Apache Spark Streaming Tutorial for Beginners

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In a world where we generate data at an extremely fast rate, the correct analysis of the data and providing useful and meaningful results at the right time can provide helpful solutions for many domains dealing with data products. We can apply this in Health Care and Finance to Media, Retail, Travel Services and etc. some solid examples include Netflix providing personalized recommendations at real-time, Amazon tracking your interaction with different products on its platform and providing related products immediately, or any business that needs to stream a large amount of data at real-time and implement different analysis on it. One of the amazing frameworks that can handle big data in real-time and perform different analysis, is Apache Spark. In this blog, we are going to use spark streaming to process high-velocity data at scale. Apache Spark is a lightning-fast cluster computing technology, designed for fast computation.



Spark SQL for Real-Time Analytics

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This article is part of the forthcoming Data Science for Internet of Things Practitioner course in London. If you want to be a Data Scientist for the Internet of Things, this intensive course is ideal for you. We cover complex areas like Sensor fusion, Time Series, Deep Learning and others. We work with Apache Spark, R language and leading IoT platforms. This is the 1st part of a series of 3 part article which discusses SQL with Spark for Real Time Analytics for IOT.


Practical Apache Spark in 10 minutes. Part 5 -- Streaming

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Spark is a powerful tool which can be applied to solve many interesting problems. Some of them have been discussed in our previous posts. Today we will consider another important application, namely streaming. Streaming data is the data which continuously comes as small records from different sources. There are many use cases for streaming technology such as sensor monitoring in industrial or scientific devices, server logs checking, financial markets monitoring, etc.


Top 10 Questions about Apache Spark on the MapR Data Platform

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How do I write back into MapR Streams using Spark (Java)? I am now able to read from MapR Streams using Spark. But now I want to write back into them using Spark (and Java). There is barely any documentation available online for Scala, and there isn't any available for Java. I did find a "sendToKafka" function mentioned in some Scala code, but the same isn't working for Java (because it writes DStream and I am working with JavaDStream). All I am looking for is a Java doc for MapR Streams and Spark, or just a function that lets me write JavaDStream into MapR Streams, preferably using Java. Answer 2: At this time, there is only a Scala producer in the org.apache.spark.streaming.kafka.producer