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 deal with complex data and enables more unified collaboration because we all can relate to connections.

There a number of reasons why both technical and non-technical teams within an organization could all agree on Neo4j:

• It offers incredible performance
  The more connected data gets in typical RDBMS and NoSQL databases, the faster performance query degrades. It’s fact that data within all organizations is growing rapidly in size and connectedness. Neo4j provides constant time navigation through your connected data whether your one level deep or ten levels deep.
• It guarantees data reliability
  Neo4j is fully ACID-compliant and transactional with a guarantee of referential integrity, which means that once your data is written it cannot be lost and two nodes will never disagree on the relationship between them.
• It has tremendous flexibility
  With a Neo4j graph database, non-technical business personnel, IT, and data architect staff are united and move at a speed of business due to the schema and structure of a graph model that “flexes” as business solutions and marketing industry changes. Your team won’t have to exhaust themselves modeling your domain in advance. Instead, they can make additions to the current structure without 

Read More......

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 conversation about driving business understanding and decision making). Ideally you’d be doing both: getting your data persisted and connected at the same time. Too many enterprises are simply collecting and hoarding data at this point.

Being data driven with a concrete understanding of your organization is completely dependent on connectedness. It’s all about what things are connected to and understanding the ways they’re connected. This is the essential foundation of any business intelligence, cognitive, or predictive analytics. Without understanding at the core there is no movement beyond just having vast amounts of “big data”.

Getting Connected with Neo4j

Neo4j provides and advantage in managing data due to the ease in which Neo4j can be brought into your data architecture and the short period of time needed to start seeing benefits of connecting data from your big data deluge. A flexible graph data model that is very representative of the real-world is what Neo4j has been designed from the ground up as a native graph database to read and write.

Neo4j is an ACID-compliant, transactional native graph database that guarantees reliability of the data written while providing horizontal read scalability through distributed high availability clustering. A major benefit to Neo4j being a native graph database is the ability to perform constant time traversals through your connected data. No more JOIN pain. This aspect of being native is important for a graph database because the paradigm for reading and writing data in a graph database is different from any other database type. Be wary of non-native “graph databases” because underneath that graph layer is a datastore that hasn’t been designed to deal with a highly connected graph data model.

Often times, we determine meaningful relationships between information items in advance and structuring our analytics and queries on forward-based predictions to decipher the meaning of our world. But with Neo4j, it encourages us to see the world as a connected data set whose links are made in a dynamic matter and explored in an ad-hoc manner over time.

Moving Beyond Big Data with Neo4j

The first step in moving beyond big data is connecting that data in a meaningful way. As a software and 

Read More......

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 on premise.

Diving Deeper into Neo4j 3.0

Here’s an in-depth look of what’s new in the latest version:

• Unlimited Graph Storage
  By far the biggest headline in the release. Graph to size infinite – challenge accepted! Dynamic pointer compressions expands the available address of Neo4j as needed, making it possible to house graphs regardless of size. Such features can be seen in the Neo4j Enterprise Edition, which complements its scale-out clustering features.
• Enhanced Cost-based Optimizer
  This is a huge one for us because most of the Cypher we write are complex MERGE operations so we need as much write performance as possible. A cost-based optimizer has been enhanced by adding support for write queries. The new parallel indexes capability within the optimizer also allows for swifter performance population of indexes.
• Language Drivers & Bolt
  Bolt is great for Neo4j developers because it means better performance of the applications they build all the way around and enables them to go bigger and do more with Neo4j. Bolt is a connection-driven protocol for graph access. It utilizes a portable binary encoding over web sockets or TCP for lower latency and enhanced throughput. It comes with built-in security that enhances both graph database performance and developer experience.
  Official language drivers have been released to complement Bolt, which also encapsulate the protocol. These drivers include .NET, Java, JavaScript, and Python.
• Java Stored Procedures
  These new and powerful performance facility offers low-level and direct graph access, giving you a way to conduct an imperative code when you want to conduct complex work within the database. Neo4j comes bundled with built-in procedures as part of the APOC project. There are some very useful procedures in the APOC project so you should definitely check it out. One that stood out to me as immediately useful is the last one in the list that makes periodic commit available for use outside of LOAD CSV.
• Neo4j Browser Sync
  This is a nice convenience because now you can take your styles and queries with you wherever you go. Browser Sync lets you synchronize graph style sheets and saved scripts to preserve client-side work across connections. With this, you have swift and easy access to your

Read More......

