Sunstar Company, Inc.


Kaminario Flash is well suited for OLTP and OLAP

Kaminario K2 All-Flash Array delivers consistently high performance combined with cost effectiveness and capacity efficiency no matter what the platform or workload.

The K2 is quite unique in that it:

  • Scales Right: Does BOTH Scale-Up and Scale-Out
  • Globally adapts to applications block-size
  • Supports global inline selective deduplication
  • Allows to mix & match Controllers and SSDs
  • Has highest capacity utilization (87.5% with K-RAID™) and highest density (up to 64 TB/U)

Ideally Suited for:

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions  

The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model.

OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations.

For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas 

Available from Sunstar Company. Please call to learn why you should consider Kaminario’s K2 for your enviornment.

The following table summarizes the major differences between OLTP and OLAP system design.

OLTP System
Online Transaction Processing
(Operational System)

OLAP System
Online Analytical Processing
(Data Warehouse)

Source of data

Operational data; OLTPs are the original source of the data.

Consolidation data; OLAP data comes from the various OLTP Databases

Purpose of data

To control and run fundamental business tasks

To help with planning, problem solving, and decision support

What the data

Reveals a snapshot of ongoing business processes

Multi-dimensional views of various kinds of business activities

Inserts and Updates

Short and fast inserts and updates initiated by end users

Periodic long-running batch jobs refresh the data


Relatively standardized and simple queries Returning relatively few records

Often complex queries involving aggregations

Processing Speed

Typically very fast

Depends on the amount of data involved; batch data refreshes and complex queries may take many hours; query speed can be improved by creating indexes

Space Requirements

Can be relatively small if historical data is archived

Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP

Database Design

Highly normalized with many tables

Typically de-normalized with fewer tables; use of star and/or snowflake schemas

Backup and Recovery

Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability

Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method