Neo4j is for the Non-Technical

Neo4j unifies organizations across departments and across teams, both technical and non-technical, enabling a greater level of understanding and clarity in communication than previously possible. A Neo4j graph model is whiteboard friendly and allows everyone from business to engineering groups

to speak the same language of connections. Communicating in contextually relevant connections that bring together business concepts reduces the potential for misunderstandings that cause delays and rework later.

Neo4j Connects Your Organization by Connecting Your Data

The world today is highly connected. Graph databases are whiteboard friendly and effective in mimicking erratic and inconsistent relationships through intuitive means. They help provide insights and understanding by creating connections within complex big data sets. As enterprises become increasingly data driven it is essential that all individuals, especially the non-technical groups have the ability to collaborate with engineering in a more integrated fashion. Neo4j removes the intimidation factor of technology typically required to

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Graph Advantage: Moving Beyond Big Data

Enterprises today are amassing data at a faster rate than ever before and largely this data flows into a data warehouse or data lake or just individual databases where it sits. With enterprises struggling to leverage it in a holistic and meaningful way for their business, the appeal of “big data” is waning.

So how do enterprises begin moving beyond big data?

Data Driven and Connectedness

In the last years we’ve seen enterprises acting on the acknowledgement that organizations need to be more data driven, but there is still a gap of how to really do that well. In the years ahead we’ll see an increasing push by organizations implementing new technologies promising to get them there. It’s unfortunate, but I fear many organizations will be left disappointed. Disappointed not by the technology getting in place successfully, but by the lack of real business value derived from it.

Many technical architects and business people alike have been captivated by the size and speed of data. However, when it comes to knowledge and understanding these are not the most important parameters. Technologies that are scale first place the focus in the wrong area for solving knowledge and understanding problems. What do you gain by writing 1 billion rows per day into redshift if you don’t have that data connected in a meaningful way to rest of your organization? (Now there is definitely a time and place for just getting data persisted, but that’s a very different scenario than a BI/Recommendation/Analytics

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Neo4j 3.0 Welcomes a New Era for Graphs

At GraphConnect at the end of April the Neo4j team announced the release of Neo4j 3.0. We had the opportunity to celebrate this release at The Honest Company last night with the Graph Database LA Meetup group where I shared many of these highlights from the official Neo4j announcement. The

first release in the 3.x series ushers in a new era of scalable yet reliable graph database technology with, this version of Neo4j based on a completely redesigned architecture that offers enhanced developer productivity, and varying deployment options at a massive scale.

3 Things to Expect in Neo4j 3.0

Here’s what to be expected with the new Neo4j 3.0:

• Redesigned internals that eradicates limits on node numbers and restoration of indexed and stored properties and relationships.
• Official support for language drivers via Bolt binary protocol and Java Stored Procedures support, while enabling full-stack developers for powerful application creation.
• Streamlined deployment structure and configuration for deploying Neo4j in the cloud or

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Easier Data Migration with Neo4j

Data migration is one of the necessary evils involved with keeping a database aligned with the evolving needs of the business and applications using it. With the increasing demand for enterprises of all sizes to iterate more quickly and drive change from within the data migration conversation becomes much more frequent. Data migration procedures are something that can take a very long

time or not even be feasible depending on the size and structure of the data in a database.

Data migration is not just an enterprise issue. Startups are changing at even more rapid rate while they iterate on their product(s) and business model(s) trying to figure out exactly what they need to be. Being able to perform rapid, low risk data migrations with minimal impact to existing applications using the database is one great benefit of Neo4j with it’s flexible schema-free data model.

Neo4j Graph Database Data Migration

As a native graph database Neo4j provides several advantages when it comes to managing data migrations:

• Neo4j treats relationships as primary entities within the database which means you can add a new relationship to connect certain nodes in a new way without needing to migrate a table schema enforcing a new foreign key along with inserting all the corresponding references into each row in the table or building a JOIN-table.
• Neo4j uses labels to index common nodes. A label is like a tag and node can have any number labels. Labels are useful in a data migration because while they associate nodes together under a certain type they don’t bring with them, by default, a schema definition containing properties, data types and the like that must be adhered to by any node given that label. This means you can temporarily group a set of 

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Graph Advantage: Research Organizations

Many enterprises today build their business around research that involves piecing together meaningful data from the public domain for their customers. When trying to connect data across a domain in a meaningful way building around a graph database is a great tool because it models very

well exactly how the business analysts at these research organizations are piecing together the real-world data they are finding during their research.

A business analyst may begin with one person and from there, move to the company they’re working for and then shifting to colleagues before moving on to places where their current colleagues previously worked, before finally settling for their past colleagues. Suddenly the business analysts has nearly finished building out an intuitive network of complex connections around this person of interest which would have been challenging and time consuming to try to represent in Excel.

Graph Database in Research Organizations

Research organizations are more than just managing large data volumes, their core goal is finding understanding that comes through research to gain insight of the available data. To properly leverage data relationships, a research organization requires a database technology that houses data relationship as a 

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Neo4j is Designed to be Your Source of Truth Database

When introducing the idea of using Neo4j within an enterprise one common assumption is that because Neo4j is a graph database it must not provide ACID-compliance that RDBMS has delivered for so long. This assumption isn’t unfounded considering most of the NoSQL database solutions have moved towards a performance and availability at all costs model. But it’s a fact: Neo4j is a fully

ACID-compliant and transactional database intended to be a secure and safe source of truth database for your enterprise.

Neo4j is a reliable, scalable and high-performing native graph database that’s suitable for enterprise use. Its proper ACID characteristics is a foundation of data reliability. Neo4j ensures that operations involving the modification of data happen within a transaction to guarantee consistent data.

This is especially important in graph because the paradigm for writing data reliably shifts when you introduce the concept of a relationship that is a primary entity within the database. To write a relationship reliably requires locking both the nodes it’s connected to in order to guarantee that they both agree on that relationship between them.

What is ACID?

For those that may not know or need a refresher as to what that acronym includes, heres a quick

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Graph Advantage: Connected Enterprise

The connected enterprise is the new norm. Traditional chain paradigm with sequential and siloed operations lacking a connection between customer and factory is no longer cutting it. Today enterprises are excepted to be sufficiently in touch and aware of how to interact with each uniquely individual person they are fortunate to call their customer. Technologies and operational procedures

are rapidly changing to enable information to be connected and taken together to drive decision making, direction, and interaction with the customer.

Connected Enterprise: Data Essentials

Connected data is the lifeblood of today’s enterprise. Yet, it’s frequently isolated in varying silos across an organization, with different accessibility, redundancy, quality, and varying data formats. Managing connected data involves identifying, cleaning, storing, and governing increased data volumes within an enterprise. Connected data involves essential information such as customers, users, products, services, sites, and business units.

Adequate practices for connected data management differ along a wide range of approaches. On one end, many believe that connected data should be united in one location; while on the other end, some recommend managing data assets from one application or service, even if information is

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