Apache Hive – Data Warehousing Course in Chennai

Module 1: Introduction to Apache Hive and Big Data

• Big data ecosystem overview: Hadoop, HDFS, and MapReduce
• Apache Hive architecture: Metastore, HiveServer2, and execution engines
• Hive vs traditional RDBMS: differences and appropriate use cases
• Setting up Hive on Hadoop cluster and connecting with Beeline

Module 2: HiveQL Fundamentals

• Creating databases, tables, and managed vs external tables
• Data types: primitive, complex (array, map, struct), and union
• Loading data: LOAD DATA, INSERT INTO, and CREATE TABLE AS SELECT
• Basic queries: SELECT, WHERE, GROUP BY, HAVING, and ORDER BY

Module 3: Partitioning and Bucketing

• Static and dynamic partitioning for query performance
• Partition pruning and its impact on query execution
• Bucketing: configuring bucket counts and clustered by columns
• Sorted bucketing for optimised join and aggregation performance

Module 4: File Formats and Compression

• File formats: TextFile, ORC, Parquet, Avro, and SequenceFile
• ORC and Parquet: columnar storage benefits and configuration
• Compression codecs: Snappy, Gzip, and Zstd for Hive tables
• Converting existing tables to ORC and Parquet formats

Module 5: Advanced HiveQL

• Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, and LEAD
• Complex data type operations: LATERAL VIEW and EXPLODE
• Subqueries and common table expressions (CTEs)
• User-Defined Functions (UDFs): writing and registering custom functions

Module 6: Hive Performance Optimisation

• Query execution plans: EXPLAIN and interpreting execution stages
• Tez and Spark execution engine configuration
• Cost-Based Optimizer (CBO) and statistics collection
• Join optimisations: map joins, bucket map joins, and skew joins

Module 7: Hive Integration and Security

• Hive integration with Spark, HBase, and Kafka
• Apache Ranger for Hive row and column-level security
• Hive ACID transactions: insert, update, and delete operations
• Hive Metastore as a shared schema registry across tools

Module 8: Capstone Project and Assessment

• Data warehouse design and HiveQL implementation project
• Partitioned ORC table optimisation and query performance analysis
• ETL pipeline development using Hive and Hadoop ecosystem
• Final assessment and course certification

Experience in the Industry Learn from big data engineers who have designed Hive-based data warehouses and ETL pipelines for enterprises processing petabyte-scale datasets in Hadoop, Cloudera CDH, and Hortonworks HDP environments.

Backgrounds at the Top Our Apache Hive trainers have worked at telecommunications companies, retail giants, and financial institutions building large-scale analytical data platforms where Hive is the primary SQL interface for big data.

Clear & Effective Teaching Hive architecture, HiveQL, partitioning, bucketing, file formats, query optimisation, ACID transactions, and security are explained with practical big data warehouse examples and performance tuning guidance.

Hands-On Learning Focus Students create partitioned Hive tables, convert to ORC format, write complex analytical queries, tune query performance, and build ETL pipelines through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Apache Hive releases, Hive on Tez and Spark execution engines, Hive LLAP for interactive querying, and integration with modern data lakehouse platforms.