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Optimization Strategies for Iceberg Tables

Cloudera

This blog discusses a few problems that you might encounter with Iceberg tables and offers strategies on how to optimize them in each of those scenarios. Problem with too many snapshots Everytime a write operation occurs on an Iceberg table, a new snapshot is created. See Write properties.

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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Systems of this nature generate a huge number of small objects and need attention to compact them to a more optimal size for faster reading, such as 128 MB, 256 MB, or 512 MB. As of this writing, only the optimize-data optimization is supported. Note the last four newly added configurations in the following statement.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 2

AWS Big Data

We’ve already discussed how checkpoints, when triggered by the job manager, signal all source operators to snapshot their state, which is then broadcasted as a special record called a checkpoint barrier. When barriers from all upstream partitions have arrived, the sub-task takes a snapshot of its state.

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CRM’s Have a Big Data Technical Debt Problem: Here’s How to Fix It

Smart Data Collective

Metazoa is the company behind the Salesforce ecosystem’s top software toolset for org management, Metazoa Snapshot. Created in 2006, Snapshot was the first CRM management solution designed specifically for Salesforce and was one of the first Apps to be offered on the Salesforce AppExchange. What is technical debt anyway?

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In-place version upgrades for applications on Amazon Managed Service for Apache Flink now supported

AWS Big Data

Some things to keep in mind: Stateful downgrades are not compatible and will not be accepted due to snapshot incompatibility. Validation of the state snapshot compatibility happens when the application attempts to start in the new runtime version. You don’t need to create a new application in order to upgrade in-place.

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AI transforms the IT support experience

IBM Big Data Hub

When a system reports a potential problem, it transmits essential technical detail including extended error information, such as error logs and system snapshots. Even when topics come up that the virtual assistants can’t solve on its own, automation can easily connect clients with a live agent who can help.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. It will pre-populate the properties as shown in the following screenshot.

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