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Data Integrity for AI: What’s Old is New Again

Precisely

The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

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Data Ingestion vs Data Integration: What Is the Right Approach for Your Business

Hevo

Organizations generate tons of data every second, yet 80% of enterprise data remains unstructured and unleveraged (Unstructured Data). Organizations need data ingestion and integration to realize the complete value of their data assets.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ? Bronze, Silver, and Gold – The Data Architecture Olympics? The Bronze layer is the initial landing zone for all incoming raw data, capturing it in its unprocessed, original form.

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Data Ingestion vs Data Integration: What Is the Right Approach for Your Business

Hevo

Organizations generate tons of data every second, yet 80% of enterprise data remains unstructured and unleveraged (Unstructured Data). Organizations need data ingestion and integration to realize the complete value of their data assets.

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis. This is crucial for maintaining data integrity and quality.

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Hevo vs Airflow: The Better Tool?

Hevo

Data integration is an integral part of modern business strategy, enabling businesses to convert raw data into actionable information and make data-driven decisions. Tools like Apache Airflow are used and popular for workflow automation.

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5 Helpful Extract & Load Practices for High-Quality Raw Data

Meltano

Setting the Stage: We need E&L practices, because “copying raw data” is more complex than it sounds. For instance, how would you know which orders got “canceled”, an operation that usually takes place in the same data record and just “modifies” it in place. But not at the ingestion level.