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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Why: Data Makes It Different. In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?

IT 363
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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.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

The data in Amazon Redshift is transactionally consistent and updates are automatically and continuously propagated. Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization.

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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. This approach helps in managing storage costs while maintaining the flexibility to analyze historical trends when needed.

Data Lake 104
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Complex Data Transformations — Test Planning Best Practices

Wayne Yaddow

Complex Data TransformationsTest Planning Best Practices Ensuring data accuracy with structured testing and best practices Photo by Taylor Vick on Unsplash Introduction Data transformations and conversions are crucial for data pipelines, enabling organizations to process, integrate, and refine raw data into meaningful insights.

Testing 52
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From Raw Inputs to Polished Outputs: The Art of Testing Data Transformations

Wayne Yaddow

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage. PyTest, JUnit,NUnit).

Testing 52
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Functional Gaps in Your Data Transformation Testing Tools?

Wayne Yaddow

Managing tests of complex data transformations when automated data testing tools lack important features? Photo by Marvin Meyer on Unsplash Introduction Data transformations are at the core of modern business intelligence, blending and converting disparate datasets into coherent, reliable outputs.

Testing 52