Remove Data Integration Remove Data Transformation Remove Risk
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Data Integrity, the Basis for Reliable Insights

Sisense

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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Key Challenges Affecting Data Transformations—Dev and Testing

Wayne Yaddow

Common challenges and practical mitigation strategies for reliable data transformations. Photo by Mika Baumeister on Unsplash Introduction Data transformations are important processes in data engineering, enabling organizations to structure, enrich, and integrate data for analytics , reporting, and operational decision-making.

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
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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.

Testing 52
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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. Azure Blob Storage serves as the data lake to store raw data.

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Data Engineers Are Using AI to Verify Data Transformations

Wayne Yaddow

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. This may also entail working with new data through methods like web scraping or uploading.