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

Data Engineering Podcast

A natural outgrowth of that capability is the more recent growth of reverse ETL systems that use those analytics to feed back into the operational systems used to engage with the customer. We have been building data warehouses and business intelligence systems for decades.

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5 Reasons Why ETL Professionals Should Learn Hadoop

ProjectPro

Reason Two: Handle Big Data Efficiently The emergence of needs and tools of ETL proceeded the Big Data era. As data volumes continued to grow in the traditional ETL systems, it required a proportional increase in the people, skills, software and resources.

Hadoop 52
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61 Data Observability Use Cases From Real Data Teams

Monte Carlo

Oftentimes these ETL systems come under considerable pressure as all of your stakeholders want to look at every metric a million different ways with sub second latency. It’s hard to convince departments to launch experiments or executives to trust them if no one believes in the underlying data or the dashboards they look at every day.

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

Monte Carlo

Oftentimes these ETL systems come under considerable pressure as all of your stakeholders want to look at every metric a million different ways with sub second latency. It’s hard to convince departments to launch experiments or executives to trust them if no one believes in the underlying data or the dashboards they look at every day.

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What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52
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Why a Streaming-First Approach to Digital Modernization Matters

Precisely

The Long Road from Batch to Real-Time Traditional “extract, transform, load” (ETL) systems were built under certain constraints, stemming from the cost of technology and implementation resources, as well as the inherent limits of computational power. Today’s world calls for a streaming-first approach.