Learning Path: SMACK: Getting Started with the SMACK Stack
Learning Path: SMACK: Getting Started with the SMACK Stack, available at $19.99, has an average rating of 3.3, with 73 lectures, 3 quizzes, based on 15 reviews, and has 138 subscribers.
You will learn about Basic concepts of Scala Analysing data using Spark in Scala Creation of fast data processing using SMACK Stack This course is ideal for individuals who are Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models. It is particularly useful for Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
Enroll now: Learning Path: SMACK: Getting Started with the SMACK Stack
Summary
Title: Learning Path: SMACK: Getting Started with the SMACK Stack
Price: $19.99
Average Rating: 3.3
Number of Lectures: 73
Number of Quizzes: 3
Number of Published Lectures: 73
Number of Published Quizzes: 3
Number of Curriculum Items: 76
Number of Published Curriculum Objects: 76
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Basic concepts of Scala
- Analysing data using Spark in Scala
- Creation of fast data processing using SMACK Stack
Who Should Attend
- Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
Target Audiences
- Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
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 data: 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 Apariciois 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.
Course Curriculum
Chapter 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
Chapter 2: Spark for Data Analysis in Scala
Lecture 1: The Course Overview
Lecture 2: Downloading the Competition Dataset
Lecture 3: Installing Spark Notebook
Lecture 4: Spark Abstractions – RDD, DataFrame
Lecture 5: Loading CSV data into DataFrame
Lecture 6: Different types of widgets supported for Spark Notebook for DataFrame visualizat
Lecture 7: Statistical Functions Supported by Spark
Lecture 8: Operations on DataFrame
Lecture 9: Feature Transformers
Lecture 10: Feature Selectors
Lecture 11: Architecture
Lecture 12: Algorithms: Linear Regression and Regression Trees
Chapter 3: Fast Data Processing Systems with SMACK Stack
Lecture 1: The Course Overview
Lecture 2: Modern Data-Processing Challenges
Lecture 3: The Data-Processing Pipeline Architecture
Lecture 4: SMACK Technologies
Lecture 5: Understanding Data Expert Profiles and Changing the Data Center Operations
Lecture 6: Scala Collections
Lecture 7: Iterators in Scala
Lecture 8: More Functions with Scala
Lecture 9: Actor Model In a Nutshell
Lecture 10: Working with Actors
Lecture 11: Spark Concepts
Lecture 12: Resilient Distributed Datasets
Lecture 13: Spark in Cluster Mode
Lecture 14: Spark Streaming
Lecture 15: NoSQL
Lecture 16: Apache Cassandra Installation
Lecture 17: Backup and Compression
Lecture 18: Recovery Techniques
Lecture 19: Recovery Techniques – DBMS Optimization, Bloom Filter, and More
Lecture 20: The Spark Cassandra Connector
Lecture 21: Introduction to the Spark Cassandra Connector
Lecture 22: Cassandra and Spark Streaming Basics
Lecture 23: Functions with Cassandra
Lecture 24: Akka and Cassandra
Lecture 25: Introducing Kafka
Lecture 26: Installation
Lecture 27: Cluster
Lecture 28: Architecture
Lecture 29: Producers
Lecture 30: Consumers
Lecture 31: Integration and Administration
Lecture 32: Akka, Spark, and Kafka
Lecture 33: Kafka and Cassandra
Lecture 34: The Apache Mesos Architecture
Lecture 35: Resource Allocation
Lecture 36: Running a Mesos Cluster on a Private Data Center
Lecture 37: Scheduling and Managing the Frameworks
Lecture 38: Apache Aurora
Lecture 39: Singularity
Lecture 40: Apache Spark on Apache Mesos
Lecture 41: Apache Cassandra on Apache Mesos
Lecture 42: Apache Kafka on Apache Mesos
Instructors
-
Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 4 votes
- 3 stars: 2 votes
- 4 stars: 5 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
Learning Path: SMACK: Getting Started with the SMACK Stack
Learning Path: SMACK: Getting Started with the SMACK Stack, available at $19.99, has an average rating of 3.3, with 73 lectures, 3 quizzes, based on 15 reviews, and has 138 subscribers.
