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Transform publicly available BigQuery data and Stackdriver logs into graph databases with Neo4j

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 at speed: new features help you tune your query execution for performance

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.

The Rise of Ad Hoc and Citizen Integrators

In the past few years, there has been a shift in the data industry, leading to the emergence of a new category of data citizens: the ‘ad hoc’ or ‘citizen’ integrators. With these new personas adding to the (already long) list of data workers having access to corporate information, companies are needing to re-think the way they approach their data security and data governance strategies.

Making data-intensive processing efficient and portable with Apache Beam

The appearance of Hadoop and its related ecosystem was like a Cambrian explosion of open source tools and frameworks to process big amounts of data. But companies who invested early in big data found some challenges. For example, they needed engineers with expert knowledge not only on distributed systems and data processing but also on Java and the related JVM-based languages and tools.