Hive in Depth Training and Interview Preparation course

Learn how Hive is not a database but a SQL-like data warehousing layer on Hadoop, enabling batch analysis on HDFS or S3 with MapReduce, Tez, or Spark.

Explore Apache Hive from basics to architecture, including MapReduce and Tez execution, storage options, metastore insights, and OLTP considerations, along with practical interview questions.

Learn how to connect to Hive using cli, Beeline, Knox, and Thrift server. Master session parameters with hivevar, hiveconf, hiverc, and run commands from shell, files, and hdfs.

Learn to connect to hive with hive cli, beeline, knox, thrift, and odbc/jdbc; manage namespaces, persist settings in .hiverc, and run commands with hive -e or -f.

Explore Hive data types, including primitive and collection types like struct, map, array, and union, plus text file encoding and the trade-offs of schema on read versus schema on write.

Explore implicit casting and potential data loss when casting to smaller types, and learn Hive collection types—struct, map, and array—with default column delimiters and schema on read and write.

Learn data definition in Hive, including creating, dropping, and describing databases and tables, using if not exists, and specifying table locations.

Explore data definition in Hive: describe commands, create table scripts, and distinctions between managed and external tables, with partitioning and metadata considerations.

Compare static and dynamic partitioning in Hive, defining partition keys and enabling dynamic partitions at runtime. Learn how partitioning improves query performance and supports add, drop, and alter operations.

Explore Hive interview questions covering database types, creating databases with location, loading data with local vs. dfs, internal vs. external tables, partitions, and common table management commands.

Learn how to load data in Hive from local file systems and offices, manage partitions, use overwrite and query-based loads, and export or migrate data.

Explore Hive data loading options, including local file loading, insert-select methods, dynamic partitions, and exporting query results, with CTAS and file formats.

Learn Hive ql and queries by mastering select-from-where constructs, various joins, and aggregate functions. Apply map-side joins, distribution by, sort by, limit, and union concepts for efficient data processing.

Explore Hive interview questions on column prefix queries, aggregate functions, map-side aggregation, header skipping, explode, and join types, including inner and left semi joins.

Explore how views in Hive act as logical constructs that do not store data, save queries like tables, hide complexity, and restrict data access with create view and drop view.

Explore how Hive views save queries, do not store data, hide complexity, restrict access, and how to list or drop them with show tables and drop view if exists.

Master Hive schema design for performance with partitioning and bucketing. Compare normalization and denormalization, avoid multi-pass queries using common table expressions, and understand columnar storage with compression.

Explore Hive schema design essentials, including data access patterns, normalization, partitioning, bucketing, evolving schemas, and columnar tables to optimize performance.

Explore how to tune hive queries using explain plans, limits, and joins, manage data skew, enable statistics and cost-based optimization, and apply vectorized and local execution for faster queries.

Explore Hive interview questions on tuning, predicates pushdown, bucketing, data skew, map joins, and performance optimization, including memory, parallelism, and file formats.