Common Data Engineering Tasks

Data engineering involves a range of common tasks that fill the days of data engineers. These activities are centered around building, maintaining, and optimizing systems that handle data, ensuring it is reliable, accessible, and ready for use by analysts, data scientists, and applications like AI models. Data engineers are the builders and plumbers of data, ensuring it flows smoothly and arrives clean and usable where needed.

Here are some of the most common tasks performed by data engineers:

Designing and Building Data Pipelines

This is often considered the core responsibility. Data engineers design and construct the pathways, known as data pipelines, that automate the movement and transformation of data. This involves:

• Extracting data from various sources like databases, application logs, APIs (Application Programming Interfaces), or external vendors.
• Transforming the raw data by cleaning it (handling missing values, correcting errors), structuring it (parsing JSON or XML), enriching it (joining with other data), and converting it into a suitable format for analysis or storage.
• Loading the processed data into a target system, which could be a database, a data warehouse for reporting, or a data lake for large-scale storage. You'll often hear the acronyms ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) used to describe these pipeline patterns. We'll explore these in more detail in Chapter 3.

Data Ingestion and Collection

Before data can be moved or transformed, it needs to be brought into the system. Data engineers set up processes to collect data from its origin points. This might involve writing scripts to pull data from an API at regular intervals, configuring tools to stream data from sensors in real-time, or setting up connections to replicate data from production databases. The goal is to reliably capture the necessary data with minimal impact on the source systems.

Managing Data Storage

Data needs a place to live. Data engineers are responsible for selecting, implementing, and managing various data storage solutions. This includes:

• Working with traditional relational databases (like PostgreSQL or MySQL) for structured data.
• Utilizing NoSQL databases (like MongoDB or Cassandra) for more flexible data structures or high-volume transactions.
• Setting up and maintaining data warehouses (like Snowflake, BigQuery, or Redshift) optimized for analytical queries.
• Organizing data lakes (often using technologies like Apache Hadoop HDFS or cloud storage like Amazon S3 or Google Cloud Storage) to store enormous amounts of raw data in various formats. Choosing the right storage system depends on factors like the type of data, how it will be accessed, performance requirements, and cost. We cover storage options in Chapter 4.

Data Cleaning and Transformation

Raw data is rarely perfect. It might have errors, inconsistencies, missing values, or be in a format that's difficult to work with. Data engineers write code (often using SQL, Python, or specialized tools) to clean, standardize, and reshape the data into a consistent and usable state. This ensures that data analysts and data scientists can trust the data they are working with. This step is fundamental for accurate reporting and reliable AI models.

Automating and Orchestrating Workflows

Manually running data pipelines is inefficient and prone to errors. Data engineers use workflow management tools (like Apache Airflow or Prefect) to schedule, automate, and monitor data pipelines. This ensures that data is processed regularly and reliably, and that any failures are detected and can be addressed quickly. Think of these tools as the conductors of the data orchestra, making sure every part runs at the right time.

Monitoring, Troubleshooting, and Optimization

Data pipelines and storage systems need constant attention. Data engineers monitor system performance, data quality, and pipeline execution. When things go wrong, a pipeline fails, data looks incorrect, or a system slows down, they investigate the root cause and implement fixes. They also work on optimizing pipelines and queries to run faster and consume fewer resources, which is especially important when dealing with large datasets.

Infrastructure Management

Data engineering tasks run on computing infrastructure. This might involve managing servers, working with cloud platforms (like AWS, Google Cloud, or Azure), and configuring the software needed for data processing and storage. While some organizations have dedicated infrastructure teams, data engineers often need a good understanding of the underlying systems.

Collaboration

Data engineers don't work in isolation. They collaborate closely with:

• Data Analysts and Scientists: To understand their data requirements and provide them with the clean, structured data they need.
• Software Engineers: To integrate data collection mechanisms into applications.
• Business Stakeholders: To understand the goals that the data systems need to support.

The following diagram illustrates how these tasks fit together in a typical data flow:

A simplified view of data moving from sources through engineering processes to end users.

These tasks collectively ensure that an organization's data is transformed from its raw, often messy state into a valuable asset that can drive insights and power applications. As you progress through this course, you'll learn more about the concepts and tools used to perform these activities effectively.