What is Data Engineering?

A Data Engineer’s primary focus is to assist companies in scaling their reporting capabilities beyond the limitations of spreadsheets. Automated systems are implemented to replace manual processes and import data from various sources, which is then transformed for easy visualization or use in data science models.

In this post, you will learn:

1. Why Data Engineering exists
2. Data Engineering Outcomes
3. Primary Responsibilities of a Data Engineer
4. Data Engineering Outcomes
5. A Modern Data Stack Overview

Why Data Engineering Exists

Working with data can be messy and complex. Raw data is often distributed across multiple sources, in a variety of formats, and needs to be cleaned, transformed, and integrated before it can be used for analysis. This is where data engineering comes in.

Data engineers are responsible for designing, building, and maintaining the data infrastructure and pipelines that enable organizations to ingest, store, process, and analyze data at scale. They work with a range of tools and technologies, including databases, data warehouses, data lakes, ETL (extract, transform, load) tools, and big data platforms, to ensure that data is accessible, reliable, and accurate.

Obstacles often prevent companies from quickly gaining insights from their data. These obstacles include:

1. Inability to process large amounts of data efficiently with traditional tools like Excel.
2. Difficulty in consolidating data from multiple sources.
3. Complex and inconsistent business rules across the organization.
4. Manual data refresh process which leads to wasted time and resources.
5. Lack of a centralized source of truth, resulting in duplication of efforts and inconsistent results.

Data Engineering Business Outcomes

Clean and well-structured data sets enable several key business outcomes, including:

1. Improved decision-making. Internal stakeholders can bring data to every meeting and answer more questions now that data is more accessible.
2. Increased operational efficiency. Data engineering enables the automation of repetitive tasks and processes, freeing up time and resources for more valuable work.
3. Embedded Reporting. Integrating reports and analytics directly into business applications
4. Increased revenue potential. By providing data services to customers such as customized reports or automatically pushing data to their data stack.

Primary Responsibilities of a Data Engineer

1. Extracting data from primary sources. This allows the data engineer to work with all the organizations’ data in one place and perform workloads in a platform designed to handle large data loads.
2. Transforming Data. The process of cleaning and shaping data in a way that makes it easy for business analysts to make visualizations and for data science teams to build models.
3. Loading data into a Data Warehouse and building a Data Lake. The Data Warehouse will support internal and external analytics and your business users. The Data Lake will help support Data Science initiatives.

A Modern Data Stack Overview

The set of technology and tools that is needed to carry out the data workloads is a data stack. The diagram below illustrates a high-level overview of how data flows within the various systems to make the data outcomes possible.

The diagram below reflects a batch analytics workflow, which processes data in batches at predetermined intervals, such as daily or weekly. While real-time analytics can provide immediate insights, batch analytics has several advantages. Firstly, it can handle large volumes of data more efficiently and is often more cost-effective. Secondly, batch analytics can perform more complex transformations, such as machine learning, on the data before analysis. The main drawback of batch analytics is that there can be a delay in data processing and analysis. However, as of this writing, batch analytics is easier to set up and maintain, making it a practical and effective option for organizations that require in-depth analysis of large datasets.

A more in-depth look into modern data stacks can be found here – https://www.moderndatastack.xyz/stacks.

Final Thoughts

I wrote this post from the perspective of a startup looking to get started with its data engineering project. The field is changing rapidly due to companies capturing more and more data throughout their applications and with advances in software that make data engineering more approachable to non-engineers.

Thanks for reading!