Python tutorials, tips, and best practices
Hands-on Delta Lake deep dive in Databricks. Every operation step by step: INSERT, UPDATE, DELETE, MERGE creating versions. Time travel three methods. Compare versions, track entities across history. RESTORE, VACUUM, Schema evolution, DeltaTable Python API.
Master Databricks to Azure SQL Database connectivity. JDBC connection setup, secure credentials with Key Vault, reading tables and custom queries, the ORDER BY subquery trap, write modes, upsert pattern, the three-notebook production architecture (Config + Functions + Operations), data quality functions, performance optimization with partitioned reads, and common JDBC errors.
Master PySpark foundations that every tutorial skips. SparkSession creation and configuration, SparkSession vs SparkContext history, every import you need, builder options, spark.conf.set vs builder config, stopping sessions, running PySpark locally, spark-submit, and environment comparison (Local vs Databricks vs Synapse).
PySpark Foundations: SparkSession, Imports, Configuration, and the Basics Nobody Teaches Read More »
The complete PySpark transformation cookbook for Databricks. Every function category with real code: column operations, filtering, withColumn, when/otherwise, string functions, date functions, null handling, aggregations (pivot, cube, rollup), window functions, joins, deduplication, complex types (arrays, structs, maps), nested JSON flattening, UDFs, and the pipeline pattern.
PySpark DataFrame Transformations in Azure Databricks: The Complete Cookbook Read More »
Master reading and writing every file format in Databricks. Standard CSV, pipe-delimited, single-quote qualifiers, escape characters, multiline values, JSON, Parquet, and Delta Lake. Covers all CSV options, writing with partitionBy, managed vs external tables, Delta operations, and a complete read-transform-write pipeline.
Master Azure Databricks from architecture to daily commands. Covers workspace setup, cluster types, notebooks, and every dbutils module: fs (file operations), secrets (Key Vault integration), widgets (parameterization), and notebook (orchestration). Plus Delta Lake operations, mounting storage, Workflows, cost management, and Databricks vs Synapse comparison.
Master Apache Spark and PySpark from architecture to code. Covers Driver-Executor model, lazy evaluation, RDDs vs DataFrames, Python vs PySpark comparison with code examples, all DataFrame operations, Spark SQL, partitioning, shuffling, broadcast joins, window functions, performance tuning, and Azure integration.
Master LLM fine-tuning from concepts to code. Covers when to fine-tune vs RAG vs prompt engineering, LoRA and QLoRA methods, step-by-step with OpenAI API and Hugging Face, training data preparation, 5 real-world scenarios, evaluation techniques, costs, and the data engineer role in AI projects.
Fine-Tuning Large Language Models: A Complete Guide for Data Engineers Read More »
The Python skills data engineers actually use: reading files (CSV, JSON, Parquet), pandas DataFrames, database connections, REST APIs, AWS/Azure SDKs, logging, error handling, and production ETL patterns.
Python for Data Engineers: The Essential Skills You Actually Use Every Day Read More »
Learn how to build and deploy a production-ready REST API using Python FastAPI on AWS Lambda with API Gateway and DynamoDB. Step-by-step guide with real-world examples.
Building a REST API with Python FastAPI on AWS Lambda: A Complete Guide Read More »
