Data is any set of characters that has been gathered and translated for some purpose, usually analysis. It can be any character, including text and numbers, pictures, sound, or video. If data is not put into context, it doesn't do anything to a human or computer.
A database is a collection of information that is organized so that it can be easily accessed, managed and updated.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
This IT 812 business intelligence and data warehousing looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations.
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Getting Started with Data Virtualization – What problems DV solvesDenodo
Experts and analysts agree that data virtualization's strategic role in enterprise architecture for increasing agility and flexibility in the delivery of information. In this presentation, you will find how data virtualization enables organizations to access, manage, and integrate data from a wide variety of data sources.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/IS9RGK.
How Data Virtualization Adds Value to Your Data Science StackDenodo
Watch here: https://bit.ly/3cZGCxr
For their machine learning and data science projects to be successful, data scientists need access to all of the enterprise data delivered through their myriad of data models. However, gaining access to all data, integrated into a central repository has been a challenge. Often 80% of the project time is spent on these tasks. But, a virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data science ecosystem. There is no need to change tools and learn new languages. The data virtualization platform helps data scientists offload these data integration tasks, allowing them to focus on advanced analytics.
In this session, you will learn how data virtualization:
- Provides all of the enterprise data, in real-time, and without replication
- Enables data scientists to create and share multiple logical models using simple drag and drop
- Provides a catalog of all business definitions, lineage, and relationships
An introduction to data virtualization in business intelligenceDavid Walker
A brief description of what Data Virtualisation is and how it can be used to support business intelligence applications and development. Originally presented to the ETIS Conference in Riga, Latvia in October 2013
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
Application of Data Warehousing & Data Mining to Exploitation for Supporting ...Gihan Wikramanayake
M G N A S Fernando, G N Wikramanayake (2004) "Application of Data Warehousing and Data Mining to Exploitation for Supporting the Planning of Higher Education System in Sri Lanka", In:23rd National Information Technology Conference, pp. 114-120. Computer Society of Sri Lanka Colombo, Sri Lanka: CSSL Jul 8-9, ISBN: 955-9155-12-1
This IT 812 business intelligence and data warehousing looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations.
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Getting Started with Data Virtualization – What problems DV solvesDenodo
Experts and analysts agree that data virtualization's strategic role in enterprise architecture for increasing agility and flexibility in the delivery of information. In this presentation, you will find how data virtualization enables organizations to access, manage, and integrate data from a wide variety of data sources.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/IS9RGK.
How Data Virtualization Adds Value to Your Data Science StackDenodo
Watch here: https://bit.ly/3cZGCxr
For their machine learning and data science projects to be successful, data scientists need access to all of the enterprise data delivered through their myriad of data models. However, gaining access to all data, integrated into a central repository has been a challenge. Often 80% of the project time is spent on these tasks. But, a virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data science ecosystem. There is no need to change tools and learn new languages. The data virtualization platform helps data scientists offload these data integration tasks, allowing them to focus on advanced analytics.
In this session, you will learn how data virtualization:
- Provides all of the enterprise data, in real-time, and without replication
- Enables data scientists to create and share multiple logical models using simple drag and drop
- Provides a catalog of all business definitions, lineage, and relationships
An introduction to data virtualization in business intelligenceDavid Walker
A brief description of what Data Virtualisation is and how it can be used to support business intelligence applications and development. Originally presented to the ETIS Conference in Riga, Latvia in October 2013
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
Application of Data Warehousing & Data Mining to Exploitation for Supporting ...Gihan Wikramanayake
M G N A S Fernando, G N Wikramanayake (2004) "Application of Data Warehousing and Data Mining to Exploitation for Supporting the Planning of Higher Education System in Sri Lanka", In:23rd National Information Technology Conference, pp. 114-120. Computer Society of Sri Lanka Colombo, Sri Lanka: CSSL Jul 8-9, ISBN: 955-9155-12-1
This article provides insight into relational database management systems - RDBMS, sheds light on the importance of data in any organization and how critical the role of a DBA is to its success. It highlights DBA attributes, responsibilities, tasks, career path & remuneration.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
Detailed slides of data resource management. The relationships among the many individual data elements stored in databases are based on one of several logical data structures, or models.
The database management system presentation is based on core basic concepts of database and how its works and runs .It is very easy to understand presentation for beginners to give and share so what are you waiting for grab this presentation and learn about data and database .
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. WHAT IS DATA
Data is any set of characters that has been gathered and translated for
some purpose, usually analysis. It can be any character, including text and
numbers, pictures, sound, or video. If data is not put into context, it
doesn't do anything to a human or computer.
3. DATA TYPES
A data type is a classification of the type of data that a variable or object can hold in
computer programming. Data types are an important factor in virtually all computer
programming languages, including C#, C++, JavaScript, and Visual Basic.
