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Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

phData

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Having model-level data validations along with implementing a data observability framework helps to address the data vault’s data quality challenges.

SQL 52
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Testing and Monitoring Data Pipelines: Part Two

Dataversity

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

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Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven…

ODSC - Open Data Science

Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven Data Modeling How To Get Started With Building AI in High-Risk Industries This guide will get you started building AI in your organization with ease, axing unnecessary jargon and fluff, so you can start today.

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Mainframe Data: Empowering Democratized Cloud Analytics

Precisely

Prioritize solutions that offer flexibility and ease in data sharing, allowing for streamlined creation and testing of data models. Additionally, the ideal integration solution should seamlessly meld with current systems, emphasizing real-time data observability to proactively address potential issues.

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Our Journey with Apache Arrow (Part 2): Adaptive Schemas and Sorting

Hacker News

var ( // Simplified schema definition generated by the Arrow Record encoder based on // the data observed. A comprehensive description of the Arrow data model employed in OpenTelemetry can be accessed here. Each method presents its unique advantages and disadvantages.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? With Talend, you can assess data quality, identify anomalies, and implement data cleansing processes.