Systems | Development | Analytics | API | Testing

A performance study on real-time data Integration from Oracle to Google BigQuery Using Striim.

Sibros’ Connected Vehicle Platform on Google Cloud delivers OTA data updates, collection, and commands with the flexibility and scale of the cloud

BigQuery ML reduces data to AI barrier by making it easy to manage the end-to-end lifecycle from exploration to operationalizing ML models using SQL.

Today, we’re hearing from telematics solutions company Geotab about how Google BigQuery enables them to democratize data across their entire organization and reduce the complexity of their data pipelines.

Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered without the limitations of running virtual servers.

Most commonly, data teams have worked with structured data. Unstructured data, which includes images, documents, and videos, will account for up to 80 percent of data by 2025. However, organizations currently use only a small percentage of this data to derive useful insights. One of main ways to extract value from unstructured data is by applying ML to the data.

If you’ve already centralized your log analysis on BigQuery as your single pane of glass for logs & events…congratulations! With the introduction of Log Analytics (Public Preview), something great is now even better. It leverages BigQuery while also reducing your costs and accelerating your time to value with respect to exporting and analyzing your Google Cloud logs in BigQuery.