Common examples of data types
Boolean (e.g. True or False)
Character (e.g. a)
Date (e.g. 03/01/2016)
Double (e.g. 1.79769313486232E308)
Floating-point number (e.g. 1.234)
Integer (e.g. 1234)
Long (e.g. 123456789)
Short (e.g. 0)
String (e.g. abcd)
Void (e.g. no data)
4. WHAT IS DATABASE
A database is a collection of information that is organized so that it can be
easily accessed, managed and updated.
Data is organized into rows, columns and tables, and it is indexed to make
it easier to find relevant information. Data gets updated, expanded and
deleted as new information is added. Databases process workloads to
create and update themselves, querying the data they contain and
running applications against it.
5. DATABASE TYPES
There are following common type of database
▪ Relational Databases
▪ Operational Databases
▪ Database Warehouses
▪ Distributed Databases
▪ End-User Databases
▪ External Database
▪ Hypermedia Database
▪ Navigational Database
▪ In-Memory Database.
▪ Document-Oriented Database
▪ Real-Time Database
▪ Analytical Database
6. RELATIONAL DATABASES
Relational Databases
This is the most common of all the different types of databases. In this, the data in a relational
database is stored in various data tables. Each table has a key field which is used to connect it to
other tables. Hence all the tables are related to each other through several key fields. These
databases are extensively used in various industries and will be the one you are most likely to
come across when working in IT.
Examples of relational databases are Oracle, Sybase and Microsoft SQL Server and they are often
key parts of the process of software development.
7. OPERATIONAL DATABASES
Operational Databases
In its day to day operation, an organization generates a huge amount of data. Think of things such
as inventory management, purchases, transactions and financials. All this data is collected in a
database which is often known by several names such as operational/ production database,
subject-area database (SADB) or transaction databases.
An operational database is usually hugely important to Organizations as they include the customer
database, personal database and inventory database i.e. the details of how much of a product the
company has as well as information on the customers who buy them. The data stored in
operational databases can be changed and manipulated depending on what the company requires.
8. DATABASE WAREHOUSES
Database Warehouses
Organizations are required to keep all relevant data for several years. In the UK it can be as
long as 6 years. This data is also an important source of information for analyzing and
comparing the current year data with that of the past years which also makes it easier to
determine key trends taking place. All this data from previous years are stored in a database
warehouse. Since the data stored has gone through all kinds of screening, editing and
integration it does not need any further editing or alteration.
With this database ensure that the software requirements specification (SRS) is formally
approved as part of the project quality plan.
9. DISTRIBUTED DATABASES
Distributed Databases
Many organizations have several office locations, manufacturing plants, regional offices, branch
offices and a head office at different geographic locations. Each of these work groups may have
their own database which together will form the main database of the company. This is known as a
distributed database.
10. END-USER DATABASES
End-User Databases
There is a variety of data available at the workstation of all the end users of any organization. Each
workstation is like a small database in itself which includes data in spreadsheets, presentations,
word files, note pads and downloaded files. All such small databases form a different type of
database called the end-user database. .
11. EXTERNAL DATABASE
External Database
There is a sea of information available outside world which is required by an organization. They are
privately-owned data for which one can have conditional and limited access for a fortune. This
data is meant for commercial usage. All such databases outside the organization which are of use
and limited access are together called external database.
12. HYPERMEDIA DATABASE
Hypermedia Database
Most websites have various interconnected multimedia pages which might include text, video
clips, audio clips, photographs and graphics. These all need to be stored and “called” from
somewhere when the webpage if created. All of them together form the hypermedia database.
13. NAVIGATIONAL DATABASE
Navigational Database
Navigational database has all the items which are references from other objects. In this, one has to
navigate from one reference to other or one object to other. It might be using modern systems like
XPath. One of its applications is the air flight management systems.
14. IN-MEMORY DATABASE
In-Memory Database
An in-memory databases stores data in a computer’s main memory instead of using a disk-based
storage system. It is faster and more reliable than that in a disk. They find their application in
telecommunications network equipment.
15. DOCUMENT-ORIENTED DATABASE
Document-Oriented Database
A document oriented database is a different type of database which is used in applications which
are document oriented. The data is stored in the form of text records instead of being stored in a
data table as usually happens.
16. REAL-TIME DATABASE
Real-Time Database
A real-time database handles data which constantly keep on changing. An example of this is a stock
market database where the value of shares change every minute and need to be updated in the
real-time database. This type of database is also used in medical and scientific analysis, banking,
accounting, process control, reservation systems etc. Essentially anything which requires access to
fast moving and constantly changing information.
Assume that this will require much more time than a normal relational database when it comes to
the software testing life cycle, as these are much more complicated to efficiently test within
normal timeframes.
17. ANALYTICAL DATABASE
Analytical Database
An analytical database is used to store information from different types of databases such as
selected operational databases and external databases. Other names given to analytical databases
are information databases, management databases or multi-dimensional databases. The data
stored in an analytical database is used by the management for analysis purposes, hence the
name. The data in an analytical database cannot be changed or manipulated.