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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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15 Awesome Features Should Present in Big Data System

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Really good post. I have given useful points on the features of big data system. If there are no right features, you will miss the benefits that you get from big data.

What does traditional BI tools....

Read next step...

Traditional tools quickly can become overwhelmed by the large volume of big data. Latency—the time it takes to access the data—is as an important a consideration as volume.

A little difference is there...

Suppose you might need to run an ad hoc query against the large data set or a predefined report.

A large data storage system is not a data warehouse, however, and it may not respond to queries in a few seconds. It is, rather, the organization-wide repository that stores all of its data and is the system that feeds into the data warehouses for management reporting.
Big data top components
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Big data needs to be considered in terms of how the data will be manipulated. The size of the data set will impact data capture, movement, storage, processing, presentation, analytics, reporting, and latency.

Key features of Big data system
  1. A method of collecting and categorizing data
  2. A method of moving data into the system safely and without data loss
  3. A storage system that is distributed across many servers
  4. Is scalable to thousands of servers
  5. Will offer data redundancy and backup
  6. Will offer redundancy in case of hardware failure
  7. Will be cost-effective
  8. A rich tool set and community support
  9. A method of distributed system configuration
  10. Parallel data processing
  11. System-monitoring tools
  12. Reporting tools: ETL-like tools (preferably with a graphic interface) that can be used to build tasks that process the data and monitor their progress
  13. Scheduling tools to determine when tasks will run and show task status
  14. The ability to monitor data trends in real time
  15. Local processing where the data is stored to reduce network bandwidth usage 
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