This seminar covered using Neo4j, a graph database for Java developers. It began with an overview of big data and NoSQL databases, including key-value stores, column databases, document databases, and graph databases. It then discussed why graph databases are useful when data is highly interconnected. The remainder of the seminar focused on Neo4j, explaining what it is, how it compares to relational databases, and how to model and query data using Neo4j and tools like Cypher, Spring Data Neo4j, the Neo4j browser, and Neoclipse plugin. Code examples were also provided.
Performance of graph query languages:
Analysis on theperformance of graph querylanguages: comparative study of Cypher, Gremlin and native access in Neo4j
It's 2017, and I still want to sell you a graph databaseSwanand Pagnis
The aha!s and the oh-noe!s of over one year of building our product with a graph database, Neo4j, along with big brother PostgreSQL and hipster cousin Redis with Rails.
This talk will attempt to answer an important question, "when does using a graph database make sense?", through retrospection.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Querying your database in natural language was a presentation done during PyData Silicon Valley 2014, based on the quepy software project. More information at:
http://pydata.org/sv2014/abstracts/#197
https://github.com/machinalis/quepy
Big Data Analytics 2: Leveraging Customer Behavior to Enhance Relevancy in Pe...MongoDB
This session covers how to capture and analyize customer behavior to create more relevent contexts for customers. We will cover how to use your current BI features, and more importantly, how newer technologies approach the challenge. You will walk away with a good idea on how to build and drive even more contextually relevant experiences to customers for even more successful engagements.
Performance of graph query languages:
Analysis on theperformance of graph querylanguages: comparative study of Cypher, Gremlin and native access in Neo4j
It's 2017, and I still want to sell you a graph databaseSwanand Pagnis
The aha!s and the oh-noe!s of over one year of building our product with a graph database, Neo4j, along with big brother PostgreSQL and hipster cousin Redis with Rails.
This talk will attempt to answer an important question, "when does using a graph database make sense?", through retrospection.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Querying your database in natural language was a presentation done during PyData Silicon Valley 2014, based on the quepy software project. More information at:
http://pydata.org/sv2014/abstracts/#197
https://github.com/machinalis/quepy
Big Data Analytics 2: Leveraging Customer Behavior to Enhance Relevancy in Pe...MongoDB
This session covers how to capture and analyize customer behavior to create more relevent contexts for customers. We will cover how to use your current BI features, and more importantly, how newer technologies approach the challenge. You will walk away with a good idea on how to build and drive even more contextually relevant experiences to customers for even more successful engagements.
The trend nowadays is to represent the relationships between entities in a graph structure. Neo4j is a NOSQL graph database, which allows for fast and effective queries on connected data. Implementation of own algorithms is possible, which can improve the functionality of built in API. We make use of the graph database to model and recommend movies and other media content.
Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
There are many ways to use Neo4j from Java. In this talk I want to demonstrate different APIs and examples on how to build solutions on top of Neo4j using a Java based stack.
The proliferation of data from new data sources has generated greater demand for technologies that can handle and harvest value from unstructured data. Postgres is leading the movement of integrating unstructured data with the relational environment.
Postgres first added JSON and then enhanced it with new data types, functions and operators in recent releases. Now in beta is the JSONB “binary JSON” type. These advances follow the longstanding HStore data type added in 2006 to support key/value stores in Postgres. Now Postgres users can learn how to harness these capabilities to master unstructured data challenges with Postgres.
The presentation also covers:
* An overview of JSON data types and operators
* Examples of SELECT, UPDATE, etc
* An examination of performance considerations
For more information, please email sales@enterprisedb.com
YouTube Link: https://youtu.be/T6uCFDRVoRE
** Python Certification Training: https://www.edureka.co/python **
This Edureka Python JSON PPT will introduce you to JSON in Python and how you can do Parsing with various other operations.
The session will focus on pointers like:
Introduction to JSON in Python
Why do we use JSON?
Parsing JSON
Coding Demonstration
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Graph Databases in the Microsoft EcosystemMarco Parenzan
With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.
The trend nowadays is to represent the relationships between entities in a graph structure. Neo4j is a NOSQL graph database, which allows for fast and effective queries on connected data. Implementation of own algorithms is possible, which can improve the functionality of built in API. We make use of the graph database to model and recommend movies and other media content.
Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
There are many ways to use Neo4j from Java. In this talk I want to demonstrate different APIs and examples on how to build solutions on top of Neo4j using a Java based stack.
The proliferation of data from new data sources has generated greater demand for technologies that can handle and harvest value from unstructured data. Postgres is leading the movement of integrating unstructured data with the relational environment.
Postgres first added JSON and then enhanced it with new data types, functions and operators in recent releases. Now in beta is the JSONB “binary JSON” type. These advances follow the longstanding HStore data type added in 2006 to support key/value stores in Postgres. Now Postgres users can learn how to harness these capabilities to master unstructured data challenges with Postgres.
