New Google Cloud Data Analytics products and services help organizations solve business problems with data, rather than spending time and resources building, integrating and managing the underlying infrastructure:
- BigQuery Data Transfer Service (Private Beta) – BigQuery Data Transfer Service makes it easy for users to quickly get value from all their Google-managed advertising datasets. With just a few clicks, marketing analysts can schedule data imports from Google Adwords, DoubleClick Campaign Manager, DoubleClick for Publishers and YouTube Content and Channel Owner reports. These help with more targeted Google Cloud Data Analytics.
- Cloud Dataprep (Private Beta) – Cloud Dataprep is a new managed data service, built in collaboration with Trifacta, that makes it faster and easier for BigQuery end-users to visually explore and prepare data for analysis without the need for dedicated data engineer resources.
- New Commercial Datasets – Businesses often look for datasets (public or commercial) outside their organizational boundaries. Commercial datasets offered include financial market data from Xignite, residential real-estate valuations (historical and projected) from HouseCanary, predictions for when a house will go on sale from Remine, historical weather data from AccuWeather, and news archives from Dow Jones, all immediately ready for use in BigQuery (with more to come as new partners join the program).
- Python for Google Cloud Dataflow in GA – Cloud Dataflow is a fully managed data processing service supporting both batch and stream execution of pipelines. Until recently, these benefits have been available solely to Java developers. Now there’s a Python SDK for Cloud Dataflow in GA.
- Stackdriver Monitoring for Cloud Dataflow (Beta) – We’ve integrated Cloud Dataflow with Stackdriver Monitoring so that you can access and analyze Cloud Dataflow job metrics and create alerts for specific Dataflow job conditions.
- Google Cloud Datalab in GA – This interactive data science workflow tool makes it easy to do iterative model and data analysis in a Jupyter notebook-based environment using standard SQL, Python and shell commands.
- Cloud Dataproc updates – Our fully managed service for running Apache Spark, Flink and Hadoop pipelines has new support for restarting failed jobs (including automatic restart as needed) in beta, the ability to create single-node clusters for lightweight sandbox development, in beta, GPU support, and the cloud labels feature, for more flexibility managing your Dataproc resources, is now GA.