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Veracity in Big Data: Why Accuracy Matters

Knowledge Hut

Veracity meaning in big data is the degree of accuracy and trustworthiness of data, which plays a pivotal role in deriving meaningful insights and making informed decisions. This blog will delve into the importance of veracity in Big Data, exploring why accuracy matters and how it impacts decision-making processes.

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

ProjectPro

This blog will give you an in-depth knowledge of what is a data pipeline and also explore other aspects such as data pipeline architecture, data pipeline tools, use cases, and so much more. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases.

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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. Companies analyze customers’ sentiment through social media conversations and reviews so they can make better-informed decisions. It'll be a great addition to your data science portfolio (or CV) as well.

Coding 52
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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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5 Reasons Why ETL Professionals Should Learn Hadoop

ProjectPro

That laid the foundation for an entirely new domain of ETL (an acronym for Extract Transform Load) – a field that continues to dominate data warehousing to this date. The modern technological ecosystem is run and managed by interconnected systems that can read, copy, aggregate, transform and re–load data from one another.

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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Difference between Pig and Hive-The Two Key Components of Hadoop Ecosystem

ProjectPro

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. We generally refer to Unstructured Data as “Big Data” and the framework that is used for processing Big Data is popularly known as Hadoop.

Hadoop 52