The presentation also covers:
* An overview of JSON data types and operators
* Examples of SELECT, UPDATE, etc
* An examination of performance considerations
For more information, please email sales@enterprisedb.com
YouTube Link: https://youtu.be/T6uCFDRVoRE
** Python Certification Training: https://www.edureka.co/python **
This Edureka Python JSON PPT will introduce you to JSON in Python and how you can do Parsing with various other operations.
The session will focus on pointers like:
Introduction to JSON in Python
Why do we use JSON?
Parsing JSON
Coding Demonstration
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Graph Databases in the Microsoft EcosystemMarco Parenzan
With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.
Code as Data workshop: Using source{d} Engine to extract insights from git re...source{d}
This workshop will teach you the basics git concepts (such as references, commits, and blobs) and how they can be mapped into a series of relational tables.
Once we understand the basic concepts we will show how language classification and program parsing are available as SQL custom functions, how to use them correctly, and how to obtain aggregate results with `GROUP BY` and friends. We will discuss Universal Abstract Syntax Trees and how some advanced checks can be done on top this language agnostic structure. Running these checks at scale requires some extra knowledge and we’ll discuss the challenges and their possible solutions.
To finish, we will also discuss how the information in git repositories encodes a form of social network which can be used to better understand the engineering processes of a given organization.
Combine Spring Data Neo4j and Spring Boot to quicklNeo4j
Speakers: Michael Hunger (Neo Technology) and Josh Long (Pivotal)
Spring Data Neo4j 3.0 is here and it supports Neo4j 2.0. Neo4j is a tiny graph database with a big punch. Graph databases are imminently suited to asking interesting questions, and doing analysis. Want to load the Facebook friend graph? Build a recommendation engine? Neo4j's just the ticket. Join Spring Data Neo4j lead Michael Hunger (@mesirii) and Spring Developer Advocate Josh Long (@starbuxman) for a look at how to build smart, graph-driven applications with Spring Data Neo4j and Spring Boot.
Polyglot Persistence with MongoDB and Neo4jCorie Pollock
Learn how to enhance your application by using Neo4j and MongoDB together. Polyglot persistence is the concept of taking advantage of the strengths of different database technologies to improve functionality and enhance your application. In this webinar we will examine some use cases where it makes sense to use a document database (MongoDB) with a graph database (Neo4j) in a single application. Specifically, we will show how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations. Finally we will look at the Neo4j Doc Manager project which facilitates syncing data from MongoDB to Neo4j to make polyglot persistence with MongoDB and Neo4j much easier.
Donald Miner will do a quick introduction to Apache Hadoop, then discuss the different ways Python can be used to get the job done in Hadoop. This includes writing MapReduce jobs in Python in various different ways, interacting with HBase, writing custom behavior in Pig and Hive, interacting with the Hadoop Distributed File System, using Spark, and integration with other corners of the Hadoop ecosystem. The state of Python with Hadoop is far from stable, so we'll spend some honest time talking about the state of these open source projects and what's missing will also be discussed.
Presented in DDD Melbourne on on Sat Aug 8th 2015
Himanshu Desai, Ahmed El-Harouny & Daniel Janczak
DocumentDB, Mongo or RavenDB? If you are starting out on a new project and considering NoSQL database as an option, which one should you do choose? What if the option you choose today may not work out to be the best one for your needs?
Come and join us for this session, we will take you on a journey where we will explain each of these database on their merits and compare them and also share War stories.
http://dddmelbourne.com
An introduction to Graph databases and in particular Neo4j, including where Neo4j lives on the CAP Scale in relation to other databases, the Graph data model and a very quick introduction to the Cypher Query Language.
Demi Ben Ari - Apache Spark 101 - First Steps into distributed computing:
The world has changed, having one huge server won’t do the job, the ability to Scale Out would be your savior. Apache Spark is a fast and general engine for big data processing, with streaming, SQL, machine learning and graph processing. Showing the basics of Apache Spark and distributed computing.
Demi is a Software engineer, Entrepreneur and an International Tech Speaker.
Demi has over 10 years of experience in building various systems both from the field of near real time applications and Big Data distributed systems.
Co-Founder of the “Big Things” Big Data community and Google Developer Group Cloud.
Big Data Expert, but interested in all kinds of technologies, from front-end to backend, whatever moves data around.
7. Key Value Stores
• Most Based on Dynamo: Amazon Highly
Available Key-Value Store
• Data Model:
– Global key-value mapping
– Big scalable HashMap
– Highly fault tolerant (typically)
• Projects:
8. Key Value Stores
• Pros:
– Simple data model
– Scalable
• Cons
– Create your own “foreign keys”
– Poor for complex data
9. Column Databases
• Most Based on BigTable: Google’s Distributed
Storage System for Structured Data
• Data Model:
– A big table, with column families
– Map Reduce for querying/processing
• Projects:
10. Column Databases
• Pros:
– Supports Simi-Structured Data
– Naturally Indexed (columns)
– Scalable
• Cons
– Poor for interconnected data
11. Document Databases
• Data Model:
– A collection of documents
– A document is a key value collection
– Index-centric, lots of map-reduce
• Projects :
12. Document Databases
• Pros:
– Simple, powerful data model
– Scalable
• Cons
– Poor for interconnected data
– Query model limited to keys and indexes
– Map reduce for larger queries
14. Graph Databases
• Pros:
– Powerful data model, as general as RDBMS
– Connected data locally indexed
– Easy to query
• Cons
– Sharding ( lots of people working on this)
• Scales UP reasonably well
– Requires rewiring your brain
22. GraphDB Overview
Data is more connected:
• Text (content)
• HyperText (added pointers)
• RSS (joined those pointers)
• Blogs (added pingbacks)
• Tagging (grouped related data)
• RDF (described connected data)
• GGG (content + pointers + relationships +
descriptions)
23. GraphDB Overview
Data is less structured:
• If you tried to collect all the data of every
movie ever made, how would you model
it?
