I have 3 Relational DBs, I would like to map a graph database so we can manage ontologies and query across them, the databases must remain as RDBs and the RDF mapping should allow querying via SPARQL. While I understand the theory I'm struggling to find a walk through guide, and help appreciated.
I think what you are looking for is R2RML.
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I am newly started to Apacheage and wondering What are the main differences between using PostgreSQL alone and using Apache Age with PostgreSQL for data processing. I know Apacheage is an extension for grapgh database In Postgres. But what is the importance of using ApacheAge with Postgres
Apache AGE is an extension for PostgreSQL that enables users to leverage a graph database on top of the existing relational databases. AGE is an acronym for A Graph Extension , a multi-model database fork of PostgreSQL. The basic principle of the project is to create a single storage that handles both the relational and graph data model so that the users can use the standard SQL along with openCypher, one of the most popular graph query languages today.
Reference and for more information you can visit apache age github
PostgreSQL is a relational database management system ( RDBMS ). Meanwhile AGE is an extension over PostgreSQL which allows the functionalities of a graph database to be possible. If we only use PostgreSQL we won't be able to make a graph and make nodes in it and get that functionality, so this is why we use Apache AGE with PostgreSQL.
Apache AGE basically enhances PostgreSQL's relational database capabilities by incorporating graph database features. Data can be stored, accessed, and analyzed as a graph using Apache AGE, which is especially helpful for large, interconnected data sets. Using AGE, users may model and query relationships between data by using graph database features including nodes, edges, and properties.
Also, AGE integrates with PostgreSQL's SQL engine, which means that users can leverage their existing knowledge of SQL to query and analyze graph data. For visualizing you can use Apache Age Viewer.
AGE also supports many of PostgreSQL's advanced SQL features, such as window functions and CTEs (common table expressions).
You can check their website for more details.
Although the other answers are essentially correct, I want to provide a bit of context.
1. Apache Age is a powerful open-source extension of Postgres that adds graph database functionality to the relational database.
To understand this better, you should know what graph databases are. Visit the link to learn more. (graph database). In short, you can leverage open-source extensions like Apache Age to basically extend Postgres's capabilities and model complex relationships in your data.
This combination is particularly useful in scenarios where data is both structured and interconnected, such as social networks, recommendation engines, or fraud detection systems.
The following use cases of Apache Age might should further clear things up.I hope this helps! Let me know if you have any additional questions.
Use Cases of Apache Age:
Ability to store and query graph data using SQL
Combining the strengths of both graph databases and relational databases
Efficiently managing structured and interconnected data
Finding insights and relationships that might be difficult to find using traditional SQL queries alone.
Using Apache Age with PostgreSQL can provide several benefits, such as:
Graph Database Functionality: With Apache Age, users can add graph database functionality to their PostgreSQL database. This allows them to model and store data in a way that is better suited for graph data, as opposed to traditional relational database structures.
Improved Querying: Apache Age provides a graph query language called Cypher, which is specifically designed for querying graph data. This can make it easier to query complex and interconnected data, and can provide better performance for certain types of queries.
Integration with Existing PostgreSQL Systems: Apache Age is an extension for PostgreSQL, which means that it integrates seamlessly with existing PostgreSQL systems. Users can continue to use their existing tools and interfaces, and can easily incorporate graph database functionality into their existing workflows. There are many more.
I'm working on a project for uni, that is building a URL shortener. I've studied the different types of NoSQL databases, but I can't figure out which is better for my purpose and why.
I can choose between a key/value db, document-oriented, column-oriented or graph. I'm sure the graph one is not good for my goal.
Do you have any suggestions please?
For a URL shortener, you'll not need a document store --- the data is too simple.
You'll not need a column store --- columns are for sorting and searching multiple attributes, like finding all Wongs in Hong Kong.
You'll not need a graph DB --- there's no graph.
You want a key/value DB. Some that you'll want to look at are the old standard MemCache, Redis, Aerospike, DynamoDB in EC2.
An example of a URL shortener, written in Node, for AerospikeDB, can be found in this github repo - it's just a single file - and the technique can be applied to other key-value systems.
https://github.com/aerospike/url-shortener-nodejs
As part of my final thesis, I must transform a relational database in a graph-oriented database, specifically a PostgreSQL database into a Neo4j embedded database. Now, the way is the problem. In Rik Van Bruggen's book: Learning Neo4j, he mentions a data import process using ETL activities with Trascend and MuleSoft tools, but in their official sites, there's no documentation about how to do it, neither help documentation nor examples. Apart from these tools, what other ways can I use to transform this information without using my own code?
Some modeling advice:
A well normalized relational model, which was not yet denormalized for performance reasons can be translated into the equivalent graph model.
Graph model shapes are mostly driven by use-cases, so there will be opportunity for optimization and model evolution afterwards.
A good, normalized Entity-Relationship diagram often already represents a decent graph model.
So if you still have the orignal ER diagram available, try to use it as a guide.
Here are some tips that help you with the transformation:
Each entity table is represented by a label on nodes
Each row in a table is a node
Columns on those tables become node properties.
