Online OLAP Defined
Online OLAP is short for web-based (online) analytical processing. It is an approach to briskly answer multi-dimensional analytical queries.
Typically, applications with the capacity for OLAP include business reporting capabilities for marketing, sales, management reporting, and other related facets of an organization.
OLAP can be used for data mining to discover correlations between previously thought to be unrelated data sets. OLAP databases are not as large as normal databases since not all data is required for trend analysis.
OLAP products are typically designed with multiple users in mind. OLAP tools enable users to analyze multidimensional data from a variety of different viewpoints. OLAP has been broken down into three techniques: consolidation, drill-down, and slice and dice.
Consolidation is the aggregation phase in which data is gathered and processed in multiple dimensions. Drill-down is a process by which users can navigate the finer details of their data. Slicing and dicing involves segregating data and viewing it from several different perspectives.
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InetSoft OLAP‑Related Articles
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What is Online Analytical Processing (OLAP)?
This article defines OLAP, covering its evolution from legacy terminology to modern multidimensional data analysis. It explains key approaches—MOLAP, ROLAP, and HOLAP—and their respective strengths. It also highlights standard query languages like MDX and XML/A for interacting with OLAP systems. :contentReference[oaicite:0]{index=0}
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Understanding OLAP – Online Analytical Processing
A transcript from a podcast that breaks down the OLAP concept in simple terms, emphasizing its role in delivering fast, multidimensional analytics for business intelligence. The host illustrates OLAP’s value in answering evolving business questions without repeated heavy computation. It underscores OLAP’s importance as a mindset and best practice for BI tools. :contentReference[oaicite:1]{index=1}
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OLAP Database Definition
This article explains how OLAP databases organize data into cubes with measures and dimensions, enabling fast, multidimensional access and processing. It highlights InetSoft StyleBI’s ability to pull from OLAP cubes and integrate them with various other data sources like Hadoop and SAP. It underscores the value of combining cube-based analytics with cross-datasource mashup. :contentReference[oaicite:2]{index=2}
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What Is the Definition of an OLAP Cube?
This primer covers the structure and purpose of OLAP cubes—pre‑aggregated, multidimensional data containers designed for rapid analysis. It also discusses limitations of rigid cube structure and how InetSoft’s StyleBI overlays them with user‑driven mashup for flexibility. This empowers users to work across data sources without IT intervention. :contentReference[oaicite:3]{index=3}
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Ad Hoc Analysis and OLAP Tools
The article outlines how ad hoc analysis processes complement OLAP tools to enable agile, question‑driven business intelligence. It features InetSoft’s drag‑and‑drop mashup engine and XMLA support, highlighting self‑service analytics without heavy IT help. It compares InetSoft favorably against larger players like Power BI and Tableau in cost and flexibility. :contentReference[oaicite:4]{index=4}
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Which OLAP Tool to Use?
This article benchmarks different OLAP tool types—MOLAP, ROLAP, HOLAP—against analytical needs like interactivity, visualization, and user distribution. It confirms that OLAP remains highly relevant for trend‑spotting and “slice‑and‑dice” analytics, despite the rise of cloud and in‑memory technologies. It helps readers select the right OLAP flavor based on their use case. :contentReference[oaicite:5]{index=5}