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RDBMS vs NoSQL: Key Differences and Similarities

Knowledge Hut

Making decisions in the database space requires deciding between RDBMS (Relational Database Management System) and NoSQL, each of which has unique features. RDBMS uses SQL to organize data into structured tables, whereas NoSQL is more flexible and can handle a wider range of data types because of its dynamic schemas.

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From Oracle to Databases for AI: The Evolution of Data Storage

KDnuggets

From Oracle, to NoSQL databases, and beyond, read about data management solutions from the early days of the RBDMS to those supporting AI applications.

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HBase vs Cassandra-The Battle of the Best NoSQL Databases

ProjectPro

NoSQL databases are the new-age solutions to distributed unstructured data storage and processing. The speed, scalability, and fail-over safety offered by NoSQL databases are needed in the current times in the wake of Big Data Analytics and Data Science technologies.

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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

Each of these technologies has its own strengths and weaknesses, but all of them can be used to gain insights from large data sets. As organizations continue to generate more and more data, big data technologies will become increasingly essential. Let's explore the technologies available for big data.

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NoSQL vs SQL- 4 Reasons Why NoSQL is better for Big Data applications

ProjectPro

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

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How to query JSONB array of objects in PostgreSQL

Hevo

Do you have a NoSQL database that has no rigid shape and is causing data analysis complexity nightmares? PostgreSQL is a high-performing, open-sourced object-relational database with two JSON data storage types, JSON and JSONB. With JSON in PostgreSQL, you can have a solution to your complex problem.

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.