Remove Retail Remove Structured Data Remove Unstructured Data
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Data Warehouse vs. Data Lake

Precisely

We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data. They may want to look at those numbers on a daily or weekly basis.

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Introduction to MongoDB for Data Science

Knowledge Hut

MongoDB is a NoSQL database that’s been making rounds in the data science community. MongoDB’s unique architecture and features have secured it a place uniquely in data scientists’ toolboxes globally. Let us see where MongoDB for Data Science can help you.

MongoDB 52
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Top ETL Use Cases for BI and Analytics:Real-World Examples

ProjectPro

ETL for IoT - Use ETL to analyze large volumes of data IoT devices generate. Real-World ETL Use Cases and Applications Across Industries This blog discusses the numerous ETL use cases in various industries, including finance, healthcare, and retail.

BI 52
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Generative AI vs. Predictive AI: Understanding the Differences

Edureka

paintings, songs, code) Historical data relevant to the prediction task (e.g., Here’s a detailed breakdown of the core algorithms that power predictive AI: Machine Learning Algorithms These algorithms help identify patterns and make predictions based on structured data.

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A Flexible and Efficient Storage System for Diverse Workloads

Cloudera

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 86
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Commercial Lines Insurance- the End of the Line for All Data

Cloudera

For example, the types of data sourced from other industries that we can use in the underwriting process include: Manufacturing – sensors (for quality, safety and maintenance-related). Retail – location (and associated risk), type of equipment used, inventory sensors, supply chain data, hours of operation.

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How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

Common Tools Data Sources Identification with Apache NiFi : Automates data flow, handling structured and unstructured data. Used for identifying and cataloging data sources. Data Storage with Apache HBase : Provides scalable, high-performance storage for structured and semi-structured data.