The Various Steps Involved in Data Engineering




Wissen Team


July 2, 2024

Businesses today produce so much data that they often don't know what to do with it. While a lot of the data that is generated is irrelevant, there is also a massive volume of data that is critical for business decision-making and influences how a company operates. Customer feedback, market trends, sales performance, etc., are a few examples of the latter. But understanding what stories the data tells isn't always straightforward, which is why many businesses are now turning to data engineering.

Data engineering systems suggest practical applications of business-critical data by designing and building systems that allow for the collection and analysis of raw data from multiple sources and formats.

However, crafting a data engineering project requires a profound understanding of the entire process. Let's dive right into the various steps involved in data engineering.

A Step-by-Step Guide

With data lying across different systems, stored in different structures, and managed by different technologies, the process of data analysis is becoming increasingly challenging. To get a comprehensive view of data, you need to unify the different data sets to find answers to questions quickly and efficiently.

Data engineering is that engine that allows you to uncover important insights from massive amounts of disparate data and use them to drive business decisions.

But making the most of any data engineering project requires following a streamlined process. Here's a step-by-step guide:

1. Understand the Objective of Your Data Engineering Project

The first and most critical step in data engineering is to be cognizant of your goals and objectives. Begin by understanding what needs to be built and why you need to build it. This will help you get a clear idea of your scope or requirements, the business impact, and your stakeholders. It will also help you with a fair understanding of your timelines, milestones, and resources while providing insights into what the solution may look like.

2. Find the Right Data Sets

Any data engineering project depends heavily on data, which makes it extremely important to find (and use) the right data sets. Since every data set carries a certain degree of imperfection, you must spend time cleansing and preparing data to make it more usable for your data engineering project.

Make sure to establish the right data collection mechanism, and instead of going the big bang way, start small. Begin by understanding what data you have and how much of it is critical. Then, prepare, format, and cleanse this data, so you end up with high-quality data sets for your data engineering project.

3. Choose Your Tools Wisely

As data engineering evolves rapidly, the choice of tools depends greatly on your business objectives, the outcomes you're looking to achieve through the data engineering project, and your architecture design. While evaluating different tools, make sure to see how they help you meet your unique data engineering requirements.

Assess tools on their capabilities, ease of use, and speed to deliver outcomes – securely and reliably. Also, gauge how flexible and interoperable they will be within your business environment. If budget is an issue, make a build vs. buy comparison or look for open-source tools.

4. Streamline the Data Modeling Process

If you want to achieve the best outcomes from your data engineering projects, you need to streamline your data modeling process. Knowing how to group data, structure partitions, what tables to create, and what text and symbols to use to represent data is critical in crafting the ideal data engineering blueprint.

Using the right abstraction can help you better understand the nature of data flow within your enterprise information system and aid you in creating the best visual representation of the connections between your data points and structures.

5. Be Clear About the Level of Abstraction You Need

Another critical requirement of any data engineering project is being aware and clear about the level of abstraction you need. Although you can begin the design process at a high level of abstraction, as you go along the journey, you need to make it more concrete and specific.

According to the degree of abstraction you need, you can choose from three different models:

  1. A conceptual model that offers a big-picture view of what the system will contain, how it'll be organized, and the business rules involved.
  2. A logical model that is relatively less abstract and provides greater detail about the concepts and relationships in the domain under consideration, or
  3. A physical data model that provides a schema for how the data will be physically stored within a database.

6. Invest in the Best Skills

Data Engineers need a very special skillset to develop software solutions around data. While skills across various technologies, such as SQL, Python, Java, Hadoop, etc., are indispensable, make sure the people you bring on board have substantial experience and expertise in software engineering.

Since the data engineering project would need to be up to the mark, you must ensure the team has proficiency in low code/no code programming, end-to-end testing, and automation. In addition, competence and deep knowledge of DevOps, Agile practices, distributed systems, data warehouses, and service-oriented architectures will go a long way in ensuring the long-term success of your data engineering project.

Summing Up

Nearly all employees today leverage data to do their jobs: right from solving problems to building roadmaps. Innovative data techniques are helping them resolve complex challenges in just a matter of a few hours. And one technique that is making everything possible is data engineering.

As the new fuel powering forward-thinking businesses, data engineering offers the ability to completely change how they leverage data. The right approach to data engineering can unlock true value from business-critical data while allowing different departments and individuals to make accurate data-driven decisions.

Following the steps above can help you craft a data engineering project that enhances efficiency, drives agility, and enables your company to ride on the innovation wave.  

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