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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
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Top Hive interview Questions for quick read (1 of 2)

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The selected interview questions on HIVE. Hive is a technology being used in Hadoop eco system. 1) What are major activities in Hadoop eco system? Within the Hadoop ecosystem, HDFS can load and store massive quantities of data in an efficient and reliable manner. It can also serve that same data back up to client applications, such as MapReduce jobs, for processing and data analysis. 2)What is the role of HIVE in HADOOP Eco system? Hive, often considered the Hadoop data warehouse platform, got its start at Facebook as their analyst struggled to deal with the massive quantities of data produced by the social network. Requiring analysts to learn and write MapReduce jobs was neither productive nor practical. Stockphotos.io 3)What is Hive in Hadoop? Facebook developed a data warehouse-like layer of abstraction that would be based on tables. The tables function merely as metadata, and the table schema is projected onto the data, instead of actually moving potentially massive set
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