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1° Meetup Kafka a Roma


01/02/18
https://www.meetup.com/it-IT/Roma-Kafka-meetup-group/
Big Data Streaming Analysis without code
STEFANO PAMPALONI
stefano.pampaloni@seacom.it
&
DARIO BALINZO
dariob@seacom.it
2
Let’s take a trip back in time. Each application has its
own database for storing information. But we want
that information elsewhere for analytics and
reporting.
3
We don't want to query the transactional system, so
we create a process to extract from the source to a
data warehouse / lake
4
Let’s take a trip back in time
We want to unify data from multiple systems, so
create conformed dimensions and batch processes
to federate our data. This is all batch driven, so
latency is built in by design.
5
Let’s take a trip back in time
As well as our data warehouse, we want to use our
transactional data to populate search replicas,
Graph databases, noSQL stores…all introducing
more point-to-point dependencies in our system
6
Let’s take a trip back in time
Ultimately we end up with a spaghetti architecture. It
can't scale easily, it's tightly coupled, it's generally
batch-driven and we can't get data when we want it
where we want it.
7
But…there's hope!
8
Apache Kafka, a distributed streaming platform,
enables us to decouple all our applications creating
data from those utilising it. We can create low-
latency streams of data, transformed as necessary.
Before
After
Kafka concepts
9
But…to use stream processing, we need to be Java
coders…don't we?
7
But…there's hope!
10
Happy days! We can actually build streaming data
pipelines using just our bare hands, configuration
files, and SQL.
KSQL 

An Open Source Streaming SQL
Engine for Apache Kafka
• Enables stream processing with zero coding required
• The simplest way to process streams of data in real-time
• Powered by Kafka: scalable, distributed, battle-tested
• All you need is Kafka–No complex deployments
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis
Meetup Kafka Roma Big Data Streaming Analysis

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Meetup Kafka Roma Big Data Streaming Analysis

  • 1. 1° Meetup Kafka a Roma 
 01/02/18
  • 3. Big Data Streaming Analysis without code STEFANO PAMPALONI stefano.pampaloni@seacom.it & DARIO BALINZO dariob@seacom.it
  • 4. 2 Let’s take a trip back in time. Each application has its own database for storing information. But we want that information elsewhere for analytics and reporting.
  • 5. 3 We don't want to query the transactional system, so we create a process to extract from the source to a data warehouse / lake
  • 6. 4 Let’s take a trip back in time We want to unify data from multiple systems, so create conformed dimensions and batch processes to federate our data. This is all batch driven, so latency is built in by design.
  • 7. 5 Let’s take a trip back in time As well as our data warehouse, we want to use our transactional data to populate search replicas, Graph databases, noSQL stores…all introducing more point-to-point dependencies in our system
  • 8. 6 Let’s take a trip back in time Ultimately we end up with a spaghetti architecture. It can't scale easily, it's tightly coupled, it's generally batch-driven and we can't get data when we want it where we want it.
  • 10. 8 Apache Kafka, a distributed streaming platform, enables us to decouple all our applications creating data from those utilising it. We can create low- latency streams of data, transformed as necessary.
  • 12. After
  • 14. 9 But…to use stream processing, we need to be Java coders…don't we?
  • 16. 10 Happy days! We can actually build streaming data pipelines using just our bare hands, configuration files, and SQL.
  • 17.
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  • 23. KSQL 
 An Open Source Streaming SQL Engine for Apache Kafka
  • 24. • Enables stream processing with zero coding required • The simplest way to process streams of data in real-time • Powered by Kafka: scalable, distributed, battle-tested • All you need is Kafka–No complex deployments