課程介紹

The world of Hadoop and “Big Data” can be intimidating – hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this Hadoop tutorial, you’ll not only understand what those systems are and how they fit together – but you

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哪些人適合這堂課？

• Software engineers and programmers who want to understand the larger Hadoop ecosystem, and use it to store, analyze, and vend “big data” at scale.
• Project, program, or product managers who want to understand the lingo and high-level architecture of Hadoop.
• Data analysts and database administrators who are curious about Hadoop and how it relates to their work.
• System architects who need to understand the components available in the Hadoop ecosystem, and how they fit together.

學習目標

• Design distributed systems that manage “big data” using Hadoop and related technologies.
• Use HDFS and MapReduce for storing and analyzing data at scale.
• Use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
• Analyze relational data using Hive and MySQL
• Analyze non-relational data using HBase, Cassandra, and MongoDB
• Query data interactively with Drill, Phoenix, and Presto
• Choose an appropriate data storage technology for your application
• Understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
• Publish data to your Hadoop cluster using Kafka, Sqoop, and Flume
• Consume streaming data using Spark Streaming, Flink, and Storm

課程介紹

Learn the latest Big Data Technology – Spark! And learn to use it with one of the most popular programming languages, Python!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically design

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哪些人適合這堂課？

• Someone who knows Python and would like to learn how to use it for Big Data
• Someone who is very familiar with another programming language and needs to learn Spark

學習目標

• Use Python and Spark together to analyze Big Data
• Learn how to use the new Spark 2.0 DataFrame Syntax
• Work on Consulting Projects that mimic real world situations!
• Classify Customer Churn with Logisitic Regression
• Use Spark with Random Forests for Classification
• Learn how to use Spark’s Gradient Boosted Trees
• Use Spark’s MLlib to create Powerful Machine Learning Models
• Learn about the DataBricks Platform!
• Get set up on Amazon Web Services EC2 for Big Data Analysis
• Learn how to use AWS Elastic MapReduce Service!
• Learn how to leverage the power of Linux with a Spark Environment!
• Create a Spam filter using Spark and Natural Language Processing!
• Use Spark Streaming to Analyze Tweets in Real Time!

課程介紹

New! Completely updated and re-recorded for Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data:

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哪些人適合這堂課？

• Software engineers who want to expand their skills into the world of big data processing on a cluster
• If you have no previous programming or scripting experience, you’ll want to take an introductory programming course first.

學習目標

• Frame big data analysis problems as Apache Spark scripts
• Develop distributed code using the Scala programming language
• Optimize Spark jobs through partitioning, caching, and other techniques
• Build, deploy, and run Spark scripts on Hadoop clusters
• Process continual streams of data with Spark Streaming
• Transform structured data using SparkSQL, DataSets, and DataFrames
• Traverse and analyze graph structures using GraphX
• Analyze massive data set with Machine Learning on Spark

課程介紹

New! Updated for Spark 3, more hands-on exercises, and a stronger focus on DataFrames and Structured Streaming.

“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark.

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哪些人適合這堂課？

• People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but that’s not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you.
• If you’ve never written a computer program or a script before, this course isn’t for you – yet. I suggest starting with a Python course first, if programming is new to you.
• If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark.
• If you’re training for a new career in data science or big data, Spark is an important part of it.

學習目標

• Use DataFrames and Structured Streaming in Spark 3
• Frame big data analysis problems as Spark problems
• Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
• Install and run Apache Spark on a desktop computer or on a cluster
• Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
• Implement iterative algorithms such as breadth-first-search using Spark
• Use the MLLib machine learning library to answer common data mining questions
• Understand how Spark SQL lets you work with structured data
• Understand how Spark Streaming lets your process continuous streams of data in real time
• Tune and troubleshoot large jobs running on a cluster
• Share information between nodes on a Spark cluster using broadcast variables and accumulators
• Understand how the GraphX library helps with network analysis problems

課程介紹

The main objective of this course is to help you understand the Complex Architectures of Hadoop and its components, guide you in the right direction to start with, and quickly start working with Hadoop and its components.

It covers everything tha

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哪些人適合這堂課？

• This course can be opted by anyone (students, developer, manager) who is interested to learn big data. This course assumes everyone as a beginner, and teaches all fundamentals of Big Data, Hadoop and its complex architecture.

