The YARN timeline service v.2 addresses the following two major challenges:
- Enhancing the scalability and reliability of the timeline service
- Improving usability by introducing flows and aggregation
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Introduction to Hadoop
- Hadoop Distributed File System
- MapReduce framework
- YARN
- Other changes
- Installing Hadoop 3 
- Summary
- Overview of Big Data Analytics
- Introduction to data analytics
- Introduction to big data
- Distributed computing using Apache Hadoop
- The MapReduce framework
- Hive
- Apache Spark
- Visualization using Tableau
- Summary
- Big Data Processing with MapReduce
- Scientific Computing and Big Data Analysis with Python and Hadoop
- Statistical Big Data Computing with R and Hadoop
- Batch Analytics with Apache Spark
- Real-Time Analytics with Apache Spark
- Batch Analytics with Apache Flink
- Stream Processing with Apache Flink
- Introduction to streaming execution model
- Data processing using the DataStream API
- Summary
- Visualizing Big Data
- Introduction to Cloud Computing
- Using Amazon Web Services
- Amazon Elastic Compute Cloud
- Launching multiple instances of an AMI
- What is AWS Lambda?
- Introduction to Amazon S3
- Getting started with Amazon S3
- Comprehensive security and compliance capabilities
- Query in place
- Flexible management
- Most supported platform with the largest ecosystem
- Easy and flexible data transfer
- Backup and recovery
- Data archiving
- Data lakes and big data analytics
- Hybrid Cloud storage
- Cloud-native application data
- Disaster recovery
- Amazon DynamoDB
- Amazon Kinesis Data Streams
- AWS Glue
- Amazon EMR
- Summary
YARN timeline service v.2
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