An Introduction to Data Engineering

Bidhan Baruah

December 1, 2023

One of the fundamentals of data engineering is that businesses generate a ton of data—but they don’t always capitalize on it. 

That’s partly due to a lack of proper mechanisms and processes and partly because there’s just so much data flooding in every day.

The job of preparing all this data for analysis and operational use falls on data engineers. 

Data engineers turn the deluge of raw information into a more manageable pool of clean, organized, filtered data that data analysts, data scientists, and similar roles can use to improve the business.

What Is Data Engineering?

Data engineering involves developing, testing, and maintaining architectures, tools, and techniques for collecting, storing, processing, and analyzing data. 

A data engineer’s primary goal is to enable organizations to make data-driven decisions by providing a reliable, scalable, and efficient infrastructure for handling large volumes of data.

Data engineering plays a crucial role in the data lifecycle, providing the infrastructure and processes needed to turn raw data into meaningful insights for decision-making.

Why Is Data Engineering Important?

Data engineering’s role has become essential in the era of big data and data-driven decision-making. It ensures that data is available when and where it is needed. It involves creating systems and processes for efficiently ingesting, storing, and retrieving data, making it accessible to users and applications.

Data engineers implement processes to ensure the quality and integrity of data. These processes include validation, cleaning, and governance measures to maintain the accuracy of data, which is essential for making reliable business decisions.

However, raw data is often not suitable for direct analysis. Data engineering transforms and processes data to make it suitable for analytics and reporting. 

As data volumes continue to grow, organizations need scalable solutions to handle large datasets. Data engineering addresses the challenge of scalability by designing systems that can efficiently handle increasing data loads.

Many organizations have data spread across different systems and sources. Data engineers integrate data from diverse sources, providing a unified view for analysis. This integration is essential for obtaining a comprehensive understanding of business operations.

Data engineering provides the foundation for machine learning and artificial intelligence applications. Clean, well-organized data is a prerequisite for training accurate machine learning models, and data engineering ensures that the data is in the proper format for such applications.

Data engineers create and maintain data pipelines, which automate the flow of data from source to destination. Efficient data pipelines streamline data processing workflows, reducing manual intervention and improving overall efficiency.

Quick and reliable access to data allows organizations to respond rapidly to changing business conditions. Data engineering facilitates agility by providing a responsive data infrastructure that supports the evolving needs of the business.

Well-designed data engineering solutions can improve cost efficiency by optimizing data storage, processing, and transfer. This is especially important as the volume of data grows, and organizations seek ways to manage costs associated with data management.

Finally, regulations related to data privacy and security are increasing. Data engineering plays a critical role in ensuring compliance with these stringent new laws. It involves implementing governance practices and security measures to protect sensitive information.

What Do Data Engineers Do?

A data engineer’s primary responsibilities revolve around designing, building, testing, and maintaining the architecture and infrastructure necessary for collecting, storing, and processing large volumes of data.

The fundamentals of data engineering are:

  • Acquisition: Data engineers are responsible for developing processes to collect and import data from various sources, such as databases, logs, APIs, and external data streams, into the organization’s data storage systems. 
  • Cleansing: Data engineers design and implement systems for processing and transforming raw data into a usable format. It involves cleaning, aggregating, and enriching the data to prepare it for analysis.
  • Conversion: Integrating data from disparate sources is a common task. Data engineers create processes to combine data from various systems, ensuring a unified and consistent view for analysis.
  • Disambiguation: Some data can be interpreted in multiple ways. Data engineers ensure the correct interpretation gets relayed to the individuals using the data.
  • Deduplication: When data flows in from multiple sources, it is often duplicated. Part of a data engineer’s responsibilities is removing duplicate copies of data.

These tasks prepare the data for storage in a central repository, where it can be retrieved and used by data analysts, data scientists, business intelligence analysts, and others. 

What Tools and Skills Are Needed for Data Engineering?

