1) Data warehousing aims to bring together information from multiple sources to provide a consistent database for decision support queries and analytical applications, offloading these tasks from operational transaction systems.
2) OLAP is focused on efficient multidimensional analysis of large data volumes for decision making, while OLTP is aimed at reliable processing of high-volume transactions.
3) A data warehouse is a subject-oriented, integrated collection of historical and summarized data used for analysis and decision making, separate from operational databases.
Introduction to Data Warehousing: Introduction, Necessity, Framework
of the datawarehouse, options, developing datawarehouses, end points.
Data Warehousing Design Consideration and Dimensional Modeling:
Defining Dimensional Model, Granularity of Facts, Additivity of Facts,
Functional dependency of the Data, Helper Tables, Implementation manyto-
many relationships between fact and dimensional modelling.
History, definition, need, attributes, applications of data warehousing ; difference between data mining, big data, database and data warehouse ; future scope
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting (1) and data analysis (2). Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
The Common BI/Big Data Challenges and Solutions presented by seasoned experts, Andriy Zabavskyy (BI Architect) and Serhiy Haziyev (Director of Software Architecture).
This was a complimentary workshop where attendees had the opportunity to learn, network and share knowledge during the lunch and education session.
Introduction to Data Warehousing: Introduction, Necessity, Framework
of the datawarehouse, options, developing datawarehouses, end points.
Data Warehousing Design Consideration and Dimensional Modeling:
Defining Dimensional Model, Granularity of Facts, Additivity of Facts,
Functional dependency of the Data, Helper Tables, Implementation manyto-
many relationships between fact and dimensional modelling.
History, definition, need, attributes, applications of data warehousing ; difference between data mining, big data, database and data warehouse ; future scope
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting (1) and data analysis (2). Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
The Common BI/Big Data Challenges and Solutions presented by seasoned experts, Andriy Zabavskyy (BI Architect) and Serhiy Haziyev (Director of Software Architecture).
This was a complimentary workshop where attendees had the opportunity to learn, network and share knowledge during the lunch and education session.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
2. Motivation
Aims of information technology:
• To help workers in their everyday business activity
and improve their productivity ie. clerical data
processing tasks
• To help knowledge workers (executives,
managers, analysts) make faster and better
decisions ie. decision support systems
• Two types of applications:
– Operational applications
– Analytical applications
3. Motivation
• In most organizations, data about specific parts of
business is there - lots and lots of data,
somewhere, in some form.
On the other hand:
Data is available but not information -- and not
the right information at the right time.
Aim of data warehouse is to:
• bring together information from multiple sources
as to provide a consistent database source for
decision support queries.
• off-load decision support applications from the on-
line transaction system.
4. Warehousing
• Growing industry: $ 8 billion in 1998
• Range from desktop to huge
warehouses
– Walmart: 900-CPU, 2,700 disks, 23TB
– Teradata system
• Lots of new terms
– ROLAP, MOLAP, HOLAP
– rollup. drill-down, slice& dice
5. The Architecture of Data
What’s has been
learned from data
summaries by Logical model
Business
who, what, rules physical layout of
when,
data
where,... Metadata
Database schema who,
what,
Summary data
when,
Operational data where,
6. Decision Support and OLAP
• DSS: Information technology to help
knowledge workers (executives, managers,
analysts) make faster and better decisions:
what were the sales volumes by region and by
product category in the last year?
how did the share price of computer manufacturers
correlate with quarterly profits over the past 10
years?
will a 10% discount increase sales volume
sufficiently?
• OLAP is an element of decision support
system
• Data mining is a powerful, high-
performance data analysis tool for decision
7. Data Processing Models
There are two basic data processing
models:
• OLTP – the main aim of OLTP is reliable
and efficient processing of a large number
of transactions and ensuring data
consistency.
• OLAP – the main aim of OLAP is efficient
multidimensional processing of large data
volumes.
8. Traditional OLTP
Traditionally, DBMS have been used for
on-line transaction processing (OLTP)
• order entry: pull up order xx-yy-zz and update
status field
• banking: transfer $100 from account X to account
Y
• clerical data processing tasks
• detailed up-to-date data
• structured, repetitive tasks
• short transactions are the unit of work
• read and/or update a few records
• isolation, recovery, and integrity are critical
9. OLTP vs. OLAP
• OLTP: On Line Transaction Processing
– Describes processing at operational sites
• OLAP: On Line Analytical Processing
– Describes processing at warehouse
10. OLTP vs. OLAP
OLTP OLAP
users Clerk, IT professional Knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date historical, summarized
detailed, flat relational multidimensional
isolated integrated, consolidated
usage repetitive ad-hoc
access read/write, lots of scans
index/hash on prim. key
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
11. What is a Data Warehouse
• “A data warehouse is a subject-
oriented, integrated, time-variant,
and nonvolatile collection of data in
support of management’s decision-
making process.” --- W. H. Inmon
• Collection of data that is used primarily
in organizational decision making
• A decision support database that is
maintained separately from the
organization’s operational database
12. Data Warehouse - Subject
Oriented
• Subject oriented: oriented to the major
subject areas of the corporation that
have been defined in the data model.
• E.g. for an insurance company: customer,
product, transaction or activity, policy,
claim, account, and etc.
• Operational DB and applications may
be organized differently
• E.g. based on type of insurance's: auto,
life, medical, fire, ...
13. Data Warehouse – Integrated
• There is no consistency in encoding,
naming conventions, …, among
different data sources
• Heterogeneous data sources
• When data is moved to the warehouse,
it is converted.
14. Data Warehouse - Non-Volatile
• Operational data is regularly accessed
and manipulated a record at a time, and
update is done to data in the
operational environment.
• Warehouse Data is loaded and
accessed. Update of data does not
occur in the data warehouse
environment.
15. Data Warehouse - Time Variance
• The time horizon for the data warehouse is
significantly longer than that of operational
systems.
• Operational database: current value data.
• Data warehouse data : nothing more than a
sophisticated series of snapshots, taken of at
some moment in time.
• The key structure of operational data may or
may not contain some element of time. The
key structure of the data warehouse always
contains some element of time.
16. Why Separate Data
Warehouse?
• Performance
• special data organization, access methods,
and implementation methods are needed
to support multidimensional views and
operations typical of OLAP
• Complex OLAP queries would degrade
performance for operational transactions
• Concurrency control and recovery modes
of OLTP are not compatible with OLAP
analysis
17. Why Separate Data
Warehouse?
• Function
• missing data: Decision support requires
historical data which operational DBs do
not typically maintain
• data consolidation: DS requires
consolidation (aggregation, summarization)
of data from heterogeneous sources:
operational DBs, external sources
• data quality: different sources typically use
inconsistent data representations, codes
and formats which have to be reconciled.
18. Advantages of Warehousing
• High query performance
• Local processing at sources unaffected
• Can operate when sources unavailable
• Extra information at warehouse
– Modify, summarize (store aggregates)
– Add historical information