Remove 2008 Remove Data Analytics Remove Interactive 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

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

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.

Testing 214
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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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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! Itā€™s really not enough to build an interesting model.