Category: Big Data

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Thursday, June 9, 2016 Posted by James Malone, Product Manager If you like to stay on the cutting edge of the Apache Spark and Apache Hadoop ecosystem, we have some good news — you can test new versions of these tools in a fast, easy and cost-effective way. Google Cloud Dataproc, Google Cloud Platform’s managed Spark and Hadoop service, has a preview image version which often includes preview releases of popular Spark and ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Wednesday, June 8, 2016 Posted by Robert Burke, Software Engineer Editor's update June 8, 2016: The roll-out of this feature is currently in progress. You will be able to see this functionality in the Google Developers Console as the roll-out progress over the next several days. When running a program on a single machine, you want it to spend time as efficiently as possible. Running a Cloud Dataflow pipeline should be no different ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Thursday, June 2, 2016 Posted by Dan Delorey, Technical Lead and Bosco Zubiaga, Technical Product Marketing Manager, Google Cloud Platform BigQuery is Google Cloud Platform’s serverless analytics data warehouse. It's used by thousands of companies — both big and small — to store, understand, and analyze large amounts of data. Today, we’re announcing a host of new features that make BigQuery more compatible with traditional big data workflows: Standard SQL Beta If you're ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Friday, May 27, 2016 Posted by Kaz Sato, Staff Developer Advocate, Google Cloud Platform From: Irasutoya.com If you've ever learned to program, you’ve probably written a Fizz Buzz test. With Fizz Buzz, you print the numbers from 1 to 100, except if it is divisible by 3, you print "fizz"; if it's divisible by 5, you print "buzz"; and if it's divisible by 15 you print "fizzbuzz." This trivial coding problem is typically ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Tuesday, May 24, 2016 Posted by Kaz Sato, Staff Developer Advocate, Google and Ray Sakai, Product Manager, Reactive Inc. At GCP NEXT 2016, the biggest Google Cloud Platform event held this year in San Francisco, Jeff Dean, Google Senior Fellow, presented the Cloud Vision API with Cloud Vision Explorer. This amazing demo is now available for anyone and we warmly invite you to give it a try. To recap, Cloud Vision API is ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Monday, May 23, 2016 Posted by Lak Lakshmanan, Big Data & Machine Learning Professional Services, Google Cloud Platform Demand forecasting is something that every business does. If you're a restaurant owner, you need to forecast how many diners you'll have tomorrow and what foods they'll order so that you know what ingredients to shop for and how many cooks to have in your kitchen. If you sell shirts, you need to predict in ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Wednesday, May 18, 2016 Posted by Eugene Kirpichov, Senior Software Engineer and Malo Denielou, Software Engineer Introduction Today we continue the discussion of Google Cloud Dataflow's “zero-knobs” story. Previously we showcased Cloud Dataflow's capability for Autoscaling, which dynamically adjusts the number of workers to the needs of your pipeline. In this post, we discuss Dynamic Work Rebalancing (known internally at Google as Liquid Sharding), which keeps the workers busy. We'll show how this ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Tuesday, May 17, 2016 Posted by Felipe Hoffa, Developer Advocate, Google Cloud Platform I'm a big fan of Mark Litwintschik's latest series of blog posts. He's been measuring every big data platform he can find using the billion taxi trips made available as open data by the NYC TLC. Spoiler alert: I love the stellar results for Google BigQuery and Google Cloud Dataproc — but before we get there, here’s my visualization of ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Friday, May 6, 2016 Posted by Tino Tereshko, BigQuery Technical Program Manager Google BigQuery is a serverless fully-managed analytics data warehouse that frequently delivers features and upgrades without any downtime or burden on the user. For enterprise customers, the BigQuery team strives to deliver features that improve user productivity and interoperability, and make BigQuery even easier to use. Today, the BigQuery team is announcing integration with Google Drive. You can now: Save query ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Tuesday, May 3, 2016 - Posted by Tyler Akidau, Staff Software Engineer & Apache Beam PPMC When we made the decision (in partnership with data Artisans, Cloudera, Talend, and a few other companies) to move the Google Cloud Dataflow SDK and runners into the Apache Beam incubator project, we did so with the following goal in mind: provide the world with an easy-to-use, but powerful model for data-parallel processing, both streaming and batch, ... Read More