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Announcing the AWS Well-Architected Data Analytics Lens

AWS Big Data

We are delighted to announce the release of the Data Analytics Lens. Using the Lens in the Tool’s Lens Catalog, you can directly assess your Analytics workload in the console, and produce a set of actionable results for customized improvement plans recommended by the Tool. What’s new in the Data Analytics Lens?

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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. Stand out in your industry.

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3 data and analytics trends that will dominate 2022

CIO Business Intelligence

Data analytics is a domain in constant motion. Early in 2020, it seemed clear that organizations would continue to invest heavily in analytics to support their digital transformations. Research firm Fortune Business Insights predicts the global big data analytics market will grow to $549.7 between 2021 and 2008.

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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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Live Patching Is Invaluable To Data Development In Linux

Smart Data Collective

Computer Weekly has stated that Linux is the “powerhouse of big data.” However, developing big data applications rely on the most up-to-date tools. Live patching is one of the most important technologies for developers working on data analytics projects on Linux. Live Patching is Important for Big Data Applications.

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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-tuning’s specific need is required. It is continuously updated.

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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.