What is data engineering?

Data engineering is the modern way Indicium, Netflix, Spotify, and other companies are working with data.

It is data engineering that is largely responsible for the revolution that happens in the decision-making of all businesses that apply it when working with data science and BI.

This is because it automates the entire process of extraction, transport, and delivery of high-quality data, according to the company's specific demands.

In this article, you'll better understand this concept and how it happens in the day-to-day practice of data engineers, a key role in every champion data team.

Enjoy this read on what data engineering is. And then, share your impressions with us.

Data engineering and the role of the data engineer

There's a lot of talk about data science and BI, and it's no wonder. Both are revolutionizing the way business decisions are being made.

But there is a very important aspect about these areas that is not yet talked about so much: the need to receive good and relevant data.

An analysis based on low-quality data is as good as a guess, an intuition, whatever you want to call it. And to ensure high-quality data entry, data engineering comes into play and its main function: data engineer.⠀⠀⠀⠀⠀⠀⠀

What is quality data in data engineering?

For data engineering, quality data is data with the potential to provide information to the company.

We speak of potential because, when collected, this data is in a raw state. It is through proper treatment that this data will be transformed into raw material.

A high-quality die has some characteristics, such as:

1. Accuracy – the most important, because if the information is full of errors and false material, the data is garbage.
   ⠀⠀⠀⠀⠀⠀⠀⠀⠀
2. Completeness – When you're using consumer data, it may not be fully complete and half data only tells half the story.
   ⠀⠀⠀⠀⠀⠀⠀⠀⠀
3. Availability – There's no point in good data if it's not available for everyone in the organization to do their jobs.

The Data Engineer Role in the Data Pipeline

Once the high-quality data has been chosen, it's time for the data pipeline, a program infrastructure made by those who act as data engineers to automate the process of extracting, loading, and transforming data.

In data engineering, the order between data loading and data transformation operations differentiates pipeline architectures. In short, in these operations you either work with ETL or with the ELT.

Data engineering and the ELT

ELT and ETL have advantages and disadvantages in data engineering. Here at Indicium, we preferentially use ELT for a number of good reasons, such as:

- because it is a very positive modernization of ETL;

- because it brought more efficiency when you put everything on the scale;

- because this architecture offers democratization for the use of data;

- because the transformation part is in the hands of those who master the rules of the business;

- because all this ensures more agility;

- And because time is money!

The ELT process (extract, load, transform) occurs according to its nomenclature, i.e., it is divided into these steps:

(1) extraction of data from its source, which varies according to the nature of the company;

(2) uploading, when the data is transported to where it can be accessed by all interested parties (data warehouse); and

(3) transformation, when finally the data is treated, appropriated and stored also in a data warehouse.

We'll break down each of them below.

1- Extraction: how to collect quality data

Data can come from a variety of sources. It is the duty of anyone who is a data engineer to know how to deal with this variety, as well as the limitations of each source.

See the table below for some examples of data sources and their related applications.

When you are a data engineer and work with the company's database as a source, you must be careful that its extraction does not hinder the operation of the database for the other people who will use it.

Know that a website can also be a data source, and in this case, the extraction can be done through an API. A crawler would be an ingenious solution for this, but you need to pay attention to the site's policies so that it doesn't get blocked, or vice versa.

And here, a valuable tip is the two data engineering tools for the extraction time that cannot be missing from the toolkit of those who are data engineers at Indicium: Embulk and Singer Taps. Both make it easy to get raw data from a variety of sources.

2- Charging: where to store the data

Once the raw data has been collected, it's time to upload it to an accessible and well-cataloged location.

At Indicium, this process occurs by first loading the data into data lakes and then into data warehouses.

Keeping raw data preserved and cataloged is essential to ensure integrity and access in the future.

3- Transformation: when data becomes information

Now that the data is in the data warehouse, you can turn it into information that aligns with the needs of the business.

This transformed data will remain in the data warehouse with its identifying labels, where it will be ready for use by data scientists or data analysts.

And the golden tip here is: for the data transformation done at Indicium, dbt is the right tool.

Cataloguing

The concept of the data pipeline involves the idea of making data accessible to everyone who needs it. This process, called data democratization, is crucial to ensure that the company's analytics are consistent across all of its industries.

Therefore, in addition to ensuring that the data is of quality, it is important to catalog the data in an intuitive way that is appropriate to its nature. In this way, any authorized person will be able to find and use data, contributing to the company's governance.

Orchestration

The data pipeline process only works well if each step occurs in the right order and at the right frequency.

To do this, it is necessary to understand the availability of the data at its source, its dependencies, and when this data needs to be used. And it is the mission of anyone who is a data engineer to ensure this harmony through the orchestration of the data pipeline.

Orchestration in data engineering ensures that data is extracted, loaded, and transformed in the proper order and frequency.

And a tooltip is: here at Indicium, we use Airflow for that.

Data Engineering: Summing Up

• Data engineering is the modern way Indicium, Netflix, Spotify, and other companies are working with data.
• Data scientists and data analytics need to have quick and convenient access to quality data to work their magic. And who guarantees that? Data engineer.
• The work of the data engineer revolves around the creation and maintenance of data pipelines, an infrastructure to collect data, transform it, and store it in the place where it will be requested.
• At Indicium, we use ELT in our data pipelines.
• The data extraction step (E of the ELT) is to collect the data from its various sources in an appropriate way and at the right time.
• The upload step (ELT L) consists of concentrating the data in a place where it is easily accessible by all authorized persons.
• The transformation part literally transforms the data into useful information for the company.
• This whole process requires harmony and agility, which is guaranteed by orchestration.
• Quality cataloging ensures that the data is easily accessed.
  

If you want to learn even more concepts, tools, and processes of data engineering, follow Indicium: access our channels at the bottom of this page.

And subscribe to our newsletter by entering your name and email, and clicking on the Subscribe button below.

See you later!