Remove technical primary keys, keep business primary keys
Add unique constraints for business primary keys, add indexes for frequent lookup attributes
Replace foreign keys with relationships to the other table, remove them afterwards
Remove data with default values, no need to store those
Data in tables that is denormalized and duplicated might have to be pulled out into separate nodes to get a cleaner model.
Indexed column names, might indicate an array property (like email1, email2, email3)
JOIN tables are transformed into relationships, columns on those tables become relationship properties
It is important to have an understanding of the graph model before you start to import data, then it just becomes the task of hydrating that model.
LOAD CSV might be your best option, but of course it means outputting a CSV first. Here are some great resources:
http://neo4j.com/docs/stable/query-load-csv.html
http://watch.neo4j.org/video/112447027
http://jexp.de/blog/2014/06/load-csv-into-neo4j-quickly-and-successfully/
http://jexp.de/blog/2014/10/load-cvs-with-success/
http://www.markhneedham.com/blog/2014/10/23/neo4j-cypher-avoiding-the-eager/
I've also written a ruby gem which lets you write a little ruby code to import data from various sources. It's called neo4apis. You can look at the neo4apis-twitter gem to get an idea for how it works:
https://github.com/neo4jrb/neo4apis-twitter/
https://github.com/neo4jrb/neo4apis-twitter/blob/master/lib/neo4apis/twitter.rb
I've actually been wanting to implement a neo4apis-activerecord to make it easy to import from SQL with ActiveRecord
You can not directly export data from relational and import to neo4j.
Because these are two different database structures.
Relational Database -
A relational database is a set of tables containing data fitted into predefined categories. Each table (which is sometimes called a relation) contains one or more data categories in columns. Each row contains a unique instance of data for the categories defined by the columns.
Graph-oriented database -
A graph database is essentially a collection of nodes and edges. Each node represents an entity (such as a person or business) and each edge represents a connection or relationship between two nodes.
Sollution To your Problem-
First, you need to design Neo4j Data structure. e.g What will be the nodes you required, what will be the relationships between the nodes.
After that you create Script in your application language to fetch data from relational database and insert it into neo4j.
Load CSA is a option to Import/Export (backup) functionality with graph database. you can not directly Export/Import data from Relational DB to Graph DB
Is there any advantage of using ontology based database (linked data) instead of RDBMS in an offline application? Does linked data provide more relations and reasoning capabilities using SPARQL than SQL? Can I not achieve the same using joins in SQL?
Suppose I am storing the details of various mobile phones. This database should answer user centric queries like
1.list of all mobiles with good (quantified) touch interface
2.mobiles similar to Samsung Galaxy s4
Can I not retrieve efficient results using RDBMS itself with joins? If the answer is yes, then would the performance of answering these queries between the two database models be of argument here? Basically what is the edge that I get get by using ontologies in such scenarios?
The main advantage of using ontologies is the formalized semantics. This way a reasoner can automatically infer new statements without writing specific code.
But it's true, that you can model every Linked Data also in RDBMS and the other way around. The same is true for querying with SPARQL or SQL. You can achieve the same results. SPARQL has some advantages if your SQL query requires multiple joins. This can be expressed in a far more meaningful way in SPARQL.
The disadvantage of ontology based databases is nowadays still a lack of performance in comparison to RDBMS.
I have recently started getting familiarized with NoSQL (HBase). I am definitely a noob.
I was investigating about ORMs and high level clients which can be used on HBase and came across a few.
Some ORM libraries like Kundera are providing SQL like data query functionality. I am finding this a little counter intuitive.
Can any one help me understand why we would again need SQL like querying if the whole objective was to move away from it?
Also can anyone comment on your experiences with ORMs for HBase? I looked at a few of them from http://wiki.apache.org/hadoop/SupportingProjects and started looking at Kundera.
Another related question - Does data query with Kundera run map reduce jobs internally?
kundera or Spring data might provide user friendly ORM layer over NoSQL databases, but the underlying entity model still has to be NoSQL friendly. This means that NoSQL users should not blindly follow RDBMS modeling strategies but design ORM entities in such a way so that all NoSQL capabilities can be used.
As a thumb rule, the kundera ORM entities should be designed using query-first strategy where first the queries need to defined so as to create primary keys and also ensuring that relationship model is used as minimal as possible. Querying on random columns and full scans should be avoided and so data might have to be replicated across entities for reducing multiple entity look ups. Also, transactions management needs to be planned. FYI, kundera does not support transactions(beyond single row TX supported by Hbase/Cassandra).
Reason for using Kundera:
1) If looking for SQL like support over HBase. As it is build on top of HBase native API, so it simply transforms these SQL queries in to corresponding GET or PUT method calls.
2) Currently it support HBase-0.20.6 only. Kundera-2.0.6 will enable support for HBase 0-90.x versions.
3) Kundera does not do sometihng out of the box to provide map reduce over SQL like queries. However support for such thing will be provided in Kundera-2.0.6 by enabling support for Hive native queries only!
It is totally JPA compliant, so no need to learn something new. It simply hides complexity at developer level with very minimal effort.
SQL like querying is for developement ease, quick developement, less error prone and reusability ofcourse!
-Vivek