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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? The applications must be integrated to the surrounding business systems so ideas can be tested and validated in the real world in a controlled manner.

IT 350
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How DeNA Co., Ltd. accelerated anonymized data quality tests up to 100 times faster using Amazon Redshift Serverless and dbt

AWS Big Data

Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times. The challenge Data quality tests require performing 1,300 tests on 10 TB of data monthly.

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Is Big Data Transforming Our Broken Hospital Management Systems?

Smart Data Collective

Purchase Ready-Made Big Data Solutions for Healthcare Applications. There is also a range of different data-driven solutions you can start using right now. Such products usually come with a standard set of tools, and you can test several of them to pick the best option. appeared first on SmartData Collective.

Big Data 103
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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

Pruitt says the airport’s new capabilities provide data-driven insights for improving operations, passenger experience, and non-aeronautical revenue across airport business units. Applying AI to elevate ROI Pruitt and Databricks recently finished a pilot test with Microsoft called Smart Flow.

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What is a DataOps Engineer?

DataKitchen

The data organization wants to run the Value Pipeline as robustly as a six sigma factory, and it must be able to implement and deploy process improvements as rapidly as a Silicon Valley start-up. The data engineer builds data transformations. Their product is the data. Create tests. Run the factory.

Testing 162
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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

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Introducing simplified interaction with the Airflow REST API in Amazon MWAA

AWS Big Data

response = client.create( key="test", value="Test value", description="Test description" ) print(response) print("nListing all variables.") variables = client.list() print(variables) print("nGetting the test variable.") Creating a test variable. Creating a test variable. Creating a test variable.