Name: Data Warehousing with Spark (Take II)
Start: 2018-09-06T18:30:00+01:00
End: 2018-09-06T21:00:00+01:00
Location: Tenable

Hi All, quick update ...

Ruben has a lot of material. Means we are going to break up his talk into two sessions ... and we are adding some (optional) bonus material at the end.

The agenda is as follows ...

- 6:30pm: Pre-Meetup drinks
- 7:00pm: Session 1 with Reuben
- 7:40pm: (More) Pizza and Beer
- 8:00pm: Session 2 with Rueben
- 8:30pm: (Optional) Session 3 with Roland (Using Haskell to write Spark jobs)
- 8:50pm Wrap-up/Close

Going on vacation now. Will be back on Sep., 03rd.

See you on the 6th.

Regards ...
Roland
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Hi All,

before everybody leaves for the well-deserved summer-vacation :) ...

Here it comes: The invite for the next Spark Meetup.

This time Tenable is going to host us.

And we already have one excellent talk lined up (please see below).

And I am thinking to complement this talk with 2-3 short lightening talks (10 min presentation + 3 mins of Q&A).

Soooooo ... if you have something interesting to share/talk about ... let me know.

See you in September!!!

Regards ...
Roland

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Title: Lessons learned implementing a Big Data Warehouse in a fortune 5 company

Presenter: Vicente Ruben Del Pino Ruiz

Bio: Data professional with more than 10 years of experience working in Business Intelligence and Data Warehouse environments. I have experience working with relational databases from SQL Server 2005 through SQL Server 2017, NoSQL databases like MongoDB, CouchBase and Cloud services like Azure and AWS. Recently started working with Big Data related technologies.

Target Audience: Business Intelligence and Data Warehouse professionals who are working or starting to work with Big Data. Any professional who wants to see how develop a Data Warehouse using Kimball approach in a Big Data environment, and learn which challenges are facing ahead and what are the common pitfalls when starting to work in Big Data coming from a relational database system.

Abstract: From using relational databases to using Spark and Hadoop to integrate more than 70 different data sources into a Big Data Warehouse. Lessons learned, challenges and common pitfalls faced in the process when the background is relational databases and your journey with Big Data has just started.

Roland Tritsch

Dublin Spark Meetup

Technology

Open Source

Scala

Big Data

Natural Language Processing

Machine Learning

Data Analytics

Apache Spark