What is Data Engineering?

From helping Facebook tag you in photos to helping cars drive themselves, data science has attracted lots of buzz recently. Data science allows organizations to efficiently understand enormous amounts of data from multiple sources and derive valuable insights to improve their processes. But how are these vast amounts of data transformed into readable and usable information for data analysts and business analysts to interpret? Well, this is where data engineering comes in.

Understanding Data Engineering

Data engineering refers to the process of designing and building pipelines that transport and convert data into a format wherein, by the time it reaches data analysts, scientists, or other end users, it’s in a highly usable state. These data pipelines take data from many different sources and gather them into a single warehouse representing data uniformly as a single source of truth.

Why Is Data Engineering Important?

Data engineering is necessary because it allows organizations to optimize data toward usability. For instance, data engineering plays a vital role in the following pursuits:

• Bringing data together into one place using various data integration tools
  
  
• Deepening your understanding of business domain knowledge
  
• Discovering and addressing information security loopholes, thus protecting the company from cyber attacks
  
• Finding the best practices for improving a company’s software development life cycle
  
  

Why Does Data Need Processing Through Data Engineering?

Historically, data engineers have crafted data warehouse schemas, with indexes and table structures designed to process queries in no time to ensure adequate performance. However, with the rise of data lakes—central storage repositories that hold big data from various sources in a raw, granular format—data engineers have much more data to process and deliver to data scientists and other data consumers for analytics. Data stored in data lakes may be unformatted and unstructured, which is why it needs attention from data engineers before data scientists or groups within an organization can derive value from it. 

For instance, consider all the data an organization collects about its customers:

• One system maintains order history
  
• Another system contains information about shipping and billing
  
• And other systems store behavioral, customer support and third-party information
  
  

Together, these data can provide you with a comprehensive view of the customer. These different datasets, however, are independent, making answering certain questions—like what kinds of orders result in the highest customer support costs—challenging. Data engineering combines this data and allows companies to answer their queries quickly and efficiently.

Data Engineering Process

Almost all data engineering processes in every organization go through the following steps:

1. Data Flow and Accumulation

This stage requires data engineers to input data in plain text format known as Extensible Markup Language (XML) format. As per this data, the company prepares batches of videos (updated on an hourly basis), weekly batches of labeled images and so on. Data engineers then use the available data, design models, and store the end result.

2. Data Normalization and Modeling

Once all the business data gets stored in one central location, date engineers perform data normalization and models. It involves tasks like filtering out the data necessary for insight extraction, eliminating duplicates, and conforming data to a specific data model. Date engineers then store the normalized data in a data warehouse or relational database. Data modeling and normalization are part of the transformation step of ETL (Extract, Transform, Load) pipelines—the set of processes used to move data from multiple sources into a database. 

3. Data Cleaning

During the data cleaning process, data engineers remove all incomplete, duplicate, incorrect, and corrupted data sources. Once data engineers merge different datasets from different sources, they end up finding several problems, such as incorrect outcomes, unreliable outputs, data duplication and mislabeling. 

4. Data Conversion

Once the data is cleaned and prepared for corporate use, data engineers convert them into a meaningful format so that data scientists or departments within the organization can use them for analysis. Some companies use the CSV format, while others use the JSON format.

5. Automation and Scripting

Data engineers often write repetitive manual ETL validation scripts to validate and clean massive data streams. This manual process consumes several hours of productive engineering that can otherwise be spent on higher-value tasks. However, if organizations automate the process, they’re far less likely to avoid spending several hours cleaning and validating data. Scripting for automation is essential for performing repetitive operations without the need to spend a great deal of time and effort. 

6. Data Accessibility

Once all the data is fully prepared for analysis, data engineers check if the company and customers can access them. Data accessibility is concerned with how easily users can retrieve their stored data from any repository or database.