Remove 2008 Remove Big Data Remove Data Analytics Remove Testing
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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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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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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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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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How The Cloud Made ā€˜Data-Driven Cultureā€™ Possible | Part 1

BizAcuity

2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. An efficient big data management and storage solution that AWS quickly took advantage of. They now have a disruptive data management solution to offer to its client base.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

2008 – Financial crisis : scientists flee Wall St. to join data science teams, e.g., to support advertising, social networks, gaming, and so onā€”I hired more than a few. 2018 – Global reckoning about data governance, aka ā€œOops! Data governance, for the win! No big deal.ā€. The Big Picture.