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Spark Data Streaming with MongoDB

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we are going to talk about data streaming with apache spark in Python with codes. We will also talk about how to persist our streaming data into MongoDB.We

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Handling Streaming Data with Apache Kafka – A First Look

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction When we mention BigData, one of the types of data usually talked about is the Streaming Data. Streaming Data is generated continuously, by multiple data sources say, sensors, server logs, stock prices, etc.

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Data Engineering for Streaming Data on GCP

Analytics Vidhya

Introduction Companies can access a large pool of data in the modern business environment, and using this data in real-time may produce insightful results that can spur corporate success. Real-time dashboards such as GCP provide strong data visualization and actionable information for decision-makers.

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Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

In this post, we show how to use Amazon Kinesis Data Streams to buffer and aggregate real-time streaming data for delivery into Amazon OpenSearch Service domains and collections using Amazon OpenSearch Ingestion. This decoupling provides advantages over traditional architectures.

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7 Ways to Supercharge Your ABM Strategy with Real-Time Intent

In this guide, we’ll walk through how streaming real-time intent data can supercharge your ABM strategy, including: How streaming real-time intent works The benefits of real-time intent in your ABM strategy How you can box out the competition Learn how capturing buyers’ search behavior in real time can shorten your sales cycle.

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How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark

Analytics Vidhya

Overview Streaming data is a thriving concept in the machine learning space Learn how to use a machine learning model (such as logistic regression). The post How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark appeared first on Analytics Vidhya.

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Netflix Case Study (EDA): Unveiling Data-Driven Strategies for Streaming

Analytics Vidhya

Introduction Welcome to our comprehensive data analysis blog that delves deep into the world of Netflix. As one of the leading streaming platforms globally, Netflix has revolutionized how we consume entertainment. With its vast library of movies and TV shows, it offers an abundance of choices for viewers around the world.

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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5 Key Elements for Building a Successful Data-Driven Product

Leading brands and local businesses alike are tapping into varied business and consumer data to power their products and meet consumers’ ever-evolving needs. But companies need to remember that a product can only be as good as the data that powers it. The criteria you should use to vet available data sources.

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Embedded BI and Analytics: Best Practices to Monetize Your Data

Speaker: Azmat Tanauli, Senior Director of Product Strategy at Birst

How much potential revenue is hidden in your data? In a recent Economist survey of 476 senior executives worldwide, 60% are already generating revenue from their data, and a whopping 83% have used data to make existing products or services more profitable.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Is your data secure? In which format?