Hadoop for Developers Training in Astana, Kazakhstan

This hands-on training is designed for software developers and data engineers who want to build scalable data processing applications using Apache Hadoop. The course focuses on writing and optimizing MapReduce programs, understanding Hadoop’s data flow, and integrating with other ecosystem tools such as Hive, Pig, and HBase. It prepares developers to work on batch-processing pipelines and contribute effectively to big data projects.

INTRODUCTION TO HADOOP AND MAPREDUCE PROGRAMMING

  Overview of Hadoop architecture: HDFS, YARN, and MapReduce

  Data flow in HDFS and MapReduce execution stages

  Writing your first MapReduce program in Java

  InputFormat, OutputFormat, Mapper, Reducer, Combiner logic

  Working with TextInputFormat, KeyValueInputFormat, custom formats

  Running MapReduce jobs via CLI and programmatic submission

ADVANCED MAPREDUCE AND ECOSYSTEM INTEGRATION

  Chaining jobs and managing intermediate data

  Using DistributedCache and counters

  Optimizing MapReduce jobs for performance and resource usage

  Introduction to Pig: syntax, data types, UDFs, execution model

  Using Pig for ETL-style transformations

  Introduction to Hive: tables, partitions, queries, schema on read

  Writing HiveQL queries for large-scale data analysis

DATA INGESTION, FORMATS, AND REAL-WORLD PIPELINES

  Working with structured and unstructured data

  Using Avro, Parquet, and SequenceFile formats

  Ingesting external data: Sqoop basics for RDBMS to Hadoop

  Using Hadoop Streaming for non-Java developers (Python, Bash)

  Debugging and testing Hadoop applications

  Best practices in Hadoop development and job design

Hands-on Exercises