02-08-2024, 11:16 PM
[center]![[Image: hv4rYi8KHfnTVpcBKrqsSRDbXSKaygyb.jpg]](https://sanet.pics/storage-10/0224/hv4rYi8KHfnTVpcBKrqsSRDbXSKaygyb.jpg)
Published 2/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 441.18 MB | Duration: 2h 4m
Big Data[/center]
What you'll learn
Introduction To Big Data
Understand the History and background of Big data and Hadoop
Describe the Big Data landscape including examples of real world big data problems
Explain the 5 V's of Big Data (volume, velocity, variety, veracity, and value
Understand the foundational principles that have made Big Data so successful
Requirements
Basic Programming knowledge, SQL and Data knowledge preferred
Description
Overview : The Introduction to Big Data course is the first stop in the Big Data curriculum series coming up at Stanford. It will help you get started with the background and introduction of the history of the Big Data.Description:The 'Introduction to Big Data' course is your gateway to the dynamic world of Big Data and Spark. Dive into the history and fundamentals of Big Data, gaining insights into Big Data Ecosystem technologies, including HDFS, MapReduce, Sqoop, Flume, Hive, Pig, Mahout for Machine Learning, R Connector, Ambari, Zookeeper, Oozie, and No-SQL tools like HBase. This course offers an in-depth understanding of the Big Data ecosystem, both pre and post Apache Spark era. Learn the core fundamentals and architecture of Spark and put your knowledge into practice on the Apache Spark Databricks Cloud. Get started on your Big Data journey.Learning Outcome : In this Course you'll get an introduction to working with Big Data Ecosystem technologies (HDFS, MapReduce, Sqoop, Flume, Hive, Pig, Mahout (Machine Learning), R Connector, Ambari, Zookeeper, Oozie and No-SQL like HBase) for Big Data scenarios.Understand the History and background of Big data and HadoopDescribe the Big Data landscape including examples of real world big data problemsExplain the 5 V's of Big Data (volume, velocity, variety, veracity, and value)
Overview
Section 1: Introduction
Lecture 1 Data Types
Lecture 2 V Views of Big Data
Lecture 3 History of Hadoop
Lecture 4 What is Hadoop
Lecture 5 Hadoop Eco System & Hadoop Components
Lecture 6 Big Data Ecosystem
Lecture 7 Big Data Storage
Lecture 8 Big Data Flavor
Lecture 9 Big Data on Cloud
Lecture 10 Spark EssentialIn (Challenges with Mapreduce)
Lecture 11 What is Apache Spark
Lecture 12 Big Data after Spark Ecosystem
Lecture 13 Spark Feature & Spark Components
Lecture 14 Spark Landscape (Spark in Databricks)
This course is designed for anyone willing to develop a foundation for Big Data.
Screenshots
Published 2/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 441.18 MB | Duration: 2h 4m
Big Data[/center]
What you'll learn
Introduction To Big Data
Understand the History and background of Big data and Hadoop
Describe the Big Data landscape including examples of real world big data problems
Explain the 5 V's of Big Data (volume, velocity, variety, veracity, and value
Understand the foundational principles that have made Big Data so successful
Requirements
Basic Programming knowledge, SQL and Data knowledge preferred
Description
Overview : The Introduction to Big Data course is the first stop in the Big Data curriculum series coming up at Stanford. It will help you get started with the background and introduction of the history of the Big Data.Description:The 'Introduction to Big Data' course is your gateway to the dynamic world of Big Data and Spark. Dive into the history and fundamentals of Big Data, gaining insights into Big Data Ecosystem technologies, including HDFS, MapReduce, Sqoop, Flume, Hive, Pig, Mahout for Machine Learning, R Connector, Ambari, Zookeeper, Oozie, and No-SQL tools like HBase. This course offers an in-depth understanding of the Big Data ecosystem, both pre and post Apache Spark era. Learn the core fundamentals and architecture of Spark and put your knowledge into practice on the Apache Spark Databricks Cloud. Get started on your Big Data journey.Learning Outcome : In this Course you'll get an introduction to working with Big Data Ecosystem technologies (HDFS, MapReduce, Sqoop, Flume, Hive, Pig, Mahout (Machine Learning), R Connector, Ambari, Zookeeper, Oozie and No-SQL like HBase) for Big Data scenarios.Understand the History and background of Big data and HadoopDescribe the Big Data landscape including examples of real world big data problemsExplain the 5 V's of Big Data (volume, velocity, variety, veracity, and value)
Overview
Section 1: Introduction
Lecture 1 Data Types
Lecture 2 V Views of Big Data
Lecture 3 History of Hadoop
Lecture 4 What is Hadoop
Lecture 5 Hadoop Eco System & Hadoop Components
Lecture 6 Big Data Ecosystem
Lecture 7 Big Data Storage
Lecture 8 Big Data Flavor
Lecture 9 Big Data on Cloud
Lecture 10 Spark EssentialIn (Challenges with Mapreduce)
Lecture 11 What is Apache Spark
Lecture 12 Big Data after Spark Ecosystem
Lecture 13 Spark Feature & Spark Components
Lecture 14 Spark Landscape (Spark in Databricks)
This course is designed for anyone willing to develop a foundation for Big Data.
Screenshots
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