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When Timing Goes Wrong: How Latency Issues Cascade Into Data Quality Nightmares

DataKitchen

Premature snapshots capture partial states, and overlapping batch windows intermingle different versions of the same data, creating temporal chaos. Customer records updated in your CRM don’t immediately reflect in your analytics warehouse. These synchronization gaps create a fragmented view of reality across your data ecosystem.

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

Metadata layer Contains metadata files that track table history, schema evolution, and snapshot information. In many operations (like OVERWRITE, MERGE, and DELETE), the query engine needs to know which files or rows are relevant, so it reads the current table snapshot. This is optional for operations like INSERT.

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Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

KDnuggets

Most academic datasets pale in comparison to the complexity and volume of user interactions in real-world environments, where data is typically locked away inside companies due to privacy concerns and commercial value. Its static snapshot and lack of detailed metadata limit modern applicability. That’s beginning to change.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Iceberg provides time travel and snapshotting capabilities out of the box to manage lookahead bias that could be embedded in the data (such as delayed data delivery). Icebergs time travel capability is driven by a concept called snapshots , which are recorded in metadata files.

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Introducing the new Amazon Kinesis source connector for Apache Flink

AWS Big Data

Apache Flink connectors Flink supports reading and writing data to external systems, through connectors , which are components that allow your application to interact with stream-storage message brokers, databases, or object stores. Restart from the latest snapshot (default behavior) and select allowNonRestoredState = true.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning.

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RocksDB 101: Optimizing stateful streaming in Apache Spark with Amazon EMR and AWS Glue

AWS Big Data

When interacting with S3, RocksDB is designed to improve checkpointing efficiency; it does this through incremental updates and compaction to reduce the amount of data transferred to S3 during checkpoints, and by persisting fewer large state files compared to the many small files of the default state store, reducing S3 API calls and latency.