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How to Use KSQL Stream Processing and Real-Time Databases to Analyze Streaming Data in Kafka

Rockset

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka.

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Why Mutability Is Essential for Real-Time Data Analytics

Rockset

To deliver real-time analytics, companies need a modern technology infrastructure that includes these three things: A real-time data source such as web clickstreams, IoT events produced by sensors, etc. A platform such as Apache Kafka/Confluent , Spark or Amazon Kinesis for publishing that stream of event data.

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Top 12 Data Engineering Project Ideas [With Source Code]

Knowledge Hut

Stock and Twitter Data Extraction Using Python, Kafka, and Spark Project Overview: The rising and falling of GameStop's stock price and the proliferation of cryptocurrency exchanges have made stocks a topic of widespread attention. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

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A Cost-Effective Data Warehouse Solution in CDP Public Cloud – Part1

Cloudera

A typical approach that we have seen in customers’ environments is that ETL applications pull data with a frequency of minutes and land it into HDFS storage as an extra Hive table partition file. In this way, the analytic applications are able to turn the latest data into instant business insights. Cost-Effective.

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How to Use Kafka for Event Streaming in a Microservices Architecture?

Workfall

Traditionally, web sockets were the go-to option when it came to real-time applications, but think of a situation whereby there’s server downtime. It means that there is a high risk of data loss but Apache Kafka solves this because it is distributed and can easily scale horizontally and other servers can take over the workload seamlessly.

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A Gentle Introduction to Analytical Stream Processing

Towards Data Science

From Enormous Data back to Big Data Say you are tasked with building an analytics application that must process around 1 billion events (1,000,000,000) a day. thousand (k) events a second (or around 695k events a minute if the event stream is constant), which is an easier number to rationalize. Listing 9–1.

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Changing face of real-time analytics

Rockset

One example is an application that combines web analytics with customer data and social data to predict end user behavior, churn, LTV, or just serve more timely content.” This means the lines between real-time analytics and real-time analytical applications are now blurring.