Dataflow SQL user-defined functions | Google Cloud
Send feedback Dataflow SQL user-defined functions Stay organized with collections Save and categorize content based on your preferences. Dataflow SQL supports user-defined functions (UDFs) written in Java or SQL. These functions accept columns of input and perform actions, returning the result of those actions as a value. Java UDFs Java UDFs can be either scalar functions or aggregate functions. A
Preprocessing for Machine Learning with tf.Transform
Posted by Kester Tong, David Soergel, and Gus Katsiapis, Software Engineers When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks. This preprocessing takes a variety of forms, from converting between formats, to tokenizing and stemming text and forming vocabularies,
Spotify’s Event Delivery – The Road to the Cloud (Part III) - Spotify Engineering
Whenever a user performs an action in the Spotify client—such as listening to a song or searching for an artist—a small piece of information, an event, is sent to our servers. Event delivery, the process of making sure that all events gets transported safely from clients all over the world to our central processing system, is an interesting problem. In this series of blog posts, we are going to lo
Programming model for Apache Beam | Cloud Dataflow | Google Cloud
Send feedback Programming model for Apache Beam Stay organized with collections Save and categorize content based on your preferences. Dataflow is based on the open-source Apache Beam project. This document describes the Apache Beam programming model. Overview Apache Beam is an open source, unified model for defining both batch and streaming pipelines. The Apache Beam programming model simplifies
The world beyond batch: Streaming 101 - O'Reilly Radar
The world beyond batch: Streaming 101 A high-level tour of modern data-processing concepts. Editor’s note: This is the first post in a two-part series about the evolution of data processing, with a focus on streaming systems, unbounded data sets, and the future of big data. See part two. Streaming data processing is a big deal in big data these days, and for good reasons. Amongst them: Businesses
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Big data and analytics resources | Cloud Architecture Center | Google Cloud
GitHub - GoogleCloudPlatform/DataflowSDK-examples: Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines. This repository hosts a few example pipelines to get you started with Data
Overview: Dataflow SQL reference | Google Cloud
How WePay uses stream analytics for real-time fraud detection using GCP and Apache Kafka | Google Cloud Blog
Data Analytics | Google Cloud Blog