Remove Data Enablement Remove Data Lake Remove Testing
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Eight Top DataOps Trends for 2022

DataKitchen

In 2022, data organizations will institute robust automated processes around their AI systems to make them more accountable to stakeholders. Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric.

Testing 245
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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Cloud computing has made it much easier to integrate data sets, but that’s only the beginning. Creating a data lake has become much easier, but that’s only ten percent of the job of delivering analytics to users. It often takes months to progress from a data lake to the final delivery of insights.

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DataOps For Business Analytics Teams

DataKitchen

There’s a recent trend toward people creating data lake or data warehouse patterns and calling it data enablement or a data hub. DataOps expands upon this approach by focusing on the processes and workflows that create data enablement and business analytics.

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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

They mastered hundreds of data sets, serving thousands of people, with very few errors or missed SLAs (service level agreements). The Otezla team built a system with tens of thousands of automated tests checking data and analytics quality. Has the data arrived on time? Is the quantity of data correct?

Analytics 246
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Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)

AWS Big Data

Testing these upgrades involves running the application and addressing issues as they arise. Each test run may reveal new problems, resulting in multiple iterations of changes. 1X workers, and selecting an appropriate number of workers for processing your sample data. 2X workers and auto scaling enabled for validation.

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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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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. To get started with this feature, see Querying the AWS Glue Data Catalog.