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Happy Birthday, CDP Public Cloud

Cloudera

In the beginning, CDP ran only on AWS with a set of services that supported a handful of use cases and workload types: CDP Data Warehouse: a kubernetes-based service that allows business analysts to deploy data warehouses with secure, self-service access to enterprise data. That Was Then. New Services.

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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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Best Web Analytics 2.0 Tools: Quantitative, Qualitative, Life Saving!

Occam's Razor

If after rigorous analysis you have determined that you have evolved to a stage that you need a data warehouse then you are out of luck with Yahoo! If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. and Google, get a paid solution.

Analytics 136
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

AWS Database Migration Service (AWS DMS) is used to securely transfer the relevant data to a central Amazon Redshift cluster. The data in the central data warehouse in Amazon Redshift is then processed for analytical needs and the metadata is shared to the consumers through Amazon DataZone.

IoT 91
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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Spark SQL is an Apache Spark module for structured data processing. OutputValue" --output text) Connect to the demo EKS cluster: echo `aws cloudformation describe-stacks --stack-name $stack_name --query "Stacks[0].Outputs[?starts_with(OutputKey,'eksclusterEKSConfig')].OutputValue"

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

With these features, you can now build data pipelines completely in standard SQL that are serverless, more simple to build, and able to operate at scale. Typically, data transformation processes are used to perform this operation, and a final consistent view is stored in an S3 bucket or folder.

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Unlock scalable analytics with AWS Glue and Google BigQuery

AWS Big Data

Efficiency : Data transformation tasks that previously took weeks or months can now be accomplished within minutes, optimizing efficiency. Before you can store data in Amazon S3, you must create an S3 bucket to store the results. Enter a globally unique Name for your bucket; for example, awsglue-demo.

Analytics 101