For the past few years, big data has been all the rage, but it has not delivered on all its promise. We were expecting omniscience at our fingertips, and we’re getting moderately well-targeted advertising instead. Don’t get confused though, big data is not going anywhere. On the contrary, data keeps growing at a dizzying rate, increasing in volume, variety, velocity, and veracity.

Partnership to Accelerate Global City Network Access to Actionable Data to Address Climate Risks

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.

I am very excited to introduce Talend Data Streams, a brand new, cloud-native application that enables you to get streaming data integrations up and running in minutes all while providing unparalleled portability powered by Apache Beam.

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.

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.

Qlik introduces management team, licensing changes, and new hybrid/multi-cloud, augmented intelligence and development features. Here's my take from Qonnections 2018.

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.

5 ways to become a better data custodian under GDPR

How to best leverage your company’s most powerful asset