You will learn about Basic concepts of Scala Analysing data using Spark in Scala Creation of fast data processing using SMACK Stack This course is ideal for individuals who are Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models. It is particularly useful for Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
Enroll now: Learning Path: SMACK: Getting Started with the SMACK Stack
Summary
Title: Learning Path: SMACK: Getting Started with the SMACK Stack
Price: $19.99
Average Rating: 3.3
Number of Lectures: 73
Number of Quizzes: 3
Number of Published Lectures: 73
Number of Published Quizzes: 3
Number of Curriculum Items: 76
Number of Published Curriculum Objects: 76
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Basic concepts of Scala
- Analysing data using Spark in Scala
- Creation of fast data processing using SMACK Stack
Who Should Attend
- Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
Target Audiences
- Data Analysts, Data Scientists, and Business Analysts can use this course to make highly precise and fast data models.
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 data: 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 Apariciois 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.
Course Curriculum
Chapter 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
Chapter 2: Spark for Data Analysis in Scala
Lecture 1: The Course Overview
Lecture 2: Downloading the Competition Dataset
Lecture 3: Installing Spark Notebook
Lecture 4: Spark Abstractions – RDD, DataFrame
Lecture 5: Loading CSV data into DataFrame
Lecture 6: Different types of widgets supported for Spark Notebook for DataFrame visualizat
Lecture 7: Statistical Functions Supported by Spark
Lecture 8: Operations on DataFrame
Lecture 9: Feature Transformers
Lecture 10: Feature Selectors
Lecture 11: Architecture
Lecture 12: Algorithms: Linear Regression and Regression Trees
Chapter 3: Fast Data Processing Systems with SMACK Stack
Lecture 1: The Course Overview
Lecture 2: Modern Data-Processing Challenges
Lecture 3: The Data-Processing Pipeline Architecture
Lecture 4: SMACK Technologies
Lecture 5: Understanding Data Expert Profiles and Changing the Data Center Operations
Lecture 6: Scala Collections
Lecture 7: Iterators in Scala
Lecture 8: More Functions with Scala
Lecture 9: Actor Model In a Nutshell
Lecture 10: Working with Actors
Lecture 11: Spark Concepts
Lecture 12: Resilient Distributed Datasets
Lecture 13: Spark in Cluster Mode
Lecture 14: Spark Streaming
Lecture 15: NoSQL
Lecture 16: Apache Cassandra Installation
Lecture 17: Backup and Compression
Lecture 18: Recovery Techniques
Lecture 19: Recovery Techniques – DBMS Optimization, Bloom Filter, and More
Lecture 20: The Spark Cassandra Connector
Lecture 21: Introduction to the Spark Cassandra Connector
Lecture 22: Cassandra and Spark Streaming Basics
Lecture 23: Functions with Cassandra
Lecture 24: Akka and Cassandra
Lecture 25: Introducing Kafka
Lecture 26: Installation
Lecture 27: Cluster
Lecture 28: Architecture
Lecture 29: Producers
Lecture 30: Consumers
Lecture 31: Integration and Administration
Lecture 32: Akka, Spark, and Kafka
Lecture 33: Kafka and Cassandra
Lecture 34: The Apache Mesos Architecture
Lecture 35: Resource Allocation
Lecture 36: Running a Mesos Cluster on a Private Data Center
Lecture 37: Scheduling and Managing the Frameworks
Lecture 38: Apache Aurora
Lecture 39: Singularity
Lecture 40: Apache Spark on Apache Mesos
Lecture 41: Apache Cassandra on Apache Mesos
Lecture 42: Apache Kafka on Apache Mesos
Instructors
-
Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 4 votes
- 3 stars: 2 votes
- 4 stars: 5 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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