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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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Top Key Architecture Components in HIVE

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5 architectural components present in Hadoop Hive: Shell: allows interactive queries like MySQL shell connected to a database – Also supports web and JDBC clients Driver: session handles, fetch, execute Compiler: parse, plan, optimize Execution engine: DAG of stages (M/R, HDFS, or metadata) Metastore: schema, location in HDFS, SerDe

 Data Mode of Hive:
  • Tables
– Typed columns (int, float, string, date, boolean)
– Also, list: map (for JSON-like data)
  • Partitions
– e.g., to range-partition tables by date
  • Buckets
– Hash partitions within ranges (useful for sampling, join optimization)

HIVE Meta Store
  • Database: namespace containing a set of tables
  • Holds table definitions (column types, physical layout)
  • Partition data 
  • Uses JPOX ORM for implementation; can be stored in Derby, MySQL, many other relational databases
Physical Layout of HIVE
  • Warehouse directory in HDFS
– e.g., /home/hive/warehouse
  • Tables stored in subdirectories of warehouse
– Partitions, buckets form subdirectories of tables
  • Actual data stored in flat files
– Control char-delimited text, or SequenceFiles
– With custom SerDe, can use arbitrary format

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