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 first-class entity.

As a native graph database, Neo4j provides several essential advantages for businesses today:

• Neo4j structures data connections precisely as they exist in the world around us with the contextually specific connections between entities as primary entities that can be explored in constant time.
• Real-time results for queries that are exploring the many different and complex connections around and between entities throughout the database without any JOIN operations necessary.
• Data and its structure can easily be changed as well as adding new data varieties.
• Neo4j stores the data in a way that even the marketing team will understand because the entities (nodes) and connections (relationships) to other entities that were drawn on the whiteboard during the session where everyone discussed how the data was connected is exactly how it will be stored and queried.

Research Value from Connected Data

A research organization needs to quickly adapt to our rapidly evolving world. Bringing research conclusions to their customers in a timely manner is critical. Most existing hurdles in deriving meaning from research and making this actionable by the business have a lot to do with relationship navigation between data points. Creating connected-data applications on top of current data points is becoming too 

Read More......

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 housed in multiple locations.

In both cases, data architects require a data model that’s versatile and fluid when exceptions arise and business needs change. And the only model that can answer this is the graph database.

Data Management and Graph Databases

Enterprises today are flooded with “big data”, a majority of which is master data. Dealing with complex relationships between data points could be the biggest problem facing today’s enterprises.

The cost of a poor-performing data management system will affect an enterprise because data is constantly being shared, remixed, enhanced and connected. As a matter of fact, a majority of data management systems are created with a relational database, which aren’t even made for traversing connected data.

Yet, relationships in a data are essential to maintain competitive advantage with business analytics 

Read More......

Every Organization Needs a Knowledge Graph

A knowledge graph as it relates to individual organizations is a unification of information across that organization enriched with contextual and semantic relevance. Introducing a knowledge graph creates a comprehensive and baseline set of knowledge accessible by personnel, applications and customers alike to gain understanding and drive actions and direction.

This foundational knowledge graph is not only useful for people and applications, but provides a relevant and evolving dataset for sophisticated learning and intelligence software systems to utilize in providing personalized internal guidance as well as highly engaging interactions with customers.

Knowledge Sharing Falling Short

To engage all personnel in collaboration and knowledge sharing, a majority of organizations today have adopted social networking trends and offering different kinds of internal tools. However, such applications can generate large volumes of unstructured organization data stored in isolated systems across an organization. This attempt at creating a holistic understanding falls short because all this knowledge sharing and information isn’t actually being connected together.

The main result from this approach is a complex infrastructure containing data silos filled with duplicated, expired, and redundant information. This makes it hard to see the right information and acquire important insights. Organizations today need a graph data platform to support increasingly complex data management needs; deal with information flow, data infrastructure and communication problems; and allow next-generation systems to effectively seek, share, filter, and review data.

Knowledge Graph: Understanding and Growth

By embracing the nuanced complexities, semantics and contextual connections within an organization, a knowledge graph can be a catalyst for understanding and growth. The diverse and complex aspects of an organization’s business and operations are often well understood by a few subject matter experts that have gained extensive knowledge over their years of experience.

Providing a way for people and systems to connect and leverage a holistic perspective of this knowledge that exists perpetuates better insights and decision making by your subject matter experts because they 

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.

Challenges in Network and IT Operations

Enterprises are facing an increasing number of challenges as network complexity continues to increase. Here are a few examples of such challenges:

• Network troubleshooting
  Regardless if it involves network changes, increasing security access or enhancing infrastructure usages, the interdependencies of the network elements involved are highly intricate, which makes it very hard to troubleshoot.
Cause-and-effect analysis
Relationships within different nodes in the network are neither hierarchical nor linear. This makes it challenging to quickly determine dependence of sub-groups of network elements on one another. The more systems being brought together, the more complex these relationships become and the more difficult it is to isolate the chain of failure.
• Expanding virtual and physical nodes
  With surging growth within the size of networks as well as the components included to support users and services, your enterprise’s IT team will have to create systems that make room for both future and current requirements.

Network and IT Operations Benefits from Neo4j

For any IT and network operations, the Neo4j graph database should be considered for the benefits it provides with flexible graph model, which more accurately represents the topology and

Read More......