GOTO is a vendor independent international software development conference with more that 90 top speaker and 1300 attendees. The conference cover topics such as .Net, Java, Open Source, Agile, Architecture and Design, Web, Cloud, New Languages and Processes

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Artem Aliev, Software Engineer at DataStax

Artem Aliev

Biography: Artem Aliev

Artem Aliev is a software developer in the DataStax Enterprise Analytics team. He works on integrating Apache Cassandra noSQL database with analytics solution like Spark and Hive. efore that he works as Big Data Solution Architect, Developer of Apache Harmony J2SE implementation and as a lead of performance optimisation team for  enterprise storage software at EMC corporation. o he can talk about the big data processing pipeline: from data on disks to machine learning and visualisation.

Twitter: @__ali

Presentation: Solving classical data analytic task by using modern distributed databases

Track: The State of Data / Time: Tuesday 11:30 - 12:20 / Location: Grandball

NoSQL databases have a limited query languages that are not suitable for analytical request. The classical solution provided by most of them is a Hadoop integration. That is not fast.  Thus a number of fast distributed, parallel query/computation engines appears recently to fix Hadoop  performance problems.
The presentation will show how to solve classical data analytic task by using modern distributed databases and in-memory engines using as example Spark and Cassandra. It will cover following topics:
  • Apache Spark benefits, architecture and Scala API. (Don't be afraid of Scala, we are here to help you)
  • Load and store data from Cassandra NoSQL database
  • Data enrichments and joins
  • Spark Machine learning and graph algorithms
Target audience: Software engineers and solution architects using or planning to use NoSql products for analytics, particularly Cassandra and Spark.

Workshop: Intro to Apache Spark

Track: Workshop / Time: Thursday 09:00 - 16:00 / Location: Margrethe

This one day session features a mix of hands-on technical exercises, brief lectures, demos, and case studies – structured to get developers up to speed leveraging Apache Spark for a range of use cases. 

Topics:

 

  • Overview of Big Data and Spark
  • Installing Spark Locally
  • Using Spark’s Core APIs in Scala, Java, & Python
  • Building Spark Applications
  • Deploying on a Big Data Cluster
  • Combining SQL, Machine Learning, and Streaming for Unified Pipelines

 

Target Audience:

This class is intended for developers who have some background developing apps in Java, Python, or Scala, but are not already familiar with Spark.