Apache Spark Streaming Tutorial for Beginners

#artificialintelligence

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

@machinelearnbot

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.


Apache Spark - Dzone Refcardz

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Spark Streaming provides a scalable, fault tolerant, efficient way of processing streaming data using Spark's simple programming model. It converts streaming data into "micro" batches, which enable Spark's batch programming model to be applied in Streaming use cases. This unified programming model makes it easy to combine batch and interactive data processing with streaming. The core abstraction in Spark Streaming is Discretized Stream (DStream). DStream is a sequence of RDDs.


Apache Spark: A Unified Engine for Big Data Processing

#artificialintelligence

Analyses performed using Spark of brain activity in a larval zebrafish: embedding dynamics of whole-brain activity into lower-dimensional trajectories. This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications. The growth of data volumes in industry and research poses tremendous opportunities, as well as tremendous computational challenges. As data sizes have outpaced the capabilities of single machines, users have needed new systems to scale out computations to multiple nodes. As a result, there has been an explosion of new cluster programming models targeting diverse computing workloads.1,4,7,10 At first, these models were relatively specialized, with new models developed for new workloads; for example, MapReduce4 supported batch processing, but Google also developed Dremel13 for interactive SQL queries and Pregel11 for iterative graph algorithms. In the open source Apache Hadoop stack, systems like Storm1 and Impala9 are also specialized. Even in the relational database world, the trend has been to move away from "one-size-fits-all" systems.18 Unfortunately, most big data applications need to combine many different processing types. The very nature of "big data" is that it is diverse and messy; a typical pipeline will need MapReduce-like code for data loading, SQL-like queries, and iterative machine learning. Specialized engines can thus create both complexity and inefficiency; users must stitch together disparate systems, and some applications simply cannot be expressed efficiently in any engine. In 2009, our group at the University of California, Berkeley, started the Apache Spark project to design a unified engine for distributed data processing.


Apache Spark: A Unified Engine for Big Data Processing

@machinelearnbot

Analyses performed using Spark of brain activity in a larval zebrafish: embedding dynamics of whole-brain activity into lower-dimensional trajectories. The growth of data volumes in industry and research poses tremendous opportunities, as well as tremendous computational challenges. As data sizes have outpaced the capabilities of single machines, users have needed new systems to scale out computations to multiple nodes. As a result, there has been an explosion of new cluster programming models targeting diverse computing workloads.1,4,7,10 At first, these models were relatively specialized, with new models developed for new workloads; for example, MapReduce4 supported batch processing, but Google also developed Dremel13 for interactive SQL queries and Pregel11 for iterative graph algorithms. In the open source Apache Hadoop stack, systems like Storm1 and Impala9 are also specialized. Even in the relational database world, the trend has been to move away from "one-size-fits-all" systems.18 Unfortunately, most big data applications need to combine many different processing types. The very nature of "big data" is that it is diverse and messy; a typical pipeline will need MapReduce-like code for data loading, SQL-like queries, and iterative machine learning. Specialized engines can thus create both complexity and inefficiency; users must stitch together disparate systems, and some applications simply cannot be expressed efficiently in any engine. In 2009, our group at the University of California, Berkeley, started the Apache Spark project to design a unified engine for distributed data processing. Spark has a programming model similar to MapReduce but extends it with a data-sharing abstraction called "Resilient Distributed Datasets," or RDDs.25 Using this simple extension, Spark can capture a wide range of processing workloads that previously needed separate engines, including SQL, streaming, machine learning, and graph processing2,26,6 (see Figure 1).