SQL Fundamentals for Data Engineers
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Key Insights
- SQL is the universal language of data.
- This beginner module covers essential SQL concepts every data engineer needs: SELECT queries, JOINs, aggregations, CTEs, window functions, and query optimization basics.
Overview
SQL (Structured Query Language) remains the most important language for data engineers. Despite the proliferation of NoSQL databases and big data technologies, SQL is the universal interface for querying and manipulating structured data. Every data engineer must master SQL to work effectively with relational databases, data warehouses, and increasingly with data lakes and streaming systems.
Modern SQL extends far beyond simple SELECT statements. Data engineers use common table expressions (CTEs), window functions, complex joins, query optimization techniques, and database-specific features to build efficient data pipelines. Understanding query execution plans and indexing strategies is essential for performance tuning at scale.
Key Concepts
- Window Functions: SQL functions that perform calculations across related rows while preserving individual row identity, enabling running totals and ranking.
- Common Table Expressions: Temporary named result sets that simplify complex queries by breaking them into readable, reusable steps.
- Query Optimization: Techniques including index usage, join ordering, and partition pruning that improve query execution speed.
- Indexing Strategies: Data structures that speed up data retrieval at the cost of write performance, including B-tree, bitmap, and hash indexes.
- ACID Compliance: Atomicity, Consistency, Isolation, Durability — the four properties that guarantee reliable database transaction processing.
Key Takeaways
- SQL is the universal language for structured data manipulation and remains essential for data engineers.
- Window functions and CTEs enable powerful query patterns that simplify complex data transformations.
- Understanding query execution plans is critical for performance optimization.
- Proper indexing strategies balance read performance against write overhead in production databases.
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1. The One-Pager
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Knowledge Check
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Hands-On Lab
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Polars: cash-flow profile by risk rating
BeginnerThe transactions table is exported to a CSV. Load it with polars and compute, for each risk_rating, the total USD cash-deposit volume. Print the result sorted by total descending. The CSV columns are: id, account_id, txn_type, amount, currency, txn_date. Customer risk ratings are in a second CSV: id, name, jurisdiction, risk_rating, is_pep. Complete this pipeline: join transactions to customers via accounts, filter cash_deposit, group by risk_rating, and print.
Pyodide + Polars are loaded from a CDN (large first download). Code runs locally; no data leaves this page.
Flashcards
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