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Frank McSherry, Chief Scientist
How to use Standard SQL over Kafka
From the basics to advanced use cases
You can write SQL and make
streaming infrastructure
A brief history of SQL systems
(spoiler: it ends up with streams)
OLTP
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
OLTP
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
OLTP OLAP
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
OLTP OLAP
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
(row-based) (columnar)
different designs
OLTP OLAP
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
(row-based) (columnar)
OLTP OLVM
Reads
Writes
Transactions
Analytics
Dashboards
Monitoring
(pull) (push)
different designs
Analytics
Dashboards
Monitoring
OLTP
Kafka
OLTP
Kafka
Analytics
Dashboards
Monitoring
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
Stream connection 1/3:
SQL systems can have
streaming internals
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
Stream connection 2/3:
SQL systems can consume
streams as inputs
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
Stream connection 3/3:
SQL systems can produce
streams as outputs
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
sensors
readings
max_temp_by_region
Bonus Features 1/3:
SQL over unbounded data
This could be unbounded
This is all we need to store
Bonus Features 2/3:
Focus on recent events.
Like now() but for "event time"
Bonus Features 3/3:
Event-driven SQL queries
How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize
For each query in queries ...
... restrict by the query argument ...
... keep the top three regions.
You can write SQL and make
streaming infrastructure
Streams and SQL combine as
inputs flow to systems flow to outputs
Streams and SQL lead to new idioms
working with unbounded data,
focusing on recent events,
event-driven SQL queries,
... and more. (seriously)
https://materialize.com/kafka
Head to
for free shirts, cloud sign-up, and a guide to Kafka.
- @frankmcsherry
"best DB shirt released in 2020
(great design, soft material)"
@andy_pavlo

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How to use Standard SQL over Kafka: From the basics to advanced use cases | Frank McSherry, Materialize