Data Engineering Podcast

NOVEMBER 4, 2021

Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. 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.

Data Warehouse 100

Data Warehouse Business Intelligence ETL System Data Lake 100

ProjectPro

SEPTEMBER 30, 2014

"Hadoop is a key ingredient in allowing LinkedIn to build many of our most computationally difficult features, allowing us to harness our incredible data about the professional world for our users," said Jay Kreps, Principal Engineer, LinkedIn.

Hadoop 52

Hadoop Unstructured Data ETL Tools ETL System 52

Precisely

APRIL 3, 2023

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.

ETL System 52

ETL System Transportation Architecture Manufacturing 52

ProjectPro

NOVEMBER 30, 2021

That's where the ETL (Extract, Transform, and Load) pipeline comes into the picture! Table of Contents What is ETL Pipeline? First, we will start with understanding the Data pipelines with a straightforward layman's example. Now let us try to understand ETL data pipelines in more detail.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

MAY 17, 2023

Go Deep On Key Business Metrics Avoid Costs 30. 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. “We

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Go Deep On Key Business Metrics Avoid Costs 30. 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. “We

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52