Data Engineering Process – A Guide to the Who, What, and How

Today, for almost every business, data is the primary element employed for comprehending various business metrics. Since every company produces lots of data – from stock price, sales performance, customer retention, and customer feedback, companies can precisely use these data to answer their specific business questions. In a firm, different tools & systems generate and collect data, and each system runs under a particular department or owner. Connecting the dots of such business data from various sources can give the company a comprehensive view of what the customer wants and where the business stands. All these things come under data engineering. In this article, we will get into in-depth details of data engineering and the steps used in the data engineering process. You can learn more about data engineering process by checking out ProjectPro Data Engineering Projects for Beginners.

What is Data Engineering?

Data engineering is the designing and practicing of developing corporate systems that can help collect, manage, and analyze valuable data at scale. Almost every industry can leverage its benefit because organizations gather a massive data chunk to understand the need for the right people through the right technology. Data engineering allows companies to accumulate and process the filtered data reliably, quickly, and securely so that data scientists and professionals can analyze them from one place.  

What does a data engineer do?

They will use various settings and systems to accumulate, manage, and convert diverse data into a usable form so that business analysts and data science professionals can interpret them for business benefits. A data engineer’s ultimate goal is to extract data from various sources and make data accessible to different departments within the organization, utilizing it for evaluating & extracting granular insights from these data. Here are some of the tasks a data engineer has to perform.

1. Collect data from various sources and create a dataset that aligns with the business needs.
2. Develop algorithms for transforming data into valuable and actionable information.
3. Create, test, and maintain data pipeline architecture.
4. Collaboration with other departments is essential to understand the company’s objective and what data will yield better insight.
5. Create new data validation techniques and leverage new data analysis tools.
6. Also, data engineers need to stay aligned with the data governance and policies.

Fundamenta Steps of the Data Engineering Process

Almost all data engineering processes in every company go through the following steps.

1. Data flow and accumulation: The first stage of data engineering is to collect data from various sources and departments. The data engineers will then label those data and keep them in different files and directories under one location for further processing.

2. Data normalization and modeling: Once all the business data gets piled up in one central location, the data engineering team will perform data normalization and modeling. It includes processes like filtering out the data needed for extracting insight, removing duplicate data, and blending data into a precise data model. Data normalization & modeling work as the data transformation step toward ETL (Extract, Transform, and Load) pipelines.

3. Data cleansing: The next phase of the data engineering process in any data engineering project is data cleaning. The team removes corrupt, incorrect, wrongly formatted, incomplete, and redundant data. From the previous phase, while merging different datasets from different sources, the data engineering team could see errors like mislabeling, unreliable output, incorrect outcome, or structural errors. Data cleansing also attempts to remove those glitches and differences. Filtering outliers and rendering the most effective form of the dataset with minimum or no null values is the ultimate goal of the data cleaning phase.

4. Data conversion: Once the data is clean and prepared for corporate use, the data engineering team must convert these data to a meaningful format that various departments within the company use for further analysis. Some companies use JSON, some CSV, while others in other customized configurations. This phase will make the data entirely ready to use for others like data scientists and business analysts.

5. Automation and scripting: Scripting for automation is essential to handle various repetitive operations to reduce human efforts and perform them in less time. Automating various redundant works while dealing with big data and large data sets from different sources is essential. It is because the data engineering process extracts data from diverse sources. Thus, handling & organizing so much information manually can be tedious. So, the engineering team might also need to write scripts to automate various repetitive tasks.

6. Data accessibility: In this phase, once all the data gets fully prepared for analysis, the team checks for accessibility from both customer’s perspective & the business perspective. Data accessibility concerns how easily users can retrieve their stored data from any repository, Cloud storage pricing, or other databases. The data engineering process also assures that other departments and internal data analysis teams can access the data prepared for them to analyze. 

Data Engineering Skills

The various skills needed for the data engineering process are:

• Programming: Proficiency in a few programming languages like Python, C++, R, Scala, Java, SQL, and NoSQL can help extract data and implement logic over data.
• Database handling (relational and non-relational): Database systems rank among the top data storage for storing relational and non-relational data.
• Big data tools: The data engineering process does not deal with regular data but has to manage a massive collection of data in bulk amounts. The data engineering team uses tools like Hadoop, Kafka, and MongoDB.
• Cloud storag
• e and engineering: Storing such bulk data amounts in small storage is not feasible. Therefore, a proper understanding of cloud architecture & storage is essential in the data engineering phases.
• Automation and scripting: Automating various tasks by running scripts enables the team to do different operations in less time. Handling and organizing so much information from different sources needs this script-based automation.
• Data science: Data cleansing, normalization, blending data into a precise model or dataset, and meaningfully categorizing those datasets come under data science.
• Understanding of data security: Since data engineering processes deal with so many customers & business data, data security is also a significant factor to keep in mind.