The Data Engineer Bootcamp 2026

Define data architecture as the framework for data integration, storage, security, compliance, and reliable pipelines, balancing cost, access, and governance for stakeholders.

Explore a practical data architecture with sources, a bucket, a data warehouse, and a data mart, and learn how OLTP versus OLAP, CSV, SFTP, and Airflow orchestrate ingestion and access.

Explore data lakes, data warehouses, and data marts, their use cases, features, and differences. Learn how these structures support analytics and business intelligence, with costs and data democratization.

Explore the modern data stack as a cloud-based, ELT-biased, open-source, component-based approach. Learn how pay-as-you-go pricing, data warehouses, and connectors shape scalable data pipelines.

Connect to data from emails, databases, external services, or desktops by standardizing intake into a schema-enabled data warehouse, ensuring consistency and loudly alerting failures.

Explore key principles of good data architecture, including choosing common components wisely, planning for failure, scalability, leadership, loosely coupled systems, reversible decisions, security, and FinOps.

Data orchestration coordinates, schedules, and runs data workflows, enabling dependency management, automation, and compliance and security with auditable data processes.

Demonstrate how Apache Airflow orchestrates complex data pipelines with DAGs, tasks, and dependencies, using Python-based DAG definitions, operators, and a scheduler and executor.

Explore relational databases as the core data engineering tool, where data resides in tables of attributes and keys, linked by primary and foreign keys, using SQL for analysis.

Explore how to organize relational databases by using servers, databases, schemas, tables, and views, and learn how foreign data wrappers enable cross-server queries.

Master unions and joins, core relational database operations that combine data from multiple tables by stacking rows or linking via a foreign key, including union versus union all.

Compare OLTP and OLAP databases: OLTP handles CRUD and fast inserts, updates, and deletes for transactional apps, while OLAP supports complex analytics, large joins, and data aggregation.

Explore ACID properties—atomicity, isolation, consistency, durability—and how they guarantee reliable transactions in relational databases, helping you compare SQL and NoSQL choices for real-world use cases.

Explore document databases, a flexible NoSQL store using JSON documents with schema-less design and horizontal scalability. Understand trade-offs with analytics and relational databases for data modeling.

Explore key-value databases, a generalized version of document databases, and learn how keys and values store flexible data, with Amazon DynamoDB as an example, for SQL-driven analysis.

Learn how object storage stores files as objects in a flat hierarchy for cheap, reliable staging between systems, with examples like Amazon S3, Azure Blob Storage, and GCP Cloud Storage.

Columnar databases optimize column storage for analytics, delivering faster data retrieval than relational databases by accessing only relevant columns, exemplified by aggregating revenue column for sales data, Cassandra DB.

Explore graph databases, mapping data as nodes, edges, and properties to model relationships in social networks and fraud detection, with examples like AWS Neptune, Neo4j, and Azure Cosmos DB.

Explore NoSQL databases by answering key questions on durability in acid properties, graph database edges linking nodes, json document formats, and the efficiency of columnar databases for analytical queries.

Compare horizontal and vertical scaling, showing when to upgrade a single machine versus add servers, and discuss cost, simplicity, and coordination in data warehousing.

Learn Python for data engineering, covering basics like variables, control flow, and functions, plus practical use with pandas and numpy and integration with Airflow and Spark.

Explore how APIs enable secure, standardized data access between systems, using rest calls—get, post, put, delete, patch—to pull JSON data into data warehouses and data marts.

Discover practical shell scripting for data engineering, mastering terminal commands such as pwd, ls, cd, and mkdir. Learn to use SSH and SCP to securely access and transfer files.

explore cron, a time-based job scheduler used in data engineering to schedule scripts with five fields for minutes, hours, day of month, month, and day of week, including practical examples.

Learn how version control with git and mercurial enables concurrent coding, branching, and easy reversion, using GitHub or GitLab with pull requests, approvals, and audit trails.

Learn how unit testing and integration testing guard the software development life cycle, reducing shipping risks and bugs. See simple examples that boost code quality and refactoring confidence.

Learn containerization and Docker, encapsulating an application and dependencies into portable containers that run consistently across environments, enabling lightweight isolation, reproducible setups, and simplified deployment for data engineering.

Scale cloud infrastructure by shifting from GUI management to infrastructure as code. Use tools like Terraform, Ansible, or AWS CDK to version, track, and ensure auditable changes.

Explore big data concepts and the data engineer's role in building scalable architectures, data lakes, and processing systems to extract business insights from data characterized by volume, velocity, and variety.

Explore Hadoop as a distributed storage and processing system using HDFS for large data sets and batch analyses, enabling data lakes and cloud options like S3.

Explore Apache Spark, the in-memory big data processing framework that enables real-time stream processing, interactive analysis, and scalable machine learning via MLlib, with interfaces like PySpark and Spark SQL.

Explore Kafka as a stream processing and event-driven backbone that decouples data ingestion from consumption across microservices and real-time pipelines, with use in log aggregation and IoT data.

Explore conceptual, logical, and physical data models and their role in mapping business processes. Learn how to implement physical models with entities, keys, and data types for data engineers.

Explore entity relationship diagrams as the universal language for database design, identify tables and columns, primary and foreign keys, and one-to-many, one-to-one, and many-to-many relationships, with a high-level overview.

Learn data normalization to reduce redundancy by separating related data into separate tables, covering first, second, and third normal forms, with examples of pivoting and joining on primary keys.

Compare top-down Inmon data warehousing with bottom-up Kimball architectures, exploring normalized data in a single warehouse versus denormalized fact and dimension tables and their practical trade-offs.

Emphasize the non-optionality of security and privacy, and weave basic protections into data models and storage while aligning with CCPA, GDPR, and PII considerations.

Identify direct and indirect PII, such as social security numbers and date of birth. Protect it with data minimization, encryption at rest and in transit, and strict access controls.

Apply the principle of least privilege by granting minimum access, use RBAC and SSO to manage roles like financial analyst, and implement just-in-time access to reduce the attack surface.

Explore intermediate SQL concepts and advanced querying techniques. Learn to create and maintain databases, schemas, and tables using PostgreSQL, with practical practice for real-world work or school settings.

Install DBeaver and PostgreSQL, connect to a local Postgres server, and use SQL Fiddle if needed, then follow simple setup steps to begin the tutorial.

Explore how relational databases organize data into tables with keys and relations, connect via joins, and contrast with NoSQL and document databases like MongoDB.

Master DQL, DDL, DML, and DCL to query, define, manipulate, and control access in SQL, using select, insert, update, delete, merge, and permissions to protect PII.

Identify keywords such as select and from, along with clauses, expressions, and predicates in a sql query. Understand binding and parsing, and how explain reveals the query plan with seqscan.