Showing posts with the label Messages

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8 Ways to Optimize AWS Glue Jobs in a Nutshell

  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

Messages in Kafka the Types and Details

A message, also called a record, is the basic piece of data flowing through Kafka. Messages are how Kafka represents your data. Kafka producer Vs. consumer messages Kafka is an intermediate server that receives a message from a producer and sends them to the consumer. Here is a set of 10 Kafka Interview Questions. Kafka message format Each message has a timestamp, a value, and an optional key. Custom headers can be used if desired as well.  A simple example of a message could be something like the following: the machine with host ID “1234567” (a message key) failed with the message “Alert: Machine Failed” (a message value) at “2020-10-02T10:34:11.654Z” (a message timestamp). Here is Kafka's flowchart for dummies. Kafka record The above image shows probably the most important and common parts of a message that users deal with directly. Each key and value can interact in its own specific ways to serialize or deserialize its data. Now that we have a record, how do we let Kafka know ab