Remove Artificial Intelligence Remove Data Warehouse Remove Hadoop
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Data Integrity for AI: What’s Old is New Again

Precisely

Artificial Intelligence (AI) is all the rage, and rightly so. The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. There was no easy way to consolidate and analyze this data to more effectively manage our business.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Key Differences.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

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How to modernize data lakes with a data lakehouse architecture

IBM Journey to AI blog

The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines.