Remove Data Processing Remove Interactive Remove Metadata Remove Structured Data
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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

AWS Glue crawls both S3 bucket paths, populates the AWS Glue database tables based on the inferred schemas, and makes the data available to other analytics applications through the AWS Glue Data Catalog. Athena is used to run geospatial queries on the location data stored in the S3 buckets. Choose Run.

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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Spark SQL is an Apache Spark module for structured data processing. FINRA centralizes all its data in Amazon Simple Storage Service (Amazon S3) with a remote Hive metastore on Amazon Relational Database Service (Amazon RDS) to manage their metadata information. or later installed. OutputKey=='HiveSecretName'].OutputValue"

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Customer 360 (C360) provides a complete and unified view of a customer’s interactions and behavior across all touchpoints and channels. This view is used to identify patterns and trends in customer behavior, which can inform data-driven decisions to improve business outcomes. Then, you transform this data into a concise format.

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The new challenges of scale: What it takes to go from PB to EB data scale

CIO Business Intelligence

Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth. Consider data types. Evaluate data across the full lifecycle.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets. The following diagram shows the high-level data platform architecture before the optimizations.