1. Use of Apache Spark - Dataframe, Datasets and RDD. 2. Transformations and Actions in Spark. 3. Optimization in Spark 4. HIVE - Dynamic and Static Partitioning. 5. HIVE - Partitioning and Bucketing. 6. SQL - Rank and Dense Rank scenario based.

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Questions related to Apache Spark, HIVE, and SQL.

Apache Spark is a distributed computing framework used for big data processing.
Dataframe, Datasets, and RDD are the core abstractions in Spark.
Transformations and Actions are the two types of operations in Spark.
Optimization techniques like caching, partitioning, and broadcasting can improve Spark performance.
HIVE is a data warehousing tool that uses SQL-like syntax to query data stored in Hadoop.
Dynamic and Static Partitioning in HIVE helps to organize data into partitions for faster querying.
Partitioning and Bucketing in HIVE are used to optimize data storage and retrieval.
Rank and Dense Rank are SQL functions used to assign a rank to each row based on a specific column.

1. Use of Apache Spark - Dataframe, Datasets and RDD. 2. Transformations and Actions in Spark. 3. Optimization in Spark 4. HIVE - Dynamic and Static Partitioning. 5. HIVE - Partitioning and Bucketing. 6. SQL - Rank and Dense Rank scenario based.

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