Systems | Development | Analytics | API | Testing

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Firebase & BigQuery - Do Mobile App Analytics Easily & at Scale - Queries Included (Cloud Next '18)

In this session, we'll explain how your analytics data is stored in BigQuery and then show you some important tips on working with this data as we create queries to answer your burning mobile analytics questions.

Data and Analytics Platform Overview and Customer Examples (Cloud Next '18)

Using examples from the finance and retail industries we will walk through the core products in GCP data platform. The session will cover BigData analytics services such as: BigQuery, Dataflow, Pub/Sub, Dataproc, Dataprep, Datalab, and Datastudio.

BigQuery Nested and Repeated Fields: Dig Deeper into Data (Cloud Next '18)

Are you ready to take your knowledge of SQL to its final frontiers? Join this session to learn how you can use BigQuery and its SQL 2011 compliant features to tap deep into insights locked away in your spreadsheets, JSON files, and other semi-structured data formats.

Building the World's Largest Enterprise Data Warehouse with BigQuery (Cloud Next '18)

This talk, by one of the founders of the BigQuery team and a founder and current CTO of Looker, will make the case that BigQuery is not just another Enterprise Data Warehouse. It will show how BigQuery's unique properties follow from its technical architecture.

Diving into Your Billing Data with BigQuery and DataStudio (Cloud Next '18)

Many large organizations build custom dashboards and reporting around their cloud usage to track that usage across teams and applications, and to understand cost drivers. In this session, the Billing team and Vendasta will show you how to export your detailed billing data to BigQuery, write useful queries around that data, and create custom dashboards based on those queries in Data Studio.

How BuzzFeed Built A Great Data Experience Using BigQuery and Looker (Cloud Next '18)

This talk will focus on how BigQuery and Looker allowed a small data team at Buzzfeed to manage the data needs of 800 users, and how migrating from AWS Redshift to BigQuery gave us the performance we needed to allow interactive querying of large datasets by non-technical people.