Data can be from a data warehouse, batch mode file archives, near real-time updates, or instantaneous real-time updates from the Uber ride you just booked. Velocity refers to the increasing speed at which this data is created, and the increasing speed at which the data can be processed, stored, and analyzed by relational databases.
- 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
Velocity of data
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