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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. FactSet has several datasets available in the AWS Data Exchange marketplace, which we used for reference data.

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

Amazon Kinesis Data Analytics makes it easy to transform and analyze streaming data in real time. In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities.

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Simplify and speed up Apache Spark applications on Amazon Redshift data with Amazon Redshift integration for Apache Spark

AWS Big Data

Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark is a popular framework that you can use to build applications for use cases such as ETL (extract, transform, and load), interactive analytics, and machine learning (ML). groupBy("qtr").sum("qtysold").select(

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Data Observability and Monitoring with DataOps

DataKitchen

Data errors impact decision-making. When analytics and dashboards are inaccurate, business leaders may not be able to solve problems and pursue opportunities. Data errors infringe on work-life balance. Data errors also affect careers. We see data observability as a component of DataOps. Production Analytics.

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