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Spark vs Hive - What's the Difference

ProjectPro

Spark SQL, for instance, enables structured data processing with SQL. Hive , for instance, does not support sub-queries and unstructured data. Data update and deletion operations are also not possible with Hive. Apache Spark also offers hassle-free integration with other high-level tools.

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Veracity in Big Data: Why Accuracy Matters

Knowledge Hut

Variety: Variety represents the diverse range of data types and formats encountered in Big Data. Traditional data sources typically involve structured data, such as databases and spreadsheets. Handling this variety of data requires flexible data storage and processing methods.

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10 Sentiment Analysis Project Ideas with Source Code [2023]

ProjectPro

Building a portfolio of projects will give you the hands-on experience and skills required for performing sentiment analysis. It'll be a great addition to your data science portfolio (or CV) as well. Over the years, analyses were mostly limited to structured data within organizations.

Coding 52
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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 1- Automating the Lakehouse's data intake.

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How JPMorgan uses Hadoop to leverage Big Data Analytics?

ProjectPro

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop. JP Morgan has massive amounts of data on what its customers spend and earn.

Hadoop 52
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Is the data warehouse going under the data lake?

ProjectPro

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream dataData Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

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5 Reasons Why ETL Professionals Should Learn Hadoop

ProjectPro

While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations.

Hadoop 52