Data Stream Development with Apache Spark, Kafka, and Spring Boot
Video description
Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline
About This Video
From blueprint architecture to complete code solution, this course treats every important aspect involved in architecting and developing a data streaming pipeline
Select the right tools and frameworks and follow the best approaches to designing your data streaming framework
Build an end-to-end …
Data Stream Development with Apache Spark, Kafka, and Spring Boot
Video description
Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline
About This Video
From blueprint architecture to complete code solution, this course treats every important aspect involved in architecting and developing a data streaming pipeline
Select the right tools and frameworks and follow the best approaches to designing your data streaming framework
Build an end-to-end data streaming pipeline from a real data stream (Meetup RSVPs) and expose the analyzed data in browsers via Google Maps
In Detail
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.
Audience
This course is perfect for Java developers and architects who want to design and write data streaming pipelines. Having knowledge of the Spring framework will be an added benefit.
Running The Collection Tier (Part II – Sending Data)
Chapter 3 : Proceeding to the Data Access Tier
Dissecting the Data Access Tier
Introducing Our Data Access Tier – MongoDB
Exploring Spring Reactive
Exposing the Data Access Tier in Browser
Chapter 4 : 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
Chapter 5 : Mitigate Data Loss between Collection, Analysis and Message Queuing Tiers
Fault Tolerance (HML)
Kafka Connect
Securing Communication between Tiers
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