Introducing the AWS Cloud Map Multicluster Service Controller for K8s for Kubernetes Multicluster Service Discovery

Modern applications built using microservices patterns are distributed and dynamic by nature. Deploying these applications to Kubernetes clusters tightly couples the application and cluster together. Increasingly, customers are asking for the ability to deploy applications across clusters to allow for easier upgrades and migrations and to break down isolation boundaries. However, bridging the gap between clusters requires platform and operations...
Shay Sofer: about #wix ci pipeline on @buildkite

Shay Sofer: about #wix ci pipeline on @buildkite wix.engineering/post/6-challen…- Arnon Rotem-Gal-Oz (@arnonrgo)10:31 - Jan 26, 2022
Kubernetes instance calculator Supports aws, gcp and azure

Kubernetes instance calculator Supports aws, gcp and azure learnk8s.io/kubernetes-ins…- Arnon Rotem-Gal-Oz (@arnonrgo)10:09 - Jan 26, 2022
RT @thenewstack: Top 5 Reasons for Moving from Batch to Real-Time Analytics @iamveeve @RocksetCloud #Sponsored #Ana…

Arnon Rotem-Gal-Oz (@arnonrgo) retweeted:Top 5 Reasons for Moving from Batch to Real-Time Analytics bit.ly/3fZyhgn @iamveeve @RocksetCloud #Sponsored #Analytics #data #RealTime- The New Stack (@thenewstack)08:11 - Jan 26, 2022
Building Data Applications on the Lakehouse With the Databricks SQL Connector for Python

We are excited to announce General Availability of the Databricks SQL Connector for Python. This follows the recent General Availability of Databricks SQL on Amazon Web Services and Azure. Python developers can now build data applications on the lakehouse, benefiting from record-setting performance for analytics on all their data. The native Python connector offers simple installation and a Python DB API...
The Paper: “A Deep Probabilistic Model for Customer Lifetime Value Prediction”

A run through the paper’s neural network architecture and loss functionContentsAboutPaper OverviewDeep Dive: Architecture - Output LayerDeep Dive: ZILN LossSummaryAboutPredicting a customer’s lifetime value (LTV) can be quite a challenging task. Wang, Liu and Miao propose using a neural network with a mixture loss to handle the intricacies of churn and lifetime value modelling of new customers.In this blogpost we’ll take a look...

Category: Big Data

Intro to Apache Spark (slides)

Published by Arnon Rotem-Gal-Oz on December 16, 2020

I gave a general overview of Apache Spark to our R&D teams. You can find the slides below

Where is Apache Spark heading?

Where is Apache Spark heading?

Published by Arnon Rotem-Gal-Oz on December 4, 2020

I watched (COVID19-era version of “attended”) the latest spark Summit and in one of the keynotes Reynold Xin from Databricks, presented the following two images…

Big data isn’t – well, almost

Big data isn’t – well, almost

Published by Arnon Rotem-Gal-Oz on March 23, 2019

Back in ancient history (2004) Google’s Jeff Dean & Sanjay Ghemawat presented their innovative idea for dealing with huge data sets – a novel idea…

Big data in the cloud – welcome to cost oriented design

Big data in the cloud – welcome to cost oriented design

Published by Arnon Rotem-Gal-Oz on April 1, 2016

A couple of weeks ago I presented @ BDX2016 The slides are available on Slideshare Big data in the cloud – welcome to cost oriented…

Spark, Parquet and S3 – It’s complicated.

Spark, Parquet and S3 – It’s complicated.

Published by Arnon Rotem-Gal-Oz on August 10, 2015

(A version of this post was originally posted in AppsFlyer’s blog. Also special thanks to Morri Feldman and Michael Spector from AppsFlyer data team that…