Today, we want to share a number of updates that will make data analytics easier and more accessible to all businesses. Our goal is to help you focus on data analysis instead of infrastructure management, give you the freedom to orchestrate workloads across clouds, use machine-learning in a way that's integrated with your data analytics operations, and take advantage of open source data processing innovation.
This is the first installment in a monthly review of recently-released BigQuery features. While our rather active release notes do contain concise but actionable information, we’ve heard from some of our users that they’d love a little more information on these updates and what they mean in a bigger picture. This month, we present a number of practical new features, primarily focused on data types and data file formats.
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. You will learn how to quickly turn your Google BigQuery data or your Google Cloud logs into a graph data model, which you can use to reveal insights by connecting data points.
BigQuery is a managed analytics service that provides advanced cloud data warehouse capabilities with a diverse set of features. One of BigQuery’s most significant differentiators is its distributed analytics engine, which transforms your SQL queries into complex execution plans, dispatching them onto our execution nodes to promptly provide insights into your data.
