Introduction
When it comes to enabling business analytics there are two main schools of thought.
1. Specialized Analytics
There are several specialized Analytics or Warehouse Systems and appliances, examples include Teradata, Netezza, Exadata and Hadoop.
These systems require that data is routinely copied, synchronized and translated into their own specific data format for processing. This often means that data has to be captured from production during short time windows but does mean that subsequent analytics processing is offloaded from production sources.
These systems suffer from having out of date data, they require substantial storage to retain their own large copies of data and a completely separate and often expensive and proprietary infrastructure. Note there are not too many new proprietary analytics and warehouse systems or appliances being produced, many organizations are focusing on Hadoop which is seen as lower cost, more open and easier to scale. However Hadoop still requires (at least 3) copies of the data and mechanisms to keep its data files in sync with source data.
An additional challenge is how do you secure that data if needs to be masked.
2. Abstract Analytics
There are several Analytics Tool that abstract underlying data sources and connect directly to native databases and files, examples include Microsoft SQL Server Analysis Services (SSAS), Oracle Business Intelligence Suite and Tableau.
These systems can avoid referencing out of date data by directly connecting to production sources. They also avoid the need to maintain a large physical copy of the data.
However running workload on production to prepare and refresh aggregations and analytics cubes is a strain on production that should be avoided. Also any queries that require to be resolved by the underlying database using pass through will also place workload on production.
Using production directly also means there is no opportunity to secure the data through data masking.
Enter Delphix
Delphix can provision, production or masked, virtual copies of all the data sources necessary to support your analytics system.
Delphix for Specialised Analytics
In the simplest case Delphix can support Specialized Analytics systems by allowing all the data refresh activity to be driven directly off of virtual data copies. This takes the pressure off of production, eliminates the time window constraint and also allows for the data to be masked before being synchronized to the Analytics system.
Delphix is ideal for this scenario since can curate all the source data necessary to populate your analytics system.
Delphix for Abstract Analytics
However there is much more benefit available by using Delphix with an Abstract Analytics system. You can enjoy the benefits offered by Microsoft SSAS, Oracle OBIEE, Tableau, etc which do not need a separate siloed proprietary copy of substantial data sets.
With Delphix you can also avoid the potential performance and security risks these systems can create when underpinned directly by production databases. Abstract Analytics systems can run directly on Delphix provisioned virtual data copies. This completely isolates the analytics workload from production.
The virtual databases underpinning the Analytics Engine can be refreshed from production independently or in synchronization. You can also have multiple versions of the virtual data sets.
To improve performance the virtual databases can have schema changes applied, such as indexes, materialized views, etc. which better support the analytics engine requirements.
You can even scale out by leveraging as many copies of the databases as necessary to parallelize the query activity across as many servers as possible. You can leverage underutilized server capacity to support your analytics processes.
Finally you could perform some or all of this activity on a cloud based Delphix deployment.
Summary
Delphix
enables faster, fresher, scalable analytics whilst offloading production,
avoiding a siloed investment and leveraging your existing database licenses and
database server investments.
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