Data Stream Development with Apache Spark, Kafka, and Spring Boot [Video]
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Free ChapterIntroducing Data Streaming Architecture
-
Deployment of Collection and Message Queuing Tiers
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Proceeding to the Data Access Tier
-
Implementing the Analysis Tier
- Diving into the Analysis Tier
- Streaming Algorithms For Data Analysis
- Introducing Our Analysis Tier – Apache Spark
- Plug-in Spark Analysis Tier to Our Pipeline
- Brief Overview of Spark RDDs
- Spark Streaming
- DataFrames, Datasets and Spark SQL
- Spark Structured Streaming
- Machine Learning in 7 Steps
- MLlib (Spark ML)
- Spark ML and Structured Streaming
- Spark GraphX
-
Mitigate Data Loss between Collection, Analysis and Message Queuing Tiers
About this
video
Today, organizations have a difficult time working with huge numbers of datasets. In addition, data processing and analyzing need to be done in real time to gain insights. This is where data streaming comes in. As big data is no longer a niche topic, having the skillset to architect and develop robust data streaming pipelines is a must for all developers. In addition, they also need to think of the entire pipeline, including the trade-offs for every tier.
This course starts by explaining the blueprint architecture for developing a completely functional data streaming pipeline and installing the technologies used. With the help of live coding sessions, you will get hands-on with architecting every tier of the pipeline. You will also handle specific issues encountered working with streaming data. You will input a live data stream of Meetup RSVPs that will be analyzed and displayed via Google Maps.
By the end of the course, you will have built an efficient data streaming pipeline and will be able to analyze its various tiers, ensuring a continuous flow of data.
All the code and supporting files for this course are available at https://github.com/PacktPublishing/-Data-Stream-Development-with-Apache-Spark-Kafka-and-Spring-Boot
- Publication date:
- November 2018
- Publisher
- Packt
- Duration
- 7 hours 51 minutes
- ISBN
- 9781789539585
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