Shila Casteels

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Graph Databases - Analytical Use Cases

Mar 9, 2018 10:00:00 AM / by Shila Casteels posted in neo4j, fraud detection, graph databases, graph analytics, social network analysis, recommendation engine

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What is a graph database?

 Although graph theory has been around for centuries, graph databases started to appear relatively recently.

‘Traditional’ relational databases store data in tables. These tables have a fixed format (fixed number of columns, each with a fixed data type). Tables are linked through the primary key in one table and the corresponding foreign keys in other tables. When a query is executed, the database engine fetches the primary keys from one table and links them to the corresponding foreign keys in other tables through SQL joins.

Although this works well to insert, select, update and delete individual records over one or a limited number of tables (CRUD operations), performing joins during query execution over large schemas and data volumes is expensive and slow.

Property graph databases (like the one offered by market leader Neo4J) use nodes with labels and properties to store data instead of the relational database tables and columns. Instead of fitting all data into a fixed, predefined table structure, each node is a new instance with a structure that can be different from other nodes.

This schema-less structure offers more flexibility than the relational database table. On top of the additional flexibility, graph databases treat relationships as first class citizens. Instead of being built during query execution, relationship are persisted in a graph database. Having all relationships stored with the data not only allows to extremely outperform relational databases on relationship-heavy queryies, it allows use cases that simply aren’t possible in relational databases.

In this post, we’ll have a look at a couple of analytical use cases with graph databases.

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Business Intelligence, simplified.

Dec 18, 2017 9:30:00 AM / by Shila Casteels posted in pentaho, business intelligence, analytics, data integration


My views on BI after one year in the trenches

After having worked for about a year as a Business Intelligence consultant, I’d like to explain my views on the subject. At, we mainly work with the commercial open source BI platform Pentaho, so I’ll use that as a reference, but this post should apply to BI in general and is not meant to be limited to any given platform.

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