The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Informatica is a software tool designed to simplify Data Warehouse design and routine tasks related to
data transformation and migration i.e ETL -> Extract,transform and Load.
It is a visual interface and you will be dragging and dropping with the mouse in the Designer(client Application).This graphical approach to communicate with all major databases and can move/transform data between them. It can move huge bulk of data in a very effective way.
Informatica comes in different packages:
Informatica PowerCenter license - has all options, including distributed metadata, ability to organize repositories into a data mart domain and share metadata accross repositories.
PowerMart PowerMart - a limited license (all features except distributed metadata and multiple registered servers)Working
Working with Informatica:
Source database(s), target database(s), repository metadatabase
Informatica Server
Client Software: Designer, Server Manager and Repository Manager.
How Committed Content Marketers Get Real ResultsTomorrow People
Alistair Norman, Managing Director of award-winning B2B content marketing agency Tomorrow People, and Joe Pulizzi, the self-proclaimed ‘poster boy of content marketing’ bring you: How Committed Content Marketers Get Real Results.
Data marts,Types of Data Marts,Multidimensional Data Model,Fact table ,Dimension table ,Data Warehouse Schema,Star Schema,Snowflake Schema,Fact-Constellation Schema
What is Data Warehouse?OLTP vs. OLAP, Conceptual Modeling of Data Warehouses,Data Warehousing Components, Data Warehousing Components, Building a Data Warehouse, Mapping the Data Warehouse to a Multiprocessor Architecture, Database Architectures for Parallel Processing
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.
Business Intelligence and Multidimensional DatabaseRussel Chowdhury
It was an honor that my employer assigned me to study with Business Intelligence that follows SQL Server Analysis
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* Thanks to all the contributions gathered here to prepare the doc.
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Opendatabay - Open Data Marketplace.pptxOpendatabay
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3. Collection of conceptual tools for describing data, data
relationships, data semantics and consistency
constraint.
Conceptual representation of data structures required
for database
4.
5. Model for data management where the
databases are developed according to user's
preferences, in order to be used for specific
types of retrievals.
Multidimensional database (MDB) is mainly
optimized for data warehouse and online
analytical processing (OLAP) applications
6. Multidimensional data-base technology is a
key factor in the interactive analysis of large
amounts of data for decision-making
purposes
MDB mainly useful for sales and marketing
applications that involve time series.
7.
8. Enables interactive analyses of large amounts
of data for decision-making purposes
Rapidly process the data in the database so
that answers can be generated quickly.
Provides “just-in-time” information for
effective decision-making in a successful
OLAP application
View data as multidimensional cubes , which
have been particularly well suited for data
analyses
Enforces simplicity
9.
10.
11. Data Cube Model
Star Schema Model
Snow Flake Schema Model
Fact Constellations Schema Model
(Global Schema)
12.
13. Data is grouped or combined together in
multidimensional matrices called Data Cubes.
In two Dimension :-
row & column or products.
In three Dimension :-
one regions, products and fiscal quarters.
14. data cubes have categories of data called
dimensions and measures.
measure
◦ represents some fact (or number) such as cost or
units of service.
dimension
◦ represents descriptive categories of data such as
time or location.
15.
16. Slicing :
Refers to two- dimensional page selected
from the cube.
Dicing :
Dicing provides you the smallest available
slice.
Define a sub-cube of the original space.
Rotation :
Rotating changes the dimensional orientation
of the report from the cube data.
19. It is the simplest form of data warehousing
schema.
It consists one large central table (fact)
containing the bulk of data and a set of
smaller dimension tables one for each
dimension .
Its entity relationship diagram between
dimensions and fact table resembles a star
where one fact table is connected to multiple
dimensions or table.
20.
21.
22. It is difficult from a star schema .
In this dimensional table are organized into
hierarchy by normalization them.
The Snow Flake Schema is represented by
centralized fact table which are connected to
multiple dimensions.
23.
24.
25. It is a set of fact tables that shares some
dimensional tables.
It limits the possible queries for the data
warehouse.
Editor's Notes
Helps Analysts to know which business measures they are interested in examining, which dimensions and attributes make the data meaningful, and how the dimensions of their business are organized into levels and hierarchies.
