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WASP is now open source on GitHub!

WASP is a framework that enables the development of full stack complex real time applications like IoT for example, complex big data streaming analytics, massive data ingestion or data offload from legacy systems: streaming analytics is the next frontier and we are already there!!

 

 

We are glad to inform you that WASP is now available on github

Our business as big data specialists and system integrators is relying on open source communities like Spark, Cassandra, Kafka… so now is the time to “give back” and we hope this could be useful to someone out there.
In Italy there is a lack of open source culture but we are trying to change this, we have a lot to learn from companies like Confluent, Stratio, H2O, Cloudera etc.

Feel free to share this link ( http://www.agilelab.it/wasp-is-open-source-on-github/ ) on Social Networks like Linkedin, Twitter etc., it will be very much appreciated.

WASP relies on a kind of Kappa/Lambda architecture mainly leveraging Kafka and Spark.
WASP allows you to save time with devops architectures and integrating different components and it lets you focus on your data, business logic and algorithms, without worrying about typical big data problems like:

  • at least once or exactly once delivery
  • publishing your results in real time, be reactive
  • performing data quality on unstructured data
  • feeding different datastores from the same data flow in a safe way
  • periodically training a machine learning model

These are the main tech features that WASP provides out of the box:

  • Scalable ingestion, processing and storage
  • Complex Analytics in RT
  • Pluggable Business Logic
  • Pluggable Datastore ( Cassandra, Elastic, Hbase, Druid, Solr, Cloudera and many others )
  • Data feed publishing in RT via API
  • Integrated model server ( batch training, model hot deploy, real time prediction ) for machine learning workloads
  • Lambda and Kappa Architecture
  • Fast data interaction with Notebooks for DataScientists
  • Centralized log collection
  • Full dockerized
  • Business intelligence ready via JDBC
  • Composable Trasformation Pipeline as directed acyclic graph
  • Easily extensible

Here you can find the first release notes.

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