6 Things You Should Know About Data Engineering

Data engineering is the process of transforming and preparing data for analysis. It involves various steps such as data acquisition, cleaning, transformation, feature extraction, and modeling. 

Data engineering is essential for data-driven businesses as it allows them to make better decisions by understanding their customers and operations.

Here are 6 things you should know about data engineering:

1. Process behind data engineering

In order to fully grasp the meaning of data engineering, you should first get an idea of the process behind it.

Data acquisition: Data is collected from various sources such as sensors and databases.

Cleaning: Data is then prepared in order to be suitable for analysis through a series of steps including fixing errors and filling missing values.

Transformation: The data in its current form may not be suitable for some types of analysis. In this case, the data must go through a process called transformation where new features are added or computations are performed on existing features in order to make the data fit the desired model type. 

Feature extraction: This step is mainly focused on extracting valuable information from raw data by using tools such as cluster analysis and classification algorithms. 

Modeling: The next step is to use modeling techniques to create predictive or inferential models. There are many types of models that can be used depending on the business objective. 

Data engineering continues throughout the entire data science process and its main goal is to provide a base upon which the analysis can be done.

2. Data engineers are responsible for turning raw data into valuable insights

The number of tools and techniques used in modern-day data engineering has increased greatly, therefore requiring more time from data scientists.

This is where the role of a data engineer fits in – they handle all of the dirty work involved with preparing and cleaning up the data before handing it over to a data scientist who uses it to answer specific business questions.

In fact, a recent study shows that 95% of companies stated that big data projects fail because there isn’t enough talent to support them.

Therefore, hiring a dedicated team for data engineering will allow for better big data initiatives. You can find such a team at https://dsstream.com/ and they will be glad to help you. Keep in mind that data engineers are not the same thing as data scientists. 

3. The role of a data engineer in an organization

The role of a data engineer is very important as they handle both the technical aspects and aspects relating to the business. A great deal of value can be obtained from raw data if it has been processed correctly, therefore making the job of a data engineer quite significant.

Data engineers are also responsible for creating pipelines that allow data scientists to easily access and explore raw data, which makes their job extremely valuable as it frees up time for them to focus on making sense out of all the large amounts of information available.

They must understand how new features affect existing models and algorithms so they can provide input when deciding what steps should be taken next with regard to data processing.

4. Who benefits most from Data Engineering?

Data engineering is essential for any company that has huge amounts of data. It can be applied to various fields including customer intelligence, advertising, marketing, fraud detection, security, and risk management.

Even companies in the fintech industry use data engineering as it is essential for them when making decisions that affect their bottom line. Additionally, businesses that rely on making real-time decisions can greatly benefit from data engineering.

5. Accuracy vs. Speed

A common problem that companies face is that they want both accuracy and speed, but the two are often conflicting as processing large amounts of data requires a lot of time and resources.

One possible solution is to use an approach called active learning, which involves training a machine learning algorithm on a small subset of the data before testing it on new records.

If the results obtained from the test set do not match those given by human labels, then more information can be provided so as to improve future predictions made by the machine learning model.

A good example for this would be if you wanted an algorithm to predict whether or not someone would click on an ad after seeing it for just one millisecond.

This would be impossible to do as you need much more information before deciding whether or not the ad should be clicked on.


5. The future of data engineering

In the next few years, data engineers are expected to build models that are less reliant on human intervention. To do so, they must focus their efforts on boosting the automated machine learning process while making it more accessible to the mainstream.

They must also assist in making sense of all the new technologies involved with automation and AI. Lastly, it is important for them to stay updated on the latest technological advancements in order to always provide viable solutions for their company’s big data initiatives.

6. How to become a data engineer?

From what we’ve mentioned above, one can easily conclude that data engineering will play a major role in any field involving large amounts of raw data, which means it will be around for a long time!

While this profession mostly requires people with programming skills, understanding things such as statistical analysis and mathematics when handling large amounts of data is also very important.

The good news is that there are many different online courses you can find on sites such as Coursera, Udemy, Edx, and DataCamp.

These will not only provide insight into the various aspects of this job but will also help you get one step closer to becoming a data engineer!

Data engineering is a skill that will be around for many years to come. It’s important to understand how this type of data processing works and what it can do for your business as well as the various fields where this expertise may be needed.

The good news is, there are courses available online which can provide insight into these topics and help you get started on learning more about data engineering. We hope you enjoyed this article and that you’ve found it helpful!