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Stratio Sparta 2.0


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Looking to build Kappa architectures or SMACK applications or to use distributed technologies such as Spark, Kafka, Elasticsearch and Hadoop? Do you want to build your own data-centric platform? Are you lacking a development team with Scala and Spark skills? Are you managing to move towards full Digital Transformation?
Have our questions stressed you out enough? Then you are ready for our latest release!
Our next product release will include Sparta 2.0, ready to solve the above-mentioned issues. Stratio clients will be able to build Big Data processes and data pipeline workflows in minutes with an amazing UI, integrated with Spark, Kafka and several Big Data technologies.
In this session, we will show how Sparta 2.0 and its workflow jobs are a key piece in a data-centric platform and how it is integrated with a PaaS (DC/OS). We will also show how Stratio has made sure all pieces within the platform, including Sparta 2.0 are secured.
By: José Carlos García and Javier Yuste

Published in: Technology
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Stratio Sparta 2.0

  1. 1. Building Big Data applications with Stratio Sparta SPARTA 2.0
  2. 2. 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
  3. 3. 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
  4. 4. What is Sparta?1 Questions4 New version2 Use case - Demo3 Index
  5. 5. WHAT IS SPARTA? 1
  6. 6. © 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
  7. 7. © 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
  8. 8. © 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
  9. 9. © Stratio 2016. Confidential, All Rights Reserved. SPARTA 1.0 10
  10. 10. NEW VERSION 2
  11. 11. © 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
  12. 12. © Stratio 2016. Confidential, All Rights Reserved. EOS - Data centric Suite 13
  13. 13. USE CASES DEMO 3
  14. 14. © 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
  15. 15. © Stratio 2017. Confidential, All Rights Reserved. Big Data use case 16
  16. 16. © 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)
  17. 17. BIG DATA CHILD`S PLAY THANKS Contacts: