What You'll Learn
- Fundamentals of Hive architecture, table types, partitions, and bucketing
- Hive query optimization techniques including dynamic partitioning, joins, cost-based optimization, and predicate pushdown
- File formats and SerDe (ORC, Parquet, RegexSerDe) and their performance impact
- Integration of Hive with Hadoop ecosystem tools like Spark, Kafka, HBase, Flink, and BI platforms
- Advanced Hive features such as ACID transactions, LLAP, vectorized queries, and schema evolution
- Hive security, authentication, and access control best practices
- Scenario-based interview questions on troubleshooting, query tuning, and debugging
- Best practices for scalable Hive design and handling large datasets efficiently
Requirements
- Basic understanding of SQL and Hadoop concepts
- Prior exposure to Hive is helpful but not mandatory; the course starts with fundamentals and builds up to advanced topics
Who This Course is For
- Data Engineers and Big Data Developers preparing for technical interviews
- Hadoop ecosystem professionals working with Hive, Spark, or data warehousing tools
- Data Analysts and ETL Developers seeking to strengthen their Hive knowledge
- Anyone looking to build expertise in Hive internals, performance tuning, and best practices
Your Instructor
Bigdata Engineer
Bigdata Engineer
3.9 Instructor Rating
1,669 Reviews
145,507 Students
23 Courses
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