學習目標

• Understand different technology trends, salary trends, Big Data market and different job roles in Big Data
• Understand what Hadoop is for, and how it works
• Understand complex architectures of Hadoop and its component
• Hadoop installation on your machine
• Understand how MapReduce, Hive and Pig can be used to analyze big data sets
• High quality documents
• Demos: Running HDFS commands, Hive queries, Pig queries
• Sample data sets and scripts (HDFS commands, Hive sample queries, Pig sample queries, Data Pipeline sample queries)
• Start writing your own codes in Hive and Pig to process huge volumes of data
• Design your own data pipeline using Pig and Hive
• Understand modern data architecture: Data Lake
• Practice with Big Data sets

課程介紹

Important update: Effective January 31, 2021, all Cloudera software will require a valid subscription and only be accessible via the paywall. The sandbox can still be downloaded, but the full install requires a Cloudera subscription to get access to

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哪些人適合這堂課？

• This course is for anyone that wants to know how Big Data works, and what technologies are involved
• The main focus is on the Hadoop ecosystem. We don’t cover any technologies not on the Hortonworks Data Platform Stack
• The course compares MapR, Cloudera, and Hortonworks, but we only use the Hortonworks Data Platform (HDP) in the demos

學習目標

• Process Big Data using batch
• Process Big Data using realtime data
• Be familiar with the technologies in the Hadoop Stack
• Be able to install and configure the Hortonworks Data Platform (HDP)

課程介紹

“Big data” analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduce and Hadoop. Ever wonder how Google manages to analyze the entire Internet on a continual basis? You’ll

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哪些人適合這堂課？

• This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon’s Elastic MapReduce service – but we won’t spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don’t know Python, you’ll need to be able to pick it up based on the examples we give. If you’re new to programming, you’ll want to learn a programming or scripting language before taking this course.

學習目標

• Understand how MapReduce can be used to analyze big data sets
• Write your own MapReduce jobs using Python and MRJob
• Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce
• Chain MapReduce jobs together to analyze more complex problems
• Analyze social network data using MapReduce
• Analyze movie ratings data using MapReduce and produce movie recommendations with it.
• Understand other Hadoop-based technologies, including Hive, Pig, and Spark
• Understand what Hadoop is for, and how it works

課程介紹

Apache NiFi (Cloudera DataFlows – ex Hortonworks DataFlow) is an innovative technology to build data flows and solve your streaming challenges?

In today’s big data world, fast data is becoming increasingly important. Streaming data at scale and rapi

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哪些人適合這堂課？

• Beginners who want to get started on learning Apache NiFi
• Architects who want to get an overview of Apache NiFi

學習目標

• Install and configure Apache NiFi
• Design Apache NiFi Architecture
• Master core functionalities like FlowFile, FlowFile Processor, Connection, Flow Controller, Process Groups, etc.
• Use NiFi to stream Data between different systems at scale
• Monitor Apache NiFi
• Integrate NiFi with Apache Kafka
• Integration NiFi with MongoDB

課程介紹

面對撲面而來的資料浪潮，包含 Google、Facebook、Intel、Pfizer、Bank of America 等國際級企業，都已經採用 R 語言進行資料分析，許多全球一流大學如 Stanford、Johns Hopkins 和 UCLA 也將 R 視為資料分析課程的先修科目。根據國際知名的 KDnuggets 論壇統計，R 語言已經連續三年獲得資料科學家最常使用的資料分析語言第一名的殊榮。

《R 語言探索之旅》以問題導向的方式教學R語言。從資料萃取和資料清理開始，有目的的進行探索性資

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哪些人適合這堂課？

• 適合所有想在短時間上手 R 語言的朋友

學習目標

• 認識 R 語言的編輯環境
• 學會 R 語言的基本操作
• 熟悉 R 語言的資料整理
• 體驗 R 語言的資料分析與視覺化

課程介紹

Tableau的高级分析以LOD为代表。依赖于最受欢迎的中文LOD博客全新升级而成，从“非IT”的视角出发，建立了广义LOD的概念和体系，帮助Tableau业务分析师理解并使用高级分析表达式。

2021年全新升级内容，核心内容重新录制、重新发布，特别是关于“广义LOD表达式”的框架、四种结构性分析的典型案例、购物篮分析的高级案例等内容。同时覆盖了广受欢迎的官方15大LOD表达式内容。

•行级别函数与聚合函数的区别——理解聚合的两个层次

•行级别函数 字符串函数｜日期函数｜——所有的行级别表

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哪些人適合這堂課？

• Tableau中高级用户
• 大数据业务分析用户
• 期望参加Tableau认证考试的用户

學習目標

• 理解LOD-详细级别
• Tableau的多种详细级别及其表达式
• Fixed /include/exclude LOD的语法与用法
• 15大详细级别表达式的解读
• 表计算的逻辑和10大案例解读