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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. For our testing, we generated about 58,176 small objects with total size of 2 GB.

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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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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

It also applies general software engineering principles like integrating with git repositories, setting up DRYer code, adding functional test cases, and including external libraries. In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. For more information, refer SQL models.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. Your Chance: Want to test a professional logistics analytics software? These applications are designed to benefit logistics and shipping companies alike.

Big Data 275
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Defining Simplicity for Enterprise Software as “a 10 Year Old Can Demo it”

Cloudera

During the development of Operational Database and Replication Manager, I kept telling folks across the team it has to be “so simple that a 10 year old can demo it”. No one took me seriously… until that moment during an internal sales kick-off meeting. . “so so simple that a 10 year old can demo it”. How hard is it for engineering to build?

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots.

Data Lake 116
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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data. You could first create a snapshot table, run sanity checks on the snapshot table, and ensure that everything is in order. As of this writing, the “__BACKUP__” suffix is hardcoded.