Learning Path Smack Getting Started With The Smack Stack

What you'll learn
Basic concepts of Scala
Analysing data using Spark in Scala
Creation of fast data processing using SMACK Stack
Requirements
Experience with Scala is essential
Basic knowledge of data processing concepts
Description
If you want to outrun your competitors by taking business decisions using your data, then this course is for you.
SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data.
SMACK: Getting Started with Scala, Spark, and the SMACK Stack gets you familiar with Scala and understanding the various features offered by it. You will also get to understand the process for data analysis using Spark. Finally, you will be introduced to the SMACK Stack which helps us to process data blazingly fast. Development using these technologies can be summarized as: More dаta: Less Time.
This Learning Path is a learner material and the curriculum is so planned to meet your learning needs. It starts with the basics of Apache Spark, one of the trending big data processing frameworks on the market today. We it moves on to Scala, which has emerged as an important tool for performing various data analysis tasks efficiently. It will help you leverage popular Scala libraries and tools to perform core data analysis tasks with ease in Spark. In the last part, we will teach you how to integrate the SMACK stack to create a highly efficient data analysis system for fast data processing.
By the end of the course, you'll be able to analyze and process data swiftly and efficiently as compared to other traditional data analytic systems.
About the Author
For this course, we have combined the best works of this esteemed author
Nishant Garg has over 16 years of software architecture and development experience in various technologies, such as Java Enterprise Edition, SOA, Spring, Hadoop, Hive, Flume, Sqoop, Oozie, Spark, YARN, Impala, Kafka, Storm, Solr/Lucene, NoSQL databases (such as HBase, Cassandra, and MongoDB), and MPP databases (such as GreenPlum). He received his MS in software systems from the Birla Institute of Technology and Science, Pilani, India, and is currently working as a senior technical architect for the Big Data R&D Labs with Impetus Infotech Pvt. Ltd. Nishant has also undertaken many speaking engagements on big data technologies and is also the author of Learning Apache Kafka & HBase Essestials, Packt Publishing.
Anatolii Kmetiuk has been working with Scala-based technologies for four years. He has experience in Deep Learning models for text processing. He is interested in Category Theory and Type-level programming in Scala. Another field of interest is Chaos and Complexity Theory and Artificial Life, and ways to implement them in programming languages.
Raúl Estrada Aparicio is a programmer since 1996 and Java Developer since 2001. He loves functional languages such as Scala, Elixir, Clojure, and Haskell. He also loves all the topics related to Computer Science. With more than 12 years of experience in High Availability and Enterprise Software, he has designed and implemented architectures since 2003.His specialization is in systems integration and has participated in projects mainly related to the financial sector. He has been an enterprise architect for BEA Systems and Oracle Inc., but he also enjoys Mobile Programming and Game Development. He considers himself a programmer before an architect, engineer, or developer.
Overview
Section 1: Apache Spark Fundamentals
Lecture 1 Course Overview
Lecture 2 Spark Introduction
Lecture 3 Spark Components
Lecture 4 Getting Started
Lecture 5 Introduction to Hadoop
Lecture 6 Hadoop Processes and Components
Lecture 7 HDFS and YARN
Lecture 8 Map Reduce
Lecture 9 Introduction to Scala
Lecture 10 Scala Programming Fundamentals
Lecture 11 Objects in Scala
Lecture 12 Collections
Lecture 13 Spark Execution
Lecture 14 Understanding RDD
Lecture 15 RDD Operations
Lecture 16 Loading and Saving Data in Spark
Lecture 17 Managing Key-Value Pairs
Lecture 18 Accumulators
Lecture 19 Writing a Spark Application
Section 2: Spark for Data Analysis in Scala
Lecture 20 The Course Overview
Lecture 21 Downloading the Competition Dataset
Lecture 22 Installing Spark Notebook
Lecture 23 Spark Abstractions – RDD, DataFrame
Lecture 24 Loading CSV data into DataFrame
Lecture 25 Different types of widgets supported for Spark Notebook for DataFrame visualizat
Lecture 26 Statistical Functions Supported by Spark
Lecture 27 Operations on DataFrame
Lecture 28 Feature Transformers
Lecture 29 Feature Selectors
Lecture 30 Architecture
Lecture 31 Algorithms: Linear Regression and Regression Trees
Section 3: Fast Data Processing Systems with SMACK Stack
Lecture 32 The Course Overview
Lecture 33 Modern Data-Processing Challenges
Lecture 34 The Data-Processing Pipeline Architecture
Lecture 35 SMACK Technologies
Lecture 36 Understanding Data Expert Profiles and Changing the Data Center Operations
Lecture 37 Scala Collections
Lecture 38 Iterators in Scala
Lecture 39 More Functions with Scala
Lecture 40 Actor Model In a Nutshell
Lecture 41 Working with Actors
Lecture 42 Spark Concepts
Lecture 43 Resilient Distributed Datasets
Lecture 44 Spark in Cluster Mode
Lecture 45 Spark Streaming
Lecture 46 NoSQL
Lecture 47 Apache Cassandra Installation
Lecture 48 Backup and Compression
Lecture 49 Recovery Techniques
Lecture 50 Recovery Techniques – DBMS Optimization, Bloom Filter, and More
Lecture 51 The Spark Cassandra Connector
Lecture 52 Introduction to the Spark Cassandra Connector
Lecture 53 Cassandra and Spark Streaming Basics
Lecture 54 Functions with Cassandra
Lecture 55 Akka and Cassandra
Lecture 56 Introducing Kafka
Lecture 57 Installation
Lecture 58 Cluster
Lecture 59 Architecture
Lecture 60 Producers
Lecture 61 Consumers
Lecture 62 Integration and Administration
Lecture 63 Akka, Spark, and Kafka
Lecture 64 Kafka and Cassandra
Lecture 65 The Apache Mesos Architecture
Lecture 66 Resource Allocation
Lecture 67 Running a Mesos Cluster on a Private Data Center
Lecture 68 Scheduling and Managing the Frameworks
Lecture 69 Apache Aurora
Lecture 70 Singularity
Lecture 71 Apache Spark on Apache Mesos
Lecture 72 Apache Cassandra on Apache Mesos
Lecture 73 Apache Kafka on Apache Mesos
Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.


Homepage

https://www.udemy.com/course/learning-path-smack-getting-started-with-the-smack-stack/