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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. For this post, set this to 8 RPUs.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots. all_reviews ): data and metadata.

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Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift

AWS Big Data

There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (e.g. With Aurora zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse.

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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

For Description , enter Parameter group for demo Aurora MySQL database. About the authors Manish Kola is a Data Lab Solutions Architect at AWS, where he works closely with customers across various industries to architect cloud-native solutions for their data analytics and AI needs. Choose Create. mode("append").save(s3_output_folder)

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Presto was able to achieve this level of scalability by completely separating analytical compute from data storage. Presto is an open source distributed SQL query engine for data analytics and the data lakehouse, designed for running interactive analytic queries against datasets of all sizes, from gigabytes to petabytes.

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Become a Financial Storyteller

Jet Global

Microsoft Excel offers flexibility, but it’s missing so many of the elements required to assemble data quickly and easily for powerful (and accurate) financial narratives. The reports created within static spreadsheets are based on a snapshot of reality, taken the moment the data was exported from ERP. Get a Demo.

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Top Financial Reporting Challenges and How to Solve Them

Jet Global

There is yet another problem with manual processes: the resulting reports only reflect a snapshot in time. As soon as you export data from your ERP software or other business systems, it’s obsolete. I'd like to see a demo of insightsoftware solutions. I understand that I can withdraw my consent at any time.