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

AWS Big Data

Data analytics – Business analysts gather operational insights from multiple data sources, including the location data collected from the vehicles. This method uses GZIP compression to optimize storage consumption and query performance. For Data source , choose AwsDataCatalog. Choose Run.

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Sure, Trust Your Data… Until It Breaks Everything: How Automated Data Lineage Saves the Day

Octopai

By doing so, they aimed to drive innovation, optimize operations, and enhance patient care. They invested heavily in data infrastructure and hired a talented team of data scientists and analysts. Solving the data lineage problem directly supported their data products by ensuring data integrity and reliability.

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

AWS Big Data

You can use Amazon Data Firehose to aggregate and deliver log events from your applications and services captured in Amazon CloudWatch Logs to your Amazon Simple Storage Service (Amazon S3) bucket and Splunk destinations, for use cases such as data analytics, security analysis, application troubleshooting etc.

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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. Raj focuses on helping customers develop sample dashboards, embed analytics and adopt BI design patterns and best practices.

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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

6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes. This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team.

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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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