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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. “Raw data” sounds clear. Every time you change systems, you will need to modify the “raw data” to adhere to the rules of the new system.

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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

ERP and CRM systems are designed and built to fulfil a broad range of business processes and functions. This generalisation makes their data models complex and cryptic and require domain expertise. As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly.

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Why SQL on Raw Data?

Rockset

Fortunately, storage and compute substrates are changing quickly, leading to new opportunities in the form of optimized schemaless SQL processing systems. With an abundance of inexpensive storage, we can afford to build new types of indexes that allow us to ingest raw data in multiple formats. Specifically: Storage.

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Power BI System Requirements Specification of 2023

Knowledge Hut

While the numbers are impressive (and a little intimidating), what would we do with the raw data without context? The tool will sort and aggregate these raw data and transport them into actionable, intelligent insights. Below are the Power BI requirements for the system.

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Consulting Case Study: Recommender Systems

WeCloudData

Next, in order for the client to leverage their collected user clickstream data to enhance the online user experience, the WeCloudData team was tasked with developing recommender system models whereby users can receive more personalized article recommendations.

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Consulting Case Study: Recommender Systems

WeCloudData

Next, in order for the client to leverage their collected user clickstream data to enhance the online user experience, the WeCloudData team was tasked with developing recommender system models whereby users can receive more personalized article recommendations.

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Building a large scale unsupervised model anomaly detection system?—?Part 1

Lyft Engineering

Building a large scale unsupervised model anomaly detection system — Part 1 Distributed Profiling of Model Inference Logs By Anindya Saha , Han Wang , Rajeev Prabhakar Introduction LyftLearn is Lyft’s ML Platform. This motivated us to continue improving the existing Anomaly Detection system. As always, Lyft is hiring!

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