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DataOps For Business Analytics Teams

DataKitchen

Their business unit colleagues ask an endless stream of urgent questions that require analytic insights. Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. In business analytics, fire-fighting and stress are common. Analytics Hub and Spoke.

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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

Marketing invests heavily in multi-level campaigns, primarily driven by data analytics. This analytics function is so crucial to product success that the data team often reports directly into sales and marketing. As figure 2 summarizes, the data team ingests data from hundreds of internal and third-party sources.

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

AWS Big Data

Since then, customer demands for better scale, higher throughput, and agility in handling a wide variety of changing, but increasingly business critical analytics and machine learning use cases has exploded, and we have been keeping pace. Let’s dive into the highlights.

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Eight Top DataOps Trends for 2022

DataKitchen

As a result, enterprises will examine their end-to-end data operations and analytics creation workflows. Instead of allowing technology to be a barrier to teamwork, leading data organizations in 2022 will further expand the automation of workflows to improve and facilitate communication and coordination between the groups.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

The requirement to integrate enormous quantities and varieties of data coupled with extreme pressure on analytics cycle time has driven the pharmaceutical industry to lead in DataOps adoption. The bottom line is how to attain analytic agility? It often takes months to progress from a data lake to the final delivery of insights.

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Improve healthcare services through patient 360: A zero-ETL approach to enable near real-time data analytics

AWS Big Data

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. This will be your online transaction processing (OLTP) data store for transactional data.