Last updated 9/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.38 GB | Duration: 10h 54m
Build scalable and efficient data processing platforms 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- 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 . 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 perfog 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. Raul 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, eeer, 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 ing 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 Chag 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. 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