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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.

NoSQL 52
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Why Real-Time Analytics Requires Both the Flexibility of NoSQL and Strict Schemas of SQL Systems

Rockset

This is the fifth post in a series by Rockset's CTO and Co-founder Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. Traditional databases, with their wholly-inflexible structures, are brittle. Companies carefully engineered their ETL data pipelines to align with their schemas (not vice-versa).

NoSQL 52
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Smart Schema: Enabling SQL Queries on Semi-Structured Data

Rockset

In this blog post, we show how Rockset’s Smart Schema feature lets developers use real-time SQL queries to extract meaningful insights from raw semi-structured data ingested without a predefined schema. This is particularly true given the nature of real-world 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.

NoSQL 49
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Data Warehouse vs Big Data

Knowledge Hut

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Data warehousing offers several advantages.

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Mythbusting: The Venerable SQL Database and Today’s Real-Time Analytics

Rockset

Rockset is the real-time analytics database in the cloud for modern data teams. Get faster analytics on fresher data, at lower costs, by exploiting indexing over brute-force scanning. In many tech circles, SQL databases remain synonymous with old-school on-premises databases like Oracle or DB2. This may come as a surprise.

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How to Learn SQL Basics for Data Science in 2023?

ProjectPro

Data science and artificial intelligence might be the buzzwords of recent times, but they are of no value without the right data backing them. The process of data collection has increased exponentially over the last few years. Table of Contents Why SQL for Data Science? What is SQL? Why SQL for Data Science?