“Lateral thinking” was a big topic back in 2004 when I was in the Network Operations Center (NOC) business; one definition is:
This is the second of a two post series on monitoring the Neo4j graph database with popular enterprise solutions such as Prometheus and Grafana. Monitoring the status and performance of connected data processes is a crucial aspect of deploying graph based applications. In Part 1 we have seen how to expose the graph database internals and custom metrics to Prometheus, where they are stored as multi-dimensional time series.
Database Monitoring is a crucial aspect of any application deployment. After all, databases manage data and sit quite down in the stack. They are robust pieces of software, but their setup and maintenance need care and attention since any problem has the potential to be disruptive to business.
There is one common performance issue our clients run into when trying their first Cypher queries on a dataset in Neo4j. When writing a query, be sure that it doesn’t match any cycles, or you can experience unpleasant surprises.
When developing web applications with frameworks like Vue.js the best approach is to subdivide it into well-defined and reusable components for the user interface, with the business logic being encapsulated in ‘services’.
Dependencies, like graphs, are everywhere. Achieving a goal is rarely possible in a vacuum and requires collaboration between individuals and/or processes.Eliminating dependencies completely is unrealistic- they are a part of life- but they can be streamlined to improve efficiency and reduce friction.
Few years ago I decided that one day I would create a Graph Technology Landscape map, which would be useful for everyone who wants to discover the players around graph technologies. I started to collect the companies and products, but my research has never manifested into a proper blog post. Till now. I am happy to announce, that the first version of my landscape is published, I hope we can consider this as a start of a long journey.
In social network analysis, a conventional approach relies heavily on available metadata, allowing to match a virtual entity (social network account) to a real-world entity (person, company) in the network. However, a single person using multiple accounts for any reason obviously breaks the connection, forming multiple virtual entities in the network. Or multiple people can share their account, forming a single virtual entity in the network. If these cases are not taken into account, they can affect reliability of social network analysis significantly without any warning, possibly leading to misinformed decisions and further bad consequences.
In this blog we will go over the Full Text Search capabilities available in the latest major release of Neo4j.
2018- it’s been such a whirlwind of activity at GraphAware, and we’re so proud of everything we’ve accomplished this year.In fact, we grew and grew, announcing ourselves in Australia and then, later in the year, expanding into the Americas.“Neo4j is one of the most disruptive and transformative technologies I have seen in my career,” said Kyle McNamara, CEO, Americas. His team are well on their way to increasing GraphAware’s presence and strengthening the already close bond we have with Neo4j.