Remove Data Architecture Remove Document Remove Metadata Remove Snapshot
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Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. For example, Amazon DynamoDB provides a feature for streaming CDC data to Amazon DynamoDB Streams or Kinesis Data Streams.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. Non-blocking automatic table services (for example, compaction) that don’t impact writers or readers.

Data Lake 113
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A Summary Of Gartner’s Recent Innovation Insight Into Data Observability

DataKitchen

Data Observability leverages five critical technologies to create a data awareness AI engine: data profiling, active metadata analysis, machine learning, data monitoring, and data lineage. Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis.

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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

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