Fitness and Nutrition Recommendation Engine

We’ve recently been exploring the new ways we as consumers would like to be interacted with by brands as we go through our daily lives. Fitness and nutrition are key parts to our lives and being able to do those well are crucial to remaining of balanced mind and body. Active lifestyle brands have captivated us in the recent years and we think it’s time that the engagement became more personal.

They have enough information about us and we’d be willing to answer some simple questions about our fitness and nutrition goals if they asked. This simple interaction would enable them to provide us with a personalized fitness and nutrition recommendation engine.
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Jack of All Data; Master of None

In building any technology there are always trade-offs when squeezing maximum performance out of the implementation so knowing what that guiding light is for a technology becomes very important. If you’re trying to do everything, or even too many things, odds are none if it will be great because you can’t go all out for one primary objective. Your focus will be split.

This becomes especially important when evaluating the graph database landscape where you have implementations that range from

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Graph Advantage: Interest Feed

What might interest you? In this age, it’s safe to assume that you have given at least some digital declaration regarding what definitely interests you that is being used to power an interest feed. Like any good detective, we are in the business of following these clues to reveal yet undiscovered conclusions. Posts, topics, pictures, videos, people, and other assorted data that will likely interest you. Fortunately unlike detective work, digital sleuthing can be undertaken as a hard science, where algorithms and numbers are our forensic tools, culminating in an interest feed.
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Thinking in Patterns in Neo4j with Cypher

Thinking in patterns is the key to interacting with a graph database like Neo4j. One of the main challenges I see with those with deep relational database experience when transitioning to a graph database is the use of a relational approach for querying data. To query a graph database most efficiently there is a need to update the mental model for how database query interactions are approached. We’ll look at some examples of this and making this transition to thinking in patterns.Read more….

Why Connected Data is More Useful

We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.Read more….

Graph Advantage: Network and IT Operations

Network and IT operations are increasingly complex in their distribution and operation. Data complexity is a function of structure, size and connectedness. It doesn’t take an organization long to reach a point where non-graph databases just don’t keep up with constantly evolving components and topology of the network infrastructure. Network outages and failures are detrimental to any organization so being a step ahead of potential failures is a huge advantage.Read more….

When Your Data Is Not a Graph

I often get asked at the Neo4j trainings and meetups about which types of data or use cases a graph database doesn’t handle. While graph data structure models the world we live in exceptionally well there are some use cases and scenarios where your data is not a graph – or more likely not ONLY a graph.Read more….

Getting Acquainted with an Unknown Graph

Finding your way around an unknown graph can seem a bit ambiguous at first due to Neo4j being schema-free. Especially if you’re newer to graph databases and used to a relational database where you would simply open the ERD and have a look through the tables. Just because Neo4j is schema-free doesn’t mean that schema-like elements are not present. The Neo4j graph database schema elements are composed of Label Names, Relationship Types, Indexes and Constraints on Property Keys. Let’s look at some techniques for getting aquatinted with an unknown graph.Read more….

Graph Advantage: Building a Smarter Data Lake

Organizations today are amassing data at faster rate than ever before into their data lakes and often that data lake is where that data remains. Enterprises are looking for effective ways to utilize the huge volumes and varying data they’ve been collecting in their data lakes in order to respond to competitive pressures, regulations and provide empirical business guidance. It’s time to build a smarter data lake and let your data drive your organization forward.Read more….

Graph Advantage: Identity and Access Management

Identity and Access Management for a complex media workflow was actually my first experience with Neo4j as a graph database solution that enabled us to represent access throughout the flow in a structure realistic to the way it actually existed. During the last 4+ years since then I’ve continued tosee Identity and Access Management as major beneficiary of the flexible data model and performance of non-JOIN node traversal of the Neo4j graph database.Read more…

Relationship Direction in Cypher is Important

The relationship direction between two nodes is required for Cypher write queries, but can be ignored on Cypher read queries. Cypher is a graph database declarative and textual query language by Neo4j, which supported by a larger group as OpenCypher to make Cypher the SQL for graph databases. Cypher looks a bit like ASCII art in it’s representation of graph-related traversal patterns, which makes it quite intuitive and fun to use in querying graphs.Read more…

systemd Neo4j service on Ubuntu

systemd ships as the default process manager on Ubuntu 15.04; get the neo4j systemd service file to install and learn about the configuration. systemd has a lot of great features and I encourage you to check it out as it’s a big improvement IMO from SystemV and Upstart. If you’ve started workingwith systemd to manage your neo4j process, then youRead more….