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Google Cloud Platform begins filling out its data stack

ZDNet

While Google Cloud Platform (GCP) paraded out a series of executive endorsements at its NEXT conference last spring, a comment from one of the participants shed valuable light on GCP's appeal. Coke's CTO pointed to GCP's advanced "NoOps" that enabled it to - literally - stitch together the Happiness Flag quilt from over 3 million photos contributed worldwide. The quit was the star of Coke's digital marketing campaign for the 2014 World Cup. But the abilioty to rapidly mobilize viral content is not necessarily among the qualities that CIOs or CTOs would associate with more mundane jobs of running, say, SAP or Oracle. That's the narrative that Google wants to start changing with its rollout of a trio of enterprise data platforms today.


Google's new cloud service eases data preparation for machine learning

PCWorld

One of the challenges that data scientists face when running machine learning workloads is processing information before it's ready for use. Google unveiled a new cloud service Thursday aimed at easing that pain. Google Cloud Dataprep will automatically detect data schemas, joins, and anomalies such as missing or duplicate values, without requiring coding. After that, it will help users build a set of rules for processing the information. Those rules are then built in Apache Streams format and can be imported into products like Google's Cloud Dataflow for processing information as it's imported into services like the BigQuery data warehouse service.


Google Cloud: Big Data, IoT and AI Offerings - Datamation

#artificialintelligence

Continuing Datamation's series on big data, Internet of Things (IoT) and artificial intelligence offerings from major cloud providers, it's time to switch gears from Microsoft Azure to Google Cloud Platform. And given the vast amounts of data that powers the search giant's services, it's only fitting to start with big data and analytics.


Ten top noSQL Databases

@machinelearnbot

Oracle NoSQL - Oracle's relational database system was pioneering and is still widely used for many purposes in businesses of all sizes. Oracle applied its pragmatic business sensibilities when entering the market created by newer upstart competitors. The focus was on reliability and scalability, in order to persuade giants of industry and finance to put their trust in emerging Big Data technologies. DynamoDB (Amazon Web Services) - Amazon provides DynamoDB as part of its commercial Amazon Web Services package. Built specifically to handle fast, constantly growing volumes of data in all shapes and sizes, it is provided as a "managed" service and has proven particularly popular with business users needing access to reliable cloud-enabled infrastructure, real-time analytics and scalability.


Baseline: Data, ML, AI Qwiklabs google-run

#artificialintelligence

This Quest of all-Introductory-level Self-Paced Labs is intended for the complete beginner in Google Cloud. In these short hands-on labs, you will get your first-touch experiences with a subset of the Google Cloud services that provide tools for working with big (and small!) data and machine learning / artificial intelligence services in Google Cloud.