Read the notes, then try the practice. It adapts as you go.When you're ready.
Session Length
~17 min
Adaptive Checks
15 questions
Transfer Probes
8
Data engineering is the discipline of designing, building, and maintaining the systems and infrastructure that enable organizations to collect, store, process, and analyze large volumes of data. Data engineers create the pipelines and architectures that transform raw data from diverse sources into clean, reliable, and accessible formats for data scientists, analysts, and business stakeholders. The field sits at the intersection of software engineering, database administration, and distributed systems, requiring practitioners to master a broad set of tools and paradigms.
The rise of big data, cloud computing, and real-time analytics has made data engineering one of the most critical roles in modern technology organizations. Where earlier data workflows relied on simple relational databases and nightly batch jobs, today's data engineers must orchestrate complex ecosystems that include data lakes, streaming platforms like Apache Kafka, distributed processing frameworks like Apache Spark, and cloud-native services from AWS, Google Cloud, and Azure. Concepts such as ETL (Extract, Transform, Load), ELT, data modeling, schema design, and data governance form the core of the discipline.
Data engineering continues to evolve rapidly with trends such as the data lakehouse architecture, which merges the best qualities of data lakes and data warehouses; the rise of dbt for analytics engineering; real-time streaming architectures; and the growing importance of data quality, observability, and lineage. Understanding data engineering fundamentals is essential not only for aspiring data engineers but also for data scientists, machine learning engineers, and anyone who works with data at scale.
One step at a time.
A data integration pattern where data is first extracted from source systems, then transformed into a suitable format (cleaning, aggregating, enriching), and finally loaded into a target data store such as a data warehouse.
Example: A retail company extracts daily sales records from its point-of-sale system, transforms the data by standardizing currencies and removing duplicates, then loads the cleaned data into a Snowflake warehouse for reporting.
An automated sequence of processing steps that moves data from one or more sources through transformations to a destination system. Pipelines can run on a schedule (batch) or continuously (streaming) and are the backbone of data engineering workflows.
Example: An e-commerce company runs an Apache Airflow pipeline that, every hour, pulls clickstream logs from S3, joins them with user profiles from PostgreSQL, computes session metrics, and writes results to a BigQuery table for the analytics team.
A centralized, schema-enforced repository optimized for analytical queries and reporting. Data warehouses use columnar storage, indexing, and pre-aggregation to deliver fast read performance on structured, historical data.
Example: Snowflake, Amazon Redshift, and Google BigQuery are cloud data warehouses that let analysts run complex SQL queries across terabytes of structured data within seconds.
A large-scale storage system that holds raw data in its native format, including structured, semi-structured, and unstructured data. Data lakes use cheap object storage and defer schema enforcement to the time of reading (schema-on-read).
Example: A media company stores raw JSON event logs, CSV files, Parquet datasets, images, and video metadata in an Amazon S3 data lake, allowing different teams to query and process the data with their own tools.
A data processing model in which data is collected over a period of time and then processed as a group. Batch jobs typically run on a schedule and are well-suited for large-volume, latency-tolerant workloads.
Example: A bank runs a nightly Apache Spark batch job that processes the day's transactions to compute account balances, detect suspicious patterns, and generate regulatory reports.
A data processing model where records are processed individually or in micro-batches as soon as they arrive, enabling near-real-time analytics and event-driven architectures. Technologies like Apache Kafka, Apache Flink, and Spark Structured Streaming power this paradigm.
Example: A ride-sharing app uses Apache Kafka and Flink to process GPS pings from drivers in real time, updating estimated arrival times and dynamically adjusting surge pricing within seconds.
The process of defining how data is structured, related, and stored in a database or warehouse. Common approaches include dimensional modeling (star and snowflake schemas), normalized modeling (3NF), and Data Vault, each optimized for different query and ingestion patterns.
Example: A data engineer designs a star schema for a sales data warehouse with a central fact_sales table linked to dimension tables for products, customers, stores, and dates.
The automated coordination and scheduling of data workflows across multiple systems and tasks, including dependency management, retries, alerting, and monitoring. Orchestrators ensure pipelines run in the correct order and handle failures gracefully.
Example: Apache Airflow orchestrates a nightly workflow that first ingests data from an API, then runs a dbt transformation model, triggers a data quality check, and finally refreshes a dashboard, with Slack alerts on any failures.
More terms are available in the glossary.
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