What is OLAP?

Online Analytical Processing is a decision support technology that allows large amounts of data to be analyzed. In traditional OLAP, data from OLTP databases and other sources typically undergoes an extract/transform/load (ETL) process, placing it in a data warehouse or data mart database. This process organizes data into time-oriented dimension tables that facilitate subject-based analytical queries. This structure allows a wide range of statistical queries that can compute aggregate results, detect trends, find data anomalies, and perform other analyses.

However, traditional OLAP has numerous drawbacks, including long ETL times, large disk space consumption, and complex, specialized schemas.

Doradus OLAP supports complex analytical queries but employs unique storage and access techniques that overcome drawbacks of traditional OLAP. Some advantages of Doradus OLAP are:

•	Load time: Instead of long ETL processes, data can be loaded very quickly. When an application embeds Doradus in the same process, it can load data up to 1 million objects/second.

•	Merge time: When updates are applied to a shard, they are visible to queries when the shard is merged. OLAP has been optimized so that shard merging is typically seconds to a few minutes.

•	Live data queries: New data added to a shard can be included in queries before it is merged into the corresponding shard. This allows queries to include live data feeds.

•	Data model: Applications can use the full Doradus data model, including bi-directional relationships via link fields. Doradus provides full referential integrity and bi-directional navigation of link fields.

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Doradus Query Language: DQL is used for object queries, which retrieve specific objects and their values, and for aggregate queries, which perform statistical computations across large object sets. DQL features include full text searching, path expressions, quantifiers, transitive relationship searches, multi-level grouping, and other advanced search features.

•	Query speed: Object and aggregate queries process millions of objects per second without indexes. Multi-shard queries can be searched in parallel.

•	Space usage: Doradus stores data in a columnar format that compress very well. An example OLAP event application stores 1 billion objects in less than 2GB of disk space.

•	Schema evolution: An application’s schema can be changed at any time, allowing new tables and fields to be added. Automatic data aging is available to expire old data.