The document discusses Stratio Sparta, a real-time aggregation platform developed to simplify big data applications using technologies like Apache Spark and Kafka. Sparta aims to eliminate repetitive coding by providing a declarative workflow approach, focusing on high performance, scalability, and fault tolerance. It also explores various use cases and the evolution from version 1.0 to 2.0, emphasizing the platform's capabilities and features.

2. Building Big Data applications with Stratio Sparta
SPARTA 2.0

3. José Carlos García Serrano
Big Data Architect in Stratio.
I am from Granada and Computer Science Engineer in the
ETSII, post graduate in Big Data and certificate in Spark and
AWS
def fanBoy(): Seq[Skills] = {
val functional = Seq(Scala, Akka)
val processing = Seq(Spark)
val noSql = Seq(MongoDB, Cassandra)
functional ++ processing ++ noSql
}
def aLongTimeAgo(): Seq[Skills] = {
val programming = Seq(Delphi, C++)
val processing = Seq(Hadoop)
val sql = Seq(Interbase, FireBird)
programming ++ processing ++ sql
}
Sparktan one

4. Sparktan two
Javier Yuste Checa
Product Owner at Stratio.
I am from Madrid and I have a Master in Computer Science by
the UPM, with 10+ years of hands-on software development
experience.
I love travelling and motorbikes
I like:
● Software development
● Agile methodologies
● Product development
● Make things happen

5. What is Sparta?1
Questions4
New version2 Use case - Demo3
Index

6. WHAT IS SPARTA?
1

7. © Stratio 2016. Confidential, All Rights Reserved.
Towards a generic real-time aggregation platform
7
At Stratio, we have implemented several real-time analytic projects based on Apache Spark,
Kafka, Flume, Cassandra, or MongoDB.
These technologies were always a perfect fit, but soon we found ourselves writing the same
pieces of integration code over and over again.
This is how SPARTA was born

8. © Stratio 2016. Confidential, All Rights Reserved.
SPARTA - Beginning
8
The goals
○ Pure Spark!
○ No need of coding, only declarative workflows
○ Data continuously streamed in and processed in near real-time
○ Ready to use out of the box
○ Plug & play: flexible workflows (inputs, outputs, parsers, etc…)
○ High performance
○ Scalable and fault tolerant
○ Stateful operations with OLAP engine
○ Execute SQL over Streaming and batch data

9. © Stratio 2016. Confidential, All Rights Reserved.
SPARTA 1.0
9
Kafka
Flume
Crossdata
RabbitMQ
Socket
WebSocket
HDFS/S3
Twitter
MongoDB
Cassandra
ElasticSearch
Redis
JDBC
Crossdata
CSV
Parquet
Http
Kafka
HDFS/S3
Http Rest
Avro

10. © Stratio 2016. Confidential, All Rights Reserved.
SPARTA 1.0
10

11. NEW VERSION
2

12. © Stratio 2016. Confidential, All Rights Reserved.
SPARTA 2.0
12
Security
○ Data
○ Resource
○ Service
Distributed
Multiprocessing
○ Streaming
○ Batch
○ SQL / OLAP
○ Machine learning
SaaS & Multi
Tenancy
○ Sparta as a Service
○ Multi User
○ Multi Tenant
○ Multi Instance

13. © Stratio 2016. Confidential, All Rights Reserved.
EOS - Data centric Suite
13

14. USE CASES
DEMO
3

15. © Stratio 2017. Confidential, All Rights Reserved.
Big Data use case
15
• Different technologies and skills
needed
• Experience solving complex problems
• Knowledge of Big data architectures

16. © Stratio 2017. Confidential, All Rights Reserved.
Big Data use case
16

17. © Stratio 2017. Confidential, All Rights Reserved.
Simple use case
17
Input Test:
{
"event":"Big data spain",
"speech":"Sparta",
"attendees":10,
“address”: ”Kinepolis”
}
Select:
speech, attendees
Cube:
dimensions:
speech
operations:
sum(attendees)

18. BIG DATA
CHILD`S PLAY
THANKS
Contacts:
jcgarcia@stratio.com
es.linkedin.com/in/gserranojc
jyuste@stratio.com
es.linkedin.com/in/jyustecheca