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Exploring The Evolution And Adoption of Customer Data Platforms and Reverse ETL

Data Engineering Podcast

Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data. Go to dataengineeringpodcast.com/montecarlo and start trusting your data with Monte Carlo today!

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Why a Streaming-First Approach to Digital Modernization Matters

Precisely

How can an organization enable flexible digital modernization that brings together information from multiple data sources, while still maintaining trust in the integrity of that data? To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

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What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52
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61 Data Observability Use Cases From Real Data Teams

Monte Carlo

Stop Revenue Bleeding System Modernization and Optimization 33. Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Prioritize Data Assets And Efforts 41.

Data 52
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61 Data Observability Use Cases That Aren’t Totally Made Up

Monte Carlo

Stop Revenue Bleeding System Modernization and Optimization 33. Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Prioritize Data Assets And Efforts 41.