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How Ruparupa gained updated insights with an Amazon S3 data lake, AWS Glue, Apache Hudi, and Amazon QuickSight

AWS Big Data

In this post, we show how Ruparupa implemented an incrementally updated data lake to get insights into their business using Amazon Simple Storage Service (Amazon S3), AWS Glue , Apache Hudi , and Amazon QuickSight. An AWS Glue ETL job, using the Apache Hudi connector, updates the S3 data lake hourly with incremental data.

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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

A solid ramp in initial interest puts a new medicine on a trajectory to meet its lifetime sales targets. During the product launch, everyone in the sales and marketing organizations is hyper-focused on business development. Marketing invests heavily in multi-level campaigns, primarily driven by data analytics.

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

These announcements drive forward the AWS Zero-ETL vision to unify all your data, enabling you to better maximize the value of your data with comprehensive analytics and ML capabilities, and innovate faster with secure data collaboration within and across organizations.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

Does Data warehouse as a software tool will play role in future of Data & Analytics strategy? You cannot get away from a formalized delivery capability focused on regular, scheduled, structured and reasonably governed data. Data lakes don’t offer this nor should they. E.g. Data Lakes in Azure – as SaaS.

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Periscope Data Expands to Israel, Empowering Data Teams with Powerful Tools

Sisense

That’s why it has become increasingly vital for companies to leverage fresh data and deploy accurate analytics, so they can make smarter decisions that will enable them to prosper. Ziv Ben Naim, Client Services Analytics & Strategy Lead, AppsFlyer Maayan Dukas Kfir, Data Analyst, AppsFlyer. The impact on customers.

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What is a Data Pipeline?

Jet Global

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.