Skip to main content

OLAP, Cube, Business Intelligence, Analytics


OLAP, Cube, Business Intelligence, Analytics

A. Introduction:

Modern OLAP systems have emerged as the next generation of analytical tools, addressing the limitations of traditional OLAP cubes. They offer greater flexibility, scalability, and agility in handling massive datasets and real-time data, catering to the ever-evolving needs of modern businesses.

B. Evolution of OLAP Systems:

FeatureTraditional OLAP CubesModern OLAP Systems
Measure DefinitionStaticDynamic
Data SourceLimitedDiverse
Real-time SupportNoYes
DeploymentOn-premiseCloud-based or on-premise

C. Key Features of Modern OLAP Systems:

  • Dynamic Measure Definition: Define and modify metrics on the fly, eliminating the need for pre-processing and redeployment.
  • Flexible Querying: Perform ad-hoc analysis with user-defined dimensions, measures, and aggregations.
  • Real-time Data Support: Ingest and analyze both batch and streaming data for up-to-the-minute insights.
  • High Performance: Process large volumes of data efficiently with sub-second response times.
  • Scalability: Scale horizontally to accommodate growing data volumes and user base.
  • Openness: Integrate seamlessly with various BI tools and data platforms.

D. Popular Modern OLAP Systems:

  • Apache Druid: Open-source system optimized for high-performance real-time analytics.
  • Apache Pinot: Highly scalable OLAP system designed for large datasets and fast query execution.
  • ClickHouse: Open-source column-oriented database offering real-time analytics and fast querying capabilities.
  • Kylin: Cubing engine that pre-computes and stores aggregated data for high-performance querying.

E. Comparison of Popular Modern OLAP Systems:

FeatureApache DruidApache PinotClickHouseKylin
Data ModelTime-seriesKey-valueColumnarStar schema
Query LanguageSQL-likePinot Query Language (PQL)SQLMDX
StrengthsReal-time data ingestion, fast ad-hoc queriesScalability, high performanceReal-time analytics, fast ad-hoc queriesHigh performance, pre-aggregation
WeaknessesComplex data model, resource-intensiveLimited ad-hoc query capabilitiesNot ideal for complex queriesLimited real-time capabilities

F. Future of Modern OLAP Systems:

  • Deep Integration: Seamless integration with diverse data sources and BI tools for a unified data experience.
  • AI/ML Integration: Leverage AI and machine learning for automated data analysis, anomaly detection, and predictive insights.
  • Enhanced User Experience: Focus on intuitive interfaces, natural language search, and personalized dashboards.
  • Edge Computing: Support edge-based data processing and analytics for real-time insights at the source.

G. Conclusion:

Modern OLAP systems have revolutionized data analytics by providing a flexible, scalable, and real-time solution for businesses of all sizes. As the data landscape continues to evolve, these systems are poised to play an even more crucial role in unlocking the full potential of data intelligence.


Popular posts from this blog

Functional Programming in Scala for Working Class OOP Java Programmers - Part 1

Introduction Have you ever been to a scala conf and told yourself "I have no idea what this guy talks about?" did you look nervously around and see all people smiling saying "yeah that's obvious " only to get you even more nervous? . If so this post is for you, otherwise just skip it, you already know fp in scala ;) This post is optimistic, although I'm going to say functional programming in scala is not easy, our target is to understand it, so bare with me. Let's face the truth functional programmin in scala is difficult if is difficult if you are just another working class programmer coming mainly from java background. If you came from haskell background then hell it's easy. If you come from heavy math background then hell yes it's easy. But if you are a standard working class java backend engineer with previous OOP design background then hell yeah it's difficult. Scala and Design Patterns An interesting point of view on scala, is

Alternatives to Using UUIDs

  Alternatives to Using UUIDs UUIDs are valuable for several reasons: Global Uniqueness : UUIDs are designed to be globally unique across systems, ensuring that no two identifiers collide unintentionally. This property is crucial for distributed systems, databases, and scenarios where data needs to be uniquely identified regardless of location or time. Standardization : UUIDs adhere to well-defined formats (such as UUIDv4) and are widely supported by various programming languages and platforms. This consistency simplifies interoperability and data exchange. High Collision Resistance : The probability of generating duplicate UUIDs is extremely low due to the combination of timestamp, random bits, and other factors. This collision resistance is essential for avoiding data corruption. However, there are situations where UUIDs may not be the optimal choice: Length and Readability : UUIDs are lengthy (typically 36 characters in their canonical form) and may not be human-readable. In URLs,

Dev OnCall Patterns

Introduction Being On-Call is not easy. So does writing software. Being On-Call is not just a magic solution, anyone who has been On-Call can tell you that, it's a stressful, you could be woken up at the middle of the night, and be undress stress, there are way's to mitigate that. White having software developers as On-Calls has its benefits, in order to preserve the benefits you should take special measurements in order to mitigate the stress and lack of sleep missing work-life balance that comes along with it. Many software developers can tell you that even if they were not being contacted the thought of being available 24/7 had its toll on them. But on the contrary a software developer who is an On-Call's gains many insights into troubleshooting, responsibility and deeper understanding of the code that he and his peers wrote. Being an On-Call all has become a natural part of software development. Please note I do not call software development software engineering b