Bay Area Apache Spark Meetup @ Grammarly and Adobe in SF

Join us for an evening of Bay Area Apache Spark Meetup featuring tech-talks about using Apache Spark at scale from Grammarly’s Michael Chernetsov, Adobe’s Mandeep Gandhi, and Databricks’ Tim Hunter. Thanks to Grammarly for hosting and sponsoring and Adobe for

Atlanta Apache Spark Meetup: Getting Started with Structured Streaming

Structured Streaming was introduced in Spark 2.0 as a streaming component to Spark SQL’s very popular Dataframe API. Streaming problems are challenging in nature; despite interest in exploring streaming applications, many Spark users experience slow adoption caused by a steep

Boston Apache Spark User Group: April Presentation Night

A big thank you to Databricks for sponsoring this event on short notice! Agenda: * 6:00 – 6:30: Mingling + food and drink * 6:30 – 6:35: Opening Remarks * 6:40 – 7:20: Feature Talk Feature Talk Title: Evolution of an Apache

Monitoring Large-Scale Apache Spark Clusters at Databricks

At Databricks, we manage Apache Spark clusters for customers to run various production workloads. In this talk, we share our experiences in building a real-time monitoring system for thousands of Spark nodes, including the lessons we learned and the value

Bay Area Apache Spark Meetup @ Intel in Santa Clara

Join us for an evening of Bay Area Spark Meetup featuring tech-talks about using Apache Spark for Deep Learning applications from Intel’s Jiao Wang and Sergey Ermolin and Databricks’ Tathagata Das on Structured Streaming in Apache Spark 2.1. Thanks to

Structured Streaming – Spark with Databricks

Abstract: Structured Streaming – Spark with Databricks Silvio Fiorito, from Databricks, will be giving an overview of the latest Structured Streaming APIs in Apache Spark 2.1. He will focus on the key differences with the older Spark Streaming API in 1.6,

What’s changed with Apache Spark’s Structure Streaming?

The talk will compare Apache’s Spark DStream solution with the latest Structure Streaming developments. I’ll cover the technical differences between the APIs, the supported versions with external data sources, and dig into structure streaming capabilities that simplify how streaming applications

Atlanta Apache Spark User Group

R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. With the release of Spark 2.0, the R API officially supports executing user code on distributed data. This is done through a

Structured Streaming in Apache Spark in Serbia

There is a rise of Streaming solutions recently. Spark’s focus is on creating a streaming solution, which is easy to use, provides delivery guarantees and which you can control by querying an ever-growing table of incoming structured data, just like