets for data engineers include:

  • Familiarity with different types of databases and tools (SQL, NoSQL, document-based, etc.) like MongoDB, Postgres, and MySQL.
  • Open source and big data platforms like Hadoop, MapReduce, and Kafka.
  • Programming languages like Python, Java, and Scala.

And the skillsets of data scientists include:

  • Familiarity with databases and tools, similar to data engineers.
  • Programming languages like R, Python, Matlab, and TensorFlow.
  • Tools like RStudio, Tableau, and machine learning systems.
  • Comfortable with quantitative analysis, statistics, and mathematics.

Is data engineering hard?

Nothing is hard if you have the right skills and knowledge. Since this is a fairly new and niche area of engineering, becoming a data engineer can be overwhelming for entry-level software engineers as it requires multiple software engineering skills.

How to become a data engineer

With the right set of skills and knowledge, anyone can have a rewarding career as data engineer. Many data engineers have bachelor's degrees in computer science or related fields. If getting a degree is not an option, you can also consider doing an online certification course like Udacity Nanodegree, Google Cloud, or IBM certification for data engineers.

Additional foundation courses complementary to these are:

Frequently asked questions

What degree is data engineering?

Most data engineers have a bachelor's degree in computer science or a related field, but it's the skills that matter the most when it comes to working in any professional environment.

What skills are needed for data engineering?

Data engineering requires good knowledge of programming languages like Python or Java, and familiarity with databases like MongoDB and Cassandra.

Do data engineers code?

Yes, a good amount of time in data engineering is spent on coding and trying to collect and manipulate data into a usable state.

Do data engineers use SQL?

Yes they do, but they’re not limited to only this. They should be aware of NoSQL databases, as well, and how to use them.

Is Python enough for data engineers?

Data engineering is a very diverse domain and one programming language is not enough to carry out all day-to-day tasks.