Big Data learning – Working groups 2020

Learning a new solution or building an architecture for a specific use case is never easy, especially when you are trying to work alone on such an endeavour – thus this year we will debut a new way of learning specific big data solutions/use cases: working groups.

What will these working groups mean:

  • A predefined topic (see below the topics for 2020) that will be either understanding a big data solution or building a use case;
  • A group of 5 participants and one predefined driver per group – the scope of the driver is (besides being part of the group) to organize the groups, provide the meeting locations and the cloud infrastructure needed for installing the studied solution;
  • 5 physical meetings every 2 weeks (thus a 10 weeks time window for each working group). The meetings will take place either during the week (5PM – 9PM) or Saturdays morning (10AM – 2PM).
  • Active participation/contribution from each participant, for example each participant will have to present in 2 of the meetings to the rest of the group;
  • Some study @ home between the sessions;

More details and registration here.

Intro to Spark Structured Streaming using Scala and Apache Kafka

Intro to Spark Structured Streaming using Scala and Apache Kafka

Workshop date & duration: February 1st, 2020, 9:30 – 14:00, 30 min break included
Trainer: Valentina Crisan, Maria Catana
Location:  eSolutions Academy, Budişteanu Office Building, strada General Constantin Budişteanu Nr. 28C, etaj 1, Sector 1, Bucureşti
Price: 150 RON (including VAT)
Number of places: 10 no more left
Languages: Scala & SQL


Starting with Spark 2.0 structured streaming processing was introduced, modeling the stream as an unbounded/infinite table –  a big architectural change if we look at the batch model (Dstream) that existed prior to Spark 2.0. The workshop will introduce you into how Spark can read, process & analyze streams of data –  we will use stream data from Apache Kafka and Scala & SQL for reading/processing/analyzing the data. We will discuss as well stateless vs stateful queries and how Spark handles out of order data in case of aggregation queries.


You can check out the agenda and register here.