8/27/2023 0 Comments Aws glue iceberg![]() ![]() the deployment of the entire demo using Terraform.an automatic trigger (as files are written into the RAW Amazon S3 bucket), of the AWS Glue Job to convert the RDBMS transactions data into Apache Iceberg data files.an AWS DMS instance to ingest the RDBMS transaction data into Amazon S3.an Amazon Aurora PostgreSQL database as a data source.In the demo published in this GitHub repository, I propose a full end-to-end demo including: Mishra is walking us through a demo of the AWS Glue and Amazon Athena Apache Iceberg integration, assuming the database migration with AWS DMS. ![]() ![]() Time travel which enables reproducible queries.Schema evolution : supports add, drop, update, or rename.As per the documentation, Apache Iceberg offers amongst other things: Apache Iceberg is an open table format created at Netflix and donated to the Apache foundation in 2018. This is where the integration of Apache Iceberg into AWS Glue and Amazon Athena (announced GA in April 2022) can help. The ability to travel back in time in order to reproduce the results of past analysis or data science experiments?.A view of the current state of the database.On November 1 stwhat you want to see is the followingīut how do you go from that pile of transaction data into having in your data lake: Table1: Example of transactional data stored on Amazon S3 as a result of the synchronization of a fictional product table using AWS DMSīut this is not what you need to perform any data analytics. If you synchronize your database with your Amazon S3 data lake with a tool like AWS DMS (Database Migration Service), what you get in your data lake is a pile of transactional data. But how can you transfer constantly changing data from a database into a data lake based on immutable storage like Amazon S3? As mentioned by others before, as the adoption of data lakes grows together with their analytic tools, companies often face a challenge that a lot of the business data still reside on relational databases (RDBMS). ![]()
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