Data Engineering Podcast

NOVEMBER 4, 2021

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.

Data Warehouse 100

Data Warehouse Business Intelligence ETL System Data Lake 100

Precisely

APRIL 3, 2023

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.

ETL System 52

ETL System Transportation Architecture Manufacturing 52

ProjectPro

SEPTEMBER 30, 2014

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

Hadoop ETL Tools Unstructured Data ETL System 52

ProjectPro

NOVEMBER 30, 2021

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

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

MAY 17, 2023

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

Data Data Pipeline Data Engineer Data Engineering 52

Monte Carlo

MAY 17, 2023

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 Pipeline 52

Data Pipeline Data Engineer Data Engineering Data 52