Real time product recommendations

We have two talks for this meetup!

The event will be in english, but we keep it bilingual-friendly. You can ask questions in french and translation will be provided.

Speaker 1 : Adrien Lacombe (Talend)

Title: Integrating Real-Time Data Streams with Spark and Kafka for Product Recommendations

Summary :

Join Adrien Lacombe from Talend as he delves into a deep technical discussion about the data science behind product recommendation engines. Working from real-time data collected from click-streams, and using Spark and Kafka, Adrien will show you how to produce meaningful analytics results in minutes. If you are using Kafka, Spark, or any real-time data science technologies, or even if you are just trying to get a better understanding of them, this event is for you.

Speaker 2 : Simon Ouellette (Faimdata)

Title : Time Series Analytics with Spark

Summary :

spark-timeseries is a Scala / Java / Python library for interacting with time series data on Apache Spark.

Time-series are an important part of data science applications, but are notoriously difficult in the context of distributed systems, due to their sequential nature. Getting this right is therefore a challenging but important element of progress in the universe of distributed systems applied to data science.

This talk will cover the current overall design of spark-timeseries, the current functionalities, and will provide some usage examples. Because the project is still at an early stage, the talk will also cover the current weaknesses and future improvements that are in the spark-timeseries project roadmap.

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Ce meetup sera en anglais, mais n'hésitez pas à poser vos questions en français.

Présentateur 1 : Adrien Lacombe (Talend)

Titre : Intégration en temps réel de flux de données avec Spark et Kafka pour recommandation de produit.

Présentateur 2 : Simon Ouellette (Faimdata)

Titre : Analyse de séries temporelles avec Spark