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Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries

AWS Big Data

Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making. However, as data volumes continue to grow, optimizing data layout and organization becomes crucial for efficient querying and analysis.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle.

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Deliver decompressed Amazon CloudWatch Logs to Amazon S3 and Splunk using Amazon Data Firehose

AWS Big Data

You can see the decompressed data has metadata information such as logGroup , logStream , and subscriptionFilters , and the actual data is included within the message field under logEvents (the following example shows an example of CloudTrail events in the CloudWatch Logs).

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads. Track models and drive transparent processes.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

This method uses GZIP compression to optimize storage consumption and query performance. You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Choose Run.

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Enhance your analytics embedding experience with the new Amazon QuickSight JavaScript SDK

AWS Big Data

Additionally, there are major rewrites to deliver developer-focused improvements, including static type checking, enhanced runtime validation, strong consistency in call patterns, and optimized event chaining. The following eventNames and eventCodes are returned as part of the onChange callback when there is a change in the SDK code status.