Olap Cube Design

Nov 14, 200821 likes9,085 views

This document discusses OLAP cubes, which provide a multi-dimensional representation of data. It describes cubes as containing facts and dimensions, and how dimensions can be organized hierarchically. It also outlines common operations on cubes like slicing, dicing, and drilling up and down hierarchies. Finally, it provides an overview of the process of building an OLAP cube in SQL Server Analysis Services.

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Predicting Influence and Communities Using Graph AlgorithmsDatabricks

Relationships are one of the most predictive indicators of behavior and preferences. Communities detection based on relationships is a powerful tool for inferring similar preferences in peer groups, anticipating future behavior, estimating group resiliency, finding hierarchies, and preparing data for other analysis. Centrality measures based on relationships identify the most important items in a network and help us understand group dynamics such as influence, accessibility, the speed at which things spread, and bridges between groups. Data scientists use graph algorithms to identify groups and estimate important entities based on their interactions. In this session, we'll cover the common uses of community detection and centrality measures and how some of the iconic graph algorithms compute values. We'll show examples of how to run community detection and centrality algorithms in Apache Spark including using the AggregateMessages function to add your own algorithms. You'll learn best practices and tips for tricky situations. For those that want to run graph algorithms in a graph platform, we'll also illustrate a few examples in Neo4j. Some of the Community Detection Algorithms included: * Triangle Count and Clustering Coefficient to estimate network cohesiveness * Strongly Connected Components and Connected Components to find clusters * Label Propagation to quickly infer groups and data cleans with semi-supervised learning * Louvain Modularity to uncover at group hierarchies Balanced Triad to identify unstable groups * PageRank to reveal influencers * Betweenness Centrality to predict bottlenecks and bridges. Authors: Amy Hodler, Sören Reichardt

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Data Warehouse Designing: Dimensional Modelling and E-R ModellingInternational Journal of Engineering Inventions www.ijeijournal.com

The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.

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"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second. This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "

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High-Performance Advanced Analytics with Spark-AlchemyDatabricks

Pre-aggregation is a powerful analytics technique as long as the measures being computed are reaggregable. Counts reaggregate with SUM, minimums with MIN, maximums with MAX, etc. The odd one out is distinct counts, which are not reaggregable. Traditionally, the non-reaggregability of distinct counts leads to an implicit restriction: whichever system computes distinct counts has to have access to the most granular data and touch every row at query time. Because of this, in typical analytics architectures, where fast query response times are required, raw data has to be duplicated between Spark and another system such as an RDBMS. This talk is for everyone who computes or consumes distinct counts and for everyone who doesn’t understand the magical power of HyperLogLog (HLL) sketches. We will break through the limits of traditional analytics architectures using the advanced HLL functionality and cross-system interoperability of the spark-alchemy open-source library, whose capabilities go beyond what is possible with OSS Spark, Redshift or even BigQuery. We will uncover patterns for 1000x gains in analytic query performance without data duplication and with significantly less capacity. We will explore real-world use cases from Swoop’s petabyte-scale systems, improve data privacy when running analytics over sensitive data, and even see how a real-time analytics frontend running in a browser can be provisioned with data directly from Spark.

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Apache spark 소개 및 실습동현 강

빅데이터 개념 부터 시작해서 빅데이터 분석 플랫폼의 출현(hadoop)과 스파크의 등장배경까지 풀어서 작성된 spark 소개 자료 입니다. 스파크는 RDD에 대한 개념과 spark SQL 라이브러리에 대한 자료가 조금 자세히 설명 되어있습니다. (텅스텐엔진, 카탈리스트 옵티마이져에 대한 간략한 설명이 있습니다.) 마지막에는 간단한 설치 및 interactive 분석 실습자료가 포함되어 있습니다. 원본 ppt 를 공개해 두었으니 언제 어디서든 필요에 따라 변형하여 사용하시되 출처만 잘 남겨주시면 감사드리겠습니다. 다른 슬라이드나, 블로그에서 사용된 그림과 참고한 자료들은 작게 출처를 표시해두었는데, 본 ppt의 초기버전을 작성하면서 찾았던 일부 자료들은 출처가 불분명한 상태입니다. 자료 출처를 알려주시면 반영하여 수정해 두도록하겠습니다. (제보 부탁드립니다!)

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