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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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A Quick guide to Amazon RDS

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Amazon Aurora is a MySQL-compatible relational database management system (RDBMS) that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

It provides up to 5X the performance of MySQL at one tenth the cost of a commercial database. Amazon Aurora allows you to encrypt data at rest as well as in transit for your mission-critical workloads.

Key points on Amazon Aurora


  1. Amazon Aurora is a relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. It delivers up to five times the throughput of standard MySQL running on the same hardware.
  2. Amazon Aurora is designed to be compatible with MySQL 5.6, so that existing MySQL applications and tools can run without requiring modification. 
  3. Amazon Aurora joins MySQL, Oracle, Microsoft SQL Server, and PostgreSQL as the fifth database engine available to customers through Amazon RDS. 
  4. Amazon RDS handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection, and repair. You pay a simple monthly charge for each Amazon Aurora database instance you use. There are no upfront costs or long-term commitments.

What is RDS on Amazon Aurora

Amazon RDS makes it easy to manage your Amazon Aurora database by automating most of the common administrative tasks associated with running a database. 

With a few clicks in the AWS Management Console, you can quickly launch an Amazon Aurora database instance. Amazon Aurora scales storage automatically, growing storage and rebalancing I/Os to provide consistent performance without the need for over-provisioning.

For example, you can start with a database of 10GB and have it automatically grow up to 64TB without requiring availability disruptions to resize or restripe data.

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