Google Cloud Big Data and Machine Learning Blog

Innovation in data processing and machine learning technology

Google Cloud Dataprep is now a public beta

Thursday, September 21, 2017

Announcing public beta of Cloud Dataprep, offering a visual experience that makes data preparation intuitive and approachable.

Classifying text content with the Natural Language API

Tuesday, September 19, 2017

With the Natural Language API, you can now tag media content such as blogposts, news articles and marketing copy with a single API call.

New Google Data Studio features give analysts quicker and broader access to data

Friday, September 15, 2017

Over the past six months we launched over 25 new features in Google Data Studio, now we're adding new features so you can access your data even faster.

Performing prediction with TensorFlow object detection models on Google Cloud Machine Learning Engine

Monday, September 11, 2017

Learn how to export a trained object detection model into the SaveModel format then how to deploy to Cloud Machine Learning Engine and perform prediction.

How to crunch your business data from Sheets in BigQuery

Thursday, September 7, 2017

Google BigQuery can query Google Sheets just like any other table, allowing new collaborations and quicker insights.

The canonical new book about stream processing

Monday, August 28, 2017

We speak with Tyler Akidau, one of the authors of the O’Reilly Media book Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing

From data to insights: Preview a new Google Cloud training course for SQL analytics

Thursday, August 24, 2017

Get a preview of our new course From Data to Insights with Google Cloud Platform, which includes hands-on exercises using BigQuery and Google Data Studio.

Guide to using Google BigQuery on Microsoft PowerShell

Wednesday, August 23, 2017

Learn how to use PowerShell cmdlets that script to BigQuery commands straight from the PowerShell environment.

Analyzing errors in Cloud Dataflow with Stackdriver Error Reporting

Tuesday, August 22, 2017

Learn how you can use Stackdriver Error Reporting to monitor and debug your Cloud Dataflow jobs

Guide to common Cloud Dataflow use-case patterns, Part 2

Monday, August 21, 2017

Part 2 in our open-ended series that documents the most common patterns we've seen across production Cloud Dataflow deployments.

How Aucnet leveraged TensorFlow to transform their IT engineers into machine learning engineers

Thursday, August 17, 2017

Learn how Aucnet uses deep learning to build a real-time car image recognition system powered by Tensorflow.

Easier integration with Apache Spark and Hadoop via Google Cloud Dataproc Job IDs and Labels

Tuesday, August 15, 2017

Learn best practices for using Google Cloud Dataproc Job IDs and Labels to integrate your apps with Apache Spark and Hadoop.

Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization

Thursday, August 10, 2017

Learn about HyperTune, hyperparameter tuning as a service, in Cloud Machine Learning Engine.

When art meets big data: Analyzing 200,000 items from The Met collection in BigQuery

Monday, August 7, 2017

This new public dataset allows you to build a custom machine-learning model, create an app for sorting and visualizing the images, and more.

Traveloka’s journey to stream analytics on Google Cloud Platform

Thursday, August 3, 2017

Travel technology company Traveloka talks about migrating its streaming data processing pipeline to a multi-cloud solution including GCP data analytics.

How WePay uses stream analytics for real-time fraud detection using GCP and Apache Kafka

Tuesday, August 1, 2017

Learn how WePay built a new stream analytics pipeline for real-time fraud detection using Apache Kafka and Google Cloud Platform.

Life of a Cloud Dataflow service-based shuffle

Monday, July 31, 2017

Learn the practical impact of Google Cloud Dataflow's Shuffle on data pipelines using the Opinion Analysis project as an example.

Running external libraries with Cloud Dataflow for grid-computing workloads

Friday, July 28, 2017

Learn how Cloud Dataflow used in conjunction with other GCP services can unlock parallel workloads.

Cloud Dataproc is now even faster and easier to use for running Apache Spark and Apache Hadoop

Wednesday, July 26, 2017

Learn about Cloud Dataproc 1.2, which includes software component updates, environment configuration changes and YARN changes.

New hands-on labs for scientific data processing on Google Cloud Platform

Monday, July 24, 2017

Try out 7 labs which can teach scientists how to use Google Cloud products and services to support their professional goals.

Google Cloud Platform for Data Scientists: Using R with Google BigQuery, Part 2 (storing and retrieving data frames)

Thursday, July 20, 2017

Learn how to create an R data frame and stash it in BigQuery using bigrquery.

Moving Thumbtack’s data infrastructure to Google Cloud Platform

Tuesday, July 18, 2017

Learn how Thumbtack ramped up GCP usage from a few BigQuery tables to include all of its data infrastructure, a move resulting in big productivity gains.

How to aggregate data for BigQuery using Apache Airflow

Tuesday, July 11, 2017

Users of Google BigQuery, the cloud-native data warehouse service from GCP, have access to an ever-expanding range of public datasets for exploration.

After Lambda: Exactly-once processing in Cloud Dataflow, Part 3 (sources and sinks)

Thursday, July 6, 2017

The series concludes with a description of how exactly-once processing in Cloud Dataflow is supported by sources and sinks.

Counting uniques faster in BigQuery with HyperLogLog++

Wednesday, July 5, 2017

Learn how BigQuery uses HyperLogLog++, Google’s internal implementation of the HyperLogLog algorithm for cardinality estimation.

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