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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 Flinkā€™s advanced streaming capabilities.

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Data Observability and Monitoring with DataOps

DataKitchen

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Thatā€™s a fair point, and it places emphasis on what is most important ā€“ what best practices should data teams employ to apply observability to data analytics.

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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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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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Use your corporate identities for analytics with Amazon EMR and AWS IAM Identity Center

AWS Big Data

Use Lake Formation to grant permissions to users to access data. Test the solution by accessing data with a corporate identity. Audit user data access. About the Authors Pradeep Misra is a Principal Analytics Solutions Architect at AWS. Create an IAM Identity Center enabled security configuration for EMR clusters.

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Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

AWS Big Data

The Common Crawl corpus contains petabytes of data, regularly collected since 2008, and contains raw webpage data, metadata extracts, and text extracts. In addition to determining which dataset should be used, cleansing and processing the data to the fine-tuningā€™s specific need is required. It is continuously updated.

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How The Cloud Made ā€˜Data-Driven Cultureā€™ Possible | Part 1

BizAcuity

2008: Microsoft announces Windows Azure (PaaS) with Azure Blob storage (S3 competitor). AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models. Due to the unimaginable scale in which data could be accumulated in this decade, data management and AI will take the front seat in innovation.