• Actors, Characters, Locations, Dates, Costs,
Ratings, Showings, Ticket Sales, etc.
25. What is Graph
• An abstract representation of a set of
objects where some pairs are connected by
links.
Object (Vertex, Node)
Link (Edge, Arc, Relationship)
26. Different Kinds of Graphs
• Undirected Graph
• Directed Graph
• Pseudo Graph
• Multi Graph
• Hyper Graph
27. More Kinds of Graphs
• Weighted Graph
• Labeled Graph
• Property Graph
29. What is a Graph DB?
• A database with an explicit graph structure
• Each node knows its adjacent nodes
• As the number of nodes increases, the cost
of a local step (or hop) remains the same
• Plus an Index for lookups
32. What is Neo4j?
• A java based graph database
• Property Graph
• Full ACID (atomicity, consistency, isolation, durability)
• High Availability (with Enterprise Edition)
• 32 Billion Nodes, 32 Billion Relationships,
64 Billion Properties
• Embedded Server
• REST API
33. What is Neo4j?
• Both nodes and relationships can have metadata.
• Integrated pattern-matching-based query language (“Cypher”).
• Also the “Gremlin” graph traversal language can be used.
• Indexing of nodes and relationships. (Lucene)
• Nice self-contained web admin.
• Advanced path-finding with multiple algorithms.
• Optimized for reads.
• Has transactions (in the Java API)
• Scriptable in Groovy
• Online backup, advanced monitoring and High Availability is
AGPL/commercial licensed
34. Neo4j is good for :
• Highly connected data (social networks)
• Recommendations (e-commerce)
• Path Finding (how do I know you?)
• A* (Least Cost path)
• Data First Schema (bottom-up, but you still
need to design)
37. If you’ve ever
• Joined more than 7 tables together
• Modeled a graph in a table
• Written a recursive CTE
• Tried to write some crazy stored procedure
with multiple recursive self and inner joins
You should use Neo4j
38. rewiring you brain
Language LanguageCountry Country
language_code language_code country_code
language_name country_code country_name
word_count primary flag_uri
Language Country
name name
IS_SPOKEN_IN
code code
word_count as_primary flag_uri
40. rewiring you brain
Country
name
flag_uri
language_name
number_of_words
yes_in_langauge
no_in_language
currency_code
Country Language
name name
flag_uri number_of_words
SPEAKS
yes
no
Currency
code
name
41. show me the code!
GraphDatabaseService graphDb =
new EmbeddedGraphDatabase("var/neo4j");
Node david = graphDb.createNode();
Node andreas = graphDb.createNode();
david.setProperty("name", "David Montag");
andreas.setProperty("name", "Andreas Kollegger");
Relationship presentedWith =
david.createRelationshipTo(andreas,
PresentationTypes.PRESENTED_WITH);
presentedWith.setProperty("date", System.currentTimeMillis());
45. console.neo4j.org
Try it right now:
start n=node(*) match n-[r:LOVES]->m return n, type(r), m
Notice the two nodes in red, they are your result set.
47. Spring-Data-Neo4J
• Focus on Spring Data Neo4j
• VMWare is collaborating with Neo Technology, the
company behind the Neo4j graph database.
• Improved programming model: Annotation-based
programming model for applications with rich
domain models
• Cross-store persistence: Extend existing JPA
application with NoSQL persistence
• Tagging (grouped related data)
• RDF (described connected data)
51. Spring-Data-Neo4J
@RelationshipEntity
public class Role {
@StartNode private Actor actor;
@EndNode private Movie movie;
private String roleName;
}
@NodeEntity
public class Actor {
@RelatedToVia(type = “ACTS_IN”)
private Iterable<Role> roles;
}
An undirected graph is one in which edges have no orientation. The edge (a, b) is identical to the edge (b, a).A directed graph or digraph is an ordered pair D = (V, A)A pseudo graph is a graph with loopsA multi graph allows for multiple edges between nodesA hyper graph allows an edge to join more than two nodes
An undirected graph is one in which edges have no orientation. The edge (a, b) is identical to the edge (b, a).A directed graph or digraph is an ordered pair D = (V, A)A pseudo graph is a graph with loopsA multi graph allows for multiple edges between nodesA hyper graph allows an edge to join more than two nodes
Best used: For graph-style, rich or complex, interconnected data. Neo4j is quite different from the others in this sense.For example: Social relations, public transport links, road maps, network topologies.