Difference between partitioning and bucketing. Types of joins in spark Optimization Techniques in spark Broadcast variable and broadcast join Difference between ORC and Parquet Difference between RDD and Dataframe Architecture of our project and Day-to-day activities and responsibilities

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Explaining partitioning, bucketing, joins, optimization, broadcast variables, ORC vs Parquet, RDD vs Dataframe, project architecture and responsibilities for Big Data Engineer role.

Partitioning is dividing data into smaller chunks for parallel processing, while bucketing is organizing data into buckets based on a hash function.
Types of joins in Spark include inner join, outer join, left join, right join, and full outer join.
Optimization techniques in Spark include caching, repartitioning, and using broadcast variables.
Broadcast variable is a read-only variable cached on each machine, used for sharing large read-only data across tasks. Broadcast join is a join operation that uses broadcast variables to optimize performance.
ORC and Parquet are columnar storage formats used in Hadoop ecosystem. ORC is optimized for reading large datasets, while Parquet is optimized for both reading and writing.
RDD (Resilient Distributed Datasets) is a fundamental data structure in Spark, while DataFrame is a distributed collection of data organized into named columns.
Project architecture and day-to-day activities and responsibilities may vary depending on the specific project and company.

Difference between partitioning and bucketing. Types of joins in spark Optimization Techniques in spark Broadcast variable and broadcast join Difference between ORC and Parquet Difference between RDD and Dataframe Architecture of our project and Day-to-day activities and responsibilities

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