Data engineering requires a combination of technical skills and familiarity with specific tools to manage and process data effectively. Here’s an overview of the key skills and tools commonly used in data engineering.

Programming Languages: Python and Java are widely used for building data pipelines, processing data, and integrating with various data systems.

Database Knowledge: Depending on the organization’s approach, either SQL or NoSQL will be used. SQL is essential for interacting with relational databases (e.g., MySQL, PostgreSQL) and performing data manipulations. NoSQL databases (e.g., MongoDB, Cassandra) are used in non-relational databases for handling unstructured or semi-structured data.

Data Modeling Techniques: Data engineers understand how to design effective data models that reflect business requirements. Data modeling involves creating an abstract representation of the data and its relationships within a system. Data models serve as a blueprint for designing databases and organizing information to meet specific business requirements. There are several data modeling techniques, each with its approach to representing data structures. These data modeling techniques are not mutually exclusive, and often, a combination of these approaches is used to create comprehensive and effective data models based on the specific requirements of a project. The choice of technique depends on factors such as the nature of the data, the system architecture, and the goals of the data modeling process.

ETL (Extract, Transform, Load) Processes: Data engineers are proficient in designing and implementing ETL processes. These processes are used in data integration and data warehousing to move and transform data from source systems to target systems. ETL processes are critical for consolidating and organizing data from various sources into a format suitable for analysis, reporting, and business intelligence.

Data Quality and Governance: Data engineers fully understand data quality principles and implement data governance practices. 

  • Data quality refers to data accuracy, completeness, consistency, reliability, and timeliness. High-quality data is essential for making informed business decisions, conducting accurate analyses, and ensuring the overall success of data-driven initiatives. 
  • Data governance is a set of practices and policies that ensure high data quality, data management, and data stewardship within an organization. It involves defining and enforcing rules, roles, and responsibilities related to the acquisition, storage, use, and disposal of data. The goal of data governance is to establish a framework that promotes data stewardship, accountability, and compliance with data-related policies. 

Scripting and Automation: Data engineering can involve many repetitive tasks that are more efficiently handled by automation. Data engineers write scripts to automate these repetitive tasks using tools like Bash or PowerShell.

Data Warehousing: Data engineers are familiar with the major data warehousing platforms, such as Amazon Redshift, Google BigQuery, or Snowflake.

Common Data Engineering Tools

The Apache Software Foundation has staked its claim as the preeminent provider of open-source data engineering tools and platforms. Their offerings include:

  • Apache Airflow: An open-source platform for orchestrating complex workflows, particularly useful for managing data pipelines.
  • Apache Kafka: A distributed streaming platform for building real-time data pipelines and streaming applications.
  • Apache NiFi: A data integration tool for automating the flow of data between systems.
  • Apache Spark: A fast and versatile cluster computing framework for big data processing.
  • Apache Hadoop: A framework for distributed storage and processing of large datasets.
  • Apache Flink: A framework and distributed processing engine for stateful computations over unbounded and bounded data streams.

In addition to these tools, data engineers use cloud-based services such as AWS Glue, Azure Data Factory, and Google Cloud Dataflow for data integration, ETL, and data pipeline orchestration.

As previously mentioned, data engineering involves either relational databases (e.g., MySQL, PostgreSQL) for structured data or NoSQL databases (e.g., MongoDB, Cassandra) for unstructured or semi-structured data.

Data engineers may work with query engines like Dremio Sonar, Spark, Flink, and others. And they’ll likely use Git to track code changes and collaborate with team members. 

Data engineering solutions can also involve Docker, the most popular containerization platform for packaging and deploying applications, and Kubernetes, the most-used container orchestration platform for automating the deployment, scaling, and management of containerized applications.

The specific tools and skills that data engineers need may vary depending on the organization’s technology stack and requirements. Data engineers often work with a combination of these tools to build robust and scalable data pipelines.