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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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Every Business Needs an Analytics-Driven Content Marketing Strategy

Smart Data Collective

We have talked extensively about the value of using data for marketing strategies in all industries. Analytics can be particularly useful for content marketing. Many marketers are stuck in 2008, when data analytics didn’t have a place in digital marketing strategies. Find new leads.

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Make Every Sprint Count with DevOps Analytics

Sisense

DevOps first came about in 2007-2008 to fix problems in the software industry and bring with it continuous improvement and greater efficiencies. DevOps analytics is the analysis of machine data to find insights that can be acted upon. DevOps data analytics can be set up and measured at any time during your DevOps journey.

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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

In the following sample code, we generate a report showing the quarterly sales for the year 2008. To do that, we join two Amazon Redshift tables using an Apache Spark DataFrame, run a predicate pushdown, aggregate and sort the data, and write the transformed data back to Amazon Redshift. where( col("year") == 2008).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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