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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. The Otezla team built a system with tens of thousands of automated tests checking data and analytics quality.

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DataOps For Business Analytics Teams

DataKitchen

A DataOps process hub offers a way for business analytics teams to cope with fast-paced requirements without expanding staff or sacrificing quality. Analytics Hub and Spoke. The data analytics function in large enterprises is generally distributed across departments and roles. Business Analytic Challenges.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

It often takes months to progress from a data lake to the final delivery of insights. One data engineer called it the “last mile problem.” . In our many conversations about data analytics, data engineers, analysts and scientists have verbalized the difficulty of creating analytics in the modern enterprise.

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

AWS Big Data

This means you can seamlessly combine information such as clinical data stored in HealthLake with data stored in operational databases such as a patient relationship management system, together with data produced from wearable devices in near real-time. We use on-demand capacity mode.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Streaming data facilitates the constant flow of diverse and up-to-date information, enhancing the models’ ability to adapt and generate more accurate, contextually relevant outputs. OpenSearch Service offers visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5 versions).

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Introducing watsonx: The future of AI for business

IBM Big Data Hub

At IBM, we believe it is time to place the power of AI in the hands of all kinds of “AI builders” — from data scientists to developers to everyday users who have never written a single line of code. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs.

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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

The data lake implemented by Ruparupa uses Amazon S3 as the storage platform, AWS Database Migration Service (AWS DMS) as the ingestion tool, AWS Glue as the ETL (extract, transform, and load) tool, and QuickSight for analytic dashboards. The audience of these few reports was limited—a maximum of 20 people from management.