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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. This will be your OLTP data store for transactional data. version cluster. version cluster.

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Automate alerting and reporting for AWS Glue job resource usage

AWS Big Data

Many organizations today are using AWS Glue to build ETL pipelines that bring data from disparate sources and store the data in repositories like a data lake, database, or data warehouse for further consumption. EventBridge picks up the event from AWS Glue and triggers an AWS Lambda function.

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Simplify Metrics on Apache Druid With Rill Data and Cloudera

Cloudera

Cloudera users can securely connect Rill to a source of event stream data, such as Cloudera DataFlow , model data into Rill’s cloud-based Druid service, and share live operational dashboards within minutes via Rill’s interactive metrics dashboard or any connected BI solution. Cloudera Data Warehouse).

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. Data lakes are more focused around storing and maintaining all the data in an organization in one place.

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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

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Manage your workloads better using Amazon Redshift Workload Management

AWS Big Data

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

The aim was to bolster their analytical capabilities and improve data accessibility while ensuring a quick time to market and high data quality, all with low total cost of ownership (TCO) and no need for additional tools or licenses. Flat files – Other systems supply data in the form of flat files of different formats.