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Het Cisco Business Enablement Partner Program richt zich op het aanscherpen van de zakelijke vaardigheden van Cisco Channel Partners en klanten.

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Fortinet-certificering

Het Fortinet Network Security Expert (NSE) -programma is een training- en certificeringsprogramma op acht niveaus om ingenieurs van hun netwerkbeveiliging te leren voor Fortinet FW-vaardigheden en -ervaring.

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Find all the Extreme Networks online and instructor led class room based calendar here.

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Technische-certificering

We provide comprehensive curriculum of technical competency skills on the certification accomplishment.

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    • Big Data Processing with Apache Spark

    Enroll Now
    Duration
    2 Dagen
    Delivery
    (Online and onsite)
    Price
    Price Upon Request
    Processing big data in real-time is challenging due to scalability, information consistency, and fault tolerance. This Big Data Processing with Apache Spark course shows you how you can use Spark to make your overall analysis workflow faster and more efficient. You'll learn all about the core concepts and tools within the Spark ecosystem, like Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real-time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption. By the end of this course, you will not only have understood how to use machine learning extensions and structured streams but you will also be able to apply Spark in your own upcoming big data projects.  

    After completing this course, you will be able to:

    • Write your own Python programs that can interact with Spark
    • Implement data stream consumption using Apache Spark
    • Recognize common operations in Spark to process known data streams
    • Integrate Spark streaming with Amazon Web Services
    • Create a collaborative filtering model with Python and the movielens dataset
    • Apply processed data streams to Spark machine learning APIs

    Lesson 1: Introduction to Spark Distributed Processing

    • Introduction to Spark and Resilient Distributed Datasets
    • Operations Supported by the RDD API
    • Self-Contained Python Spark Programs
    • Introduction to SQL, Datasets, and DataFrames

    Lesson 2: Introduction to Spark Streaming

    • Streaming Architectures
    • Introduction to Discretized Streams
    • Windowing Operations
    • Introduction to Structured Streaming

    Lesson 3: Spark Streaming Integration with AWS

    • Spark Integration with AWS Services
    • Integrating AWS Kinesis and Python
    • AWS S3 Basic Functionality

    Lesson 4: Spark Streaming, ML, and Windowing Operations

    • Spark Integration with Machine Learning

    Big Data Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics. Although you don't need any knowledge of Spark, prior experience of working with Python is recommended.

    Hardware:

    For an optimal experience with the hands-on labs and other practical activities, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4GB RAM
    • Storage: 35 GB available space

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
    • PostgreSQL 9.0 or above
    • Python 3.0 or above
    • Spark 2.3.0
    • Amazon Web Services (AWS) account
    Overview
    Processing big data in real-time is challenging due to scalability, information consistency, and fault tolerance. This Big Data Processing with Apache Spark course shows you how you can use Spark to make your overall analysis workflow faster and more efficient. You'll learn all about the core concepts and tools within the Spark ecosystem, like Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real-time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption. By the end of this course, you will not only have understood how to use machine learning extensions and structured streams but you will also be able to apply Spark in your own upcoming big data projects.  

    After completing this course, you will be able to:

    • Write your own Python programs that can interact with Spark
    • Implement data stream consumption using Apache Spark
    • Recognize common operations in Spark to process known data streams
    • Integrate Spark streaming with Amazon Web Services
    • Create a collaborative filtering model with Python and the movielens dataset
    • Apply processed data streams to Spark machine learning APIs

    Lesson 1: Introduction to Spark Distributed Processing

    • Introduction to Spark and Resilient Distributed Datasets
    • Operations Supported by the RDD API
    • Self-Contained Python Spark Programs
    • Introduction to SQL, Datasets, and DataFrames

    Lesson 2: Introduction to Spark Streaming

    • Streaming Architectures
    • Introduction to Discretized Streams
    • Windowing Operations
    • Introduction to Structured Streaming

    Lesson 3: Spark Streaming Integration with AWS

    • Spark Integration with AWS Services
    • Integrating AWS Kinesis and Python
    • AWS S3 Basic Functionality

    Lesson 4: Spark Streaming, ML, and Windowing Operations

    • Spark Integration with Machine Learning

    Big Data Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics. Although you don't need any knowledge of Spark, prior experience of working with Python is recommended.

    Hardware:

    For an optimal experience with the hands-on labs and other practical activities, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4GB RAM
    • Storage: 35 GB available space

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
    • PostgreSQL 9.0 or above
    • Python 3.0 or above
    • Spark 2.3.0
    • Amazon Web Services (AWS) account
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