Every tech book retold chapter by chapter.
Master the world of big data with this comprehensive guide to Hadoop 3, Spark, Flink, and the AWS cloud ecosystem.
Master the art of building scalable, professional big data platforms using Kubernetes and open-source tools.
Laura Funderburk's hands-on guide to building production-grade NLP and LLM pipelines with Haystack and LangGraph, covering RAG, tool contracts, context engineering, and agentic AI architecture.
Master financial data analytics with this comprehensive guide to using Python for stock market prediction and risk management.
Chisom Nwokwu's beginner-friendly guide to data engineering covering databases, SQL, pipelines, cloud platforms, and career building.
Adi Wijaya's practical guide to building scalable data pipelines and platforms using Google Cloud Platform services like BigQuery, Dataproc, Dataflow, and Cloud Composer.
Paul Crickard's hands-on guide to building data pipelines with Python, covering ETL, NiFi, Airflow, Kafka, Spark, and production deployment.
Stephen Mariadas and Ian Huke's beginner-friendly guide to data science covering the full lifecycle from problem definition to machine learning and AI.
Ravi Mishra's comprehensive guide to Terraform covering IaC basics, multi-cloud automation across AWS, Azure, and GCP, and preparation for the HashiCorp Terraform Associate certification exam.
Bhagvan Kommadi's guide to learning data structures and algorithms using Go, covering everything from basic arrays to garbage collection.
Diana Montalion's practical guide to systems thinking for software professionals, covering nonlinear reasoning, feedback loops, and collaborative system design.
Rick J. Scavetta and Boyan Angelov's guide to using Python and R together for modern data science instead of picking sides.