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• date: 22 August 2026
• posted by: voska89

Master Hadoop, PySpark, Kafka, Delta Lake, and Cloud Data Pipelines with 60+ Hands‑On Lectures

What you'll learn

⚡ Master Apache Hadoop and HDFS – understand distributed storage, HDFS commands, and data formats (Parquet, Avro, ORC).
⚡ Become proficient in PySpark – write DataFrames, Spark SQL, RDDs, and user‑defined functions; tune Spark jobs with Catalyst and Adaptive Query Execution.
⚡ Build real‑time streaming pipelines using Apache Kafka and Spark Structured Streaming, with exactly‑once semantics and checkpointing.
⚡ Work with NoSQL databases – Cassandra, MongoDB, and Neo4j – and choose the right one for each use case.
⚡ Use Delta Lake for ACID transactions, time travel, and reliable data lakes.
⚡ Deploy Big Data workloads on AWS, GCP, and Azure (EMR, Dataproc, BigQuery, Synapse)
⚡ Orchestrate pipelines with Apache Airflow and monitor Spark applications.
⚡ Apply machine learning at scale with Spark MLlib (classification, regression, recommendation with ALS).
⚡ Analyse graphs with GraphFrames and build real‑world projects.
⚡ Prepare for interviews with theory questions, coding challenges, and a full mock interview.

Requirements

❗ Basic Python programming (functions, loops, lists, and basic libraries). No prior Big Data experience is required.
❗ Basic SQL (SELECT, JOIN, GROUP BY, aggregations). You'll review and use SQL throughout the course.
❗ A computer with at least 8 GB RAM and the ability to run Docker. The course includes a complete setup guide for a local Big Data sandbox using Docker, so you don't need to install any tools manually.
❗ A willingness to learn by doing – you'll type every line of code, complete exercises, and build projects.

Description

Big Data Bootcamp 2026 is the most comprehensive, hands‑on course for aspiring data engineers and analysts who want to build production‑grade Big Data pipelines. In this course, you will master the complete modern data stack, from distributed storage and processing to real‑time streaming, NoSQL, and cloud integration. You'll learn by doing: every lecture includes clear concept explanations, live PySpark code, deeper insights, and exercises with solutions.
You'll start with the fundamentals of Big Data and its ecosystem before setting up a full Docker‑based sandbox with Hadoop, Spark, Kafka, Hive, and Jupyter. Then you'll dive deep into Apache Spark – RDDs, DataFrames, Spark SQL, performance tuning, and Structured Streaming. You'll build real‑time pipelines with Kafka, explore NoSQL databases like Cassandra, MongoDB, and Neo4j, and learn how to deploy solutions on AWS, GCP, and Azure.
But this course goes beyond theory: you'll complete 10+ real‑world projects, including a data lake, batch ETL, sentiment analysis with MLlib, a recommendation engine, a fraud detection system, and a full‑scale data warehouse. You'll also prepare for interviews with dedicated theory questions, coding challenges, and a mock interview.
By the end, you'll have the skills, portfolio, and confidence to land a Big Data Engineer role. Whether you're a beginner or an experienced developer, this bootcamp gives you the exact toolkit used by top tech companies in 2026.
What you'll learn: Hadoop HDFS, PySpark, Spark SQL, Kafka, Spark Streaming, Delta Lake, Hive, NoSQL, Cloud Data Services, Airflow, MLlib, GraphFrames, and much more.

Who this course is for

⭐ Aspiring Data Engineers who want to break into the industry and build a strong foundation in Big Data technologies.
⭐ Software Developers looking to add distributed computing and real‑time processing skills to their toolkit.
⭐ Data Analysts and Scientists who need to process large‑scale datasets efficiently and move beyond single‑machine tools
⭐ Students and IT Professionals preparing for Big Data certifications (Databricks, AWS, GCP, Azure) or technical interviews.
⭐ Anyone who wants to understand how modern data platforms work and gain hands‑on experience with industry‑standard tools.

Homepage

https://www.udemy.com/course/big-data-bootcamp-2026-updates-from-zero-to-data-engineer

   
   
   

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