BigQuery is Google's serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator.
Analyze all your data by creating a logical data warehouse over managed, columnar storage, as well as data from object storage and spreadsheets. Build and operationalize machine learning solutions with simple SQL. Easily and securely share insights within your organization and beyond as datasets, queries, spreadsheets, and reports. BigQuery allows organizations to capture and analyze data in real time using its powerful streaming ingestion capability so that your insights are always current, and it’s free for up to 1 TB of data analyzed each month and 10 GB of data stored.
In this article, I’ll walk you through the process of building a machine learning model using BigQuery ML. As a bonus, we’ll have the chance to use BigQuery’s support for spatial functions.
In this month’s installment of What’s Happening in BigQuery, we’re sharing new features intended to make your life easier: some make BigQuery more performant and more cost effective, while others, like BigQuery ML, enable groundbreaking analysis tools in a cloud data warehouse that’s a first of its kind.
Today, we want to share a number of updates that will make data analytics easier and more accessible to all businesses.
This is the first installment in a monthly review of recently-released BigQuery features.
In today’s blog post, we will give a light introduction to working with Neo4j’s query language, Cypher, as well as demonstrate how to get started with Neo4j on Google Cloud.
Join Developer Advocate Felipe Hoffa and the the CTO of Nomanini - Dale Humby - to learn more about how they use App Engine, Kubernetes, BigQuery and other GCP tools.
This lab explores some of the bigger concepts and smaller work required to migrate data from an existing (on-premises) data warehouse solution to GCP.
In this video, Lak Lakshmanan, Tech Lead, Professional Services, shows you a new feature that makes analyzing and visualizing geospatial data in BigQuery easy and highly performant. BigQuery GIS provides a convenient, powerful way to incorporate spatial information in your decision making.
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.
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.