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Purpose and Use Cases

Databases: Used for day-to-day operations to manage transactional data and applications needing immediate read/write access, like customer databases and inventory systems.
Data Warehouses: Built for analytical processing, i.e., aggregating data from various sources to generate business insights. Ideal for historical data analysis, business intelligence, and reporting.
Data Lakes: Serve as a central repository for all types of data, which enables data scientists and analysts to run machine learning models and big data analytics. Suitable for AI/ML applications and exploratory data analysis.

Written By

Courtney Jones

Jul 26, 2024Read Time: 1 min

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❯ python3 -c "import jinja2"

Purpose and Use Cases

Databases: Used for day-to-day operations to manage transactional data and applications needing immediate read/write access, like customer databases and inventory systems.
Data Warehouses: Built for analytical processing, i.e., aggregating data from various sources to generate business insights. Ideal for historical data analysis, business intelligence, and reporting.
Data Lakes: Serve as a central repository for all types of data, which enables data scientists and analysts to run machine learning models and big data analytics. Suitable for AI/ML applications and exploratory data analysis.

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Purpose and Use Cases

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From Reactive to Orchestrated: Building Real-Time Multi-Agent AI With Confluent

Beyond Boundaries: Leveraging Confluent for Secure Inter-Organizational Data Sharing