List of Tables

Chapter 5. Stateful stream processing

Table 5.1. A nonexhaustive list of stream processing frameworks

Chapter 6. Schemas

Table 6.1. Examples of schema technologies

Chapter 7. Archiving events

Table 7.1. Examples of distributed filesystems

Table 7.2. Tools for archiving our unified log from a distributed filesystem

Table 7.3. Examples of distributed batch-processing frameworks

Chapter 8. Railway-oriented processing

Table 8.1. The three standard streams supported by Unix programs

Chapter 10. Analytics-on-read

Table 10.1. Comparing the main attributes of analytics-on-read to analytics-on-write

Table 10.2. Strengths and weaknesses of Amazon Redshift

Table 10.3. Four EC2 instance types available for a Redshift cluster

Chapter 11. Analytics-on-write

Table 11.1. The status of OOPS’s delivery trucks

Table 11.2. Adding the Location timestamp column to our table

Table 11.3. Replacing our Miles since oil change metric with Mileage and Mileage at oil change

Table 11.4. Comparing features of Apache Samza and AWS Lambda

Table 11.5. Our DynamoDB row layout dictates the format of our pre-aggregated row in our Lambda function.

Appendix Appendix. AWS primer

Table A.1. AWS Services we’ll be using in this book