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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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Why Real-Time Analytics Requires Both the Flexibility of NoSQL and Strict Schemas of SQL Systems

Rockset

Traditional databases, with their wholly-inflexible structures, are brittle. So are schemaless NoSQL databases, which capably ingest firehoses of data but are poor at extracting complex insights from that data. After debuting Project Nectar, we presented it to a new set of application developers. Here’s an example.

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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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Introduction to MongoDB for Data Science

Knowledge Hut

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. There are several benefits to MongoDB for data science operations.

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Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

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A Prequel to Data Mesh

Towards Data Science

My personal take on justifying the existence of Data Mesh A senior stakeholder at one my projects mentioned that they wanted to decentralise their data platform architecture and democratise data across the organisation. When I heard the words ‘decentralised data architecture’, I was left utterly confused at first!

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

At DareData, we believe that data scientists should be responsible for the deployment and maintenance of their own models, or at least have a strong understanding of data engineering concepts to better contribute to the lifecycle of model projects. Examples of NoSQL databases include MongoDB or Cassandra.