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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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Apache HIVE Top Features

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Apache Hive aids the examination of great datasets kept in Hadoop’s HDFS and harmonious file setups such as the Amazon S3 filesystem.


Apache HIVE Top Features


It delivers an SQL-like lingo named when keeping complete aid aimed at map/reduce. To accelerate requests, it delivers guides, containing bitmap guides.

By preset, Hive stores metadata in an implanted Apache Derby database, and different client/server databases like MySQL may optionally be applied.

Currently, there are 4 file setups maintained in Hive, which are TEXTFILE, SEQUENCE FILE, ORC, and RCFILE.

 Other attributes of Hive include:
  • Indexing to supply quickening, directory sort containing compacting, and Bitmap directory as of 0.10, further directory kinds are designed.
  • Different depository kinds such as simple written material, RCFile, HBase, ORC, and other ones.
  • Metadata depository in an RDBMS, notably decreasing the time to accomplish verbal examines throughout request implementation.
  • Operating on compressed information kept into the Hadoop environment, set of rules containing gzip, bzip2, snappy, etcetera.
  • Built-in exploiter described purposes (UDFs) to manipulate dates, cords, and different data-mining implements. Hive aids expanding the UDF set to cover use-cases not maintained by integrated purposes.
  • SQL-like requests (Hive QL), that are completely changed into map-reduce appointments.

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