Informatica MDM online Training | Informatica MDM Training Classes by Sujeet Patel| Informatica MDM
•Download as PPTX, PDF•
1 like•126 views
Informatica MDM Online Training is taken by the Sujeet Patel.I will teach you how to use Informatica MDM from the beginner to advance level
Report
Share
Report
Share
Recommended
Library management
This document describes a library management system that uses a relational database to store and manage library data. It discusses using SQL and Microsoft Access to create tables, define relationships between tables using primary and foreign keys, and insert and manipulate data. Sample entity relationship diagrams and database tables are shown for books, customers, branches, book issues and returns. The system aims to computerize the library's operations and provide a more effective way to manage library resources and user accounts than a manual paper-based system.
A mini project on designing a DATABASE for Library management system using mySQL
It keeps track of all the information about the books in the library, their cost, status and total number of books available in the Library. The user will find it easy in this automated system rather than using the manual writing system. The system contains a database where all the information will be stored safely.
~> All the data types and variables,
~> test SQL-QUERIES
~> database is in the above document
Database management system presentation
A database is a collection of data that can be used alone or combined to answer users' questions. A database management system (DBMS) provides programs to manage databases, control data access, and include a query language. When designing a database, it is important to structure the data so that specific records can be easily accessed, the database can respond to different questions, minimal storage is used, and redundant data is avoided. Key concepts in database design include entities, attributes, records, primary keys, foreign keys, and relationships between tables.
Physical database design(database)
The document discusses physical database design, including:
- Designing fields by choosing data types, coding techniques, and controlling data integrity.
- Denormalizing relations through joining tables or data replication to improve processing speed at the cost of storage space and integrity.
- Organizing physical files through sequential, indexed, or hashed arrangements and using indexes to efficiently locate records.
- Database architectures including legacy systems, current technologies, and data warehouses.
Methodology logical database design for the relational
The document outlines the steps for building and validating a logical data model from a conceptual data model for a relational database:
1. Derive relationships and map entities to tables.
2. Validate relations through normalization to minimize redundancy and update issues.
3. Validate relations can support required transactions.
4. Include integrity constraints like required fields, data types, relationships.
5. Review the logical model with users.
6. Merge individual models into a single global logical data model.
7. Consider future growth and changes the model may need to accommodate.
Introduction to Database, Purpose of Data, Data models, Components of Database
This document provides an overview of database management systems and their components. It discusses the purpose of DBMSs in providing data storage and access across applications. It also describes key DBMS concepts like data models, languages for defining and manipulating data, transaction management, storage structure, database administrators, and system users. The relational model and SQL query language are highlighted as widely adopted standards. Overall, the document gives a high-level introduction to DBMS components, data management challenges addressed by DBMSs, and their role in application development.
Physical Database Design & Performance
Introduction of Physical Database Design Process
Designing Fields
Choosing Data Types
Controlling Data Integrity
Denormalizing and Partitioning Data
Designing Physical Database Files
File Organizations
Clustering Files
Indexes
Optimizing Queries
Process management seminar
The document discusses process management in data warehousing. It describes the typical components involved - load manager, warehouse manager, and query manager. The load manager is responsible for extracting, transforming and loading data. The warehouse manager manages the data in the warehouse through indexing, aggregation and normalization. The query manager directs user queries to appropriate tables. Additionally, the document outlines the three perspectives for process modeling - conceptual, logical, and physical. The conceptual perspective represents interrelationships abstractly, the logical captures structure and data characteristics, while the physical provides execution details.
Recommended
Library management
This document describes a library management system that uses a relational database to store and manage library data. It discusses using SQL and Microsoft Access to create tables, define relationships between tables using primary and foreign keys, and insert and manipulate data. Sample entity relationship diagrams and database tables are shown for books, customers, branches, book issues and returns. The system aims to computerize the library's operations and provide a more effective way to manage library resources and user accounts than a manual paper-based system.
A mini project on designing a DATABASE for Library management system using mySQL
It keeps track of all the information about the books in the library, their cost, status and total number of books available in the Library. The user will find it easy in this automated system rather than using the manual writing system. The system contains a database where all the information will be stored safely.
~> All the data types and variables,
~> test SQL-QUERIES
~> database is in the above document
Database management system presentation
A database is a collection of data that can be used alone or combined to answer users' questions. A database management system (DBMS) provides programs to manage databases, control data access, and include a query language. When designing a database, it is important to structure the data so that specific records can be easily accessed, the database can respond to different questions, minimal storage is used, and redundant data is avoided. Key concepts in database design include entities, attributes, records, primary keys, foreign keys, and relationships between tables.
Physical database design(database)
The document discusses physical database design, including:
- Designing fields by choosing data types, coding techniques, and controlling data integrity.
- Denormalizing relations through joining tables or data replication to improve processing speed at the cost of storage space and integrity.
- Organizing physical files through sequential, indexed, or hashed arrangements and using indexes to efficiently locate records.
- Database architectures including legacy systems, current technologies, and data warehouses.
Methodology logical database design for the relational
The document outlines the steps for building and validating a logical data model from a conceptual data model for a relational database:
1. Derive relationships and map entities to tables.
2. Validate relations through normalization to minimize redundancy and update issues.
3. Validate relations can support required transactions.
4. Include integrity constraints like required fields, data types, relationships.
5. Review the logical model with users.
6. Merge individual models into a single global logical data model.
7. Consider future growth and changes the model may need to accommodate.
Introduction to Database, Purpose of Data, Data models, Components of Database
This document provides an overview of database management systems and their components. It discusses the purpose of DBMSs in providing data storage and access across applications. It also describes key DBMS concepts like data models, languages for defining and manipulating data, transaction management, storage structure, database administrators, and system users. The relational model and SQL query language are highlighted as widely adopted standards. Overall, the document gives a high-level introduction to DBMS components, data management challenges addressed by DBMSs, and their role in application development.
Physical Database Design & Performance
Introduction of Physical Database Design Process
Designing Fields
Choosing Data Types
Controlling Data Integrity
Denormalizing and Partitioning Data
Designing Physical Database Files
File Organizations
Clustering Files
Indexes
Optimizing Queries
Process management seminar
The document discusses process management in data warehousing. It describes the typical components involved - load manager, warehouse manager, and query manager. The load manager is responsible for extracting, transforming and loading data. The warehouse manager manages the data in the warehouse through indexing, aggregation and normalization. The query manager directs user queries to appropriate tables. Additionally, the document outlines the three perspectives for process modeling - conceptual, logical, and physical. The conceptual perspective represents interrelationships abstractly, the logical captures structure and data characteristics, while the physical provides execution details.
Databse management system
The document discusses information system architecture, data structures, data analysis, data validation, and database management systems. It provides examples of common data structures like arrays and linked lists. It also describes different methods of data validation such as format checks, range checks, and uniqueness checks. Finally, it outlines key features of database management systems including querying, backup and replication, rule enforcement, and security.
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
Library management sytem
This document provides an overview of a proposed library management system. It describes the current manual system, limitations of the current system, and proposed automated system with advantages like faster retrieval and storage of information. It includes entity relationship diagram, data flow diagram, system flowchart, menu tree, report formats and Gantt chart for the project.
Structure of dbms
The document discusses the architecture and components of a database management system (DBMS). It describes that a DBMS is divided into modules including a query processor and storage manager. The query processor receives and optimizes SQL queries, while the storage manager is responsible for storing, retrieving, and updating data through components like a buffer manager, file manager, and transaction manager. The document also outlines some common data structures used in a DBMS like data files, data dictionaries, and indices.
Dbms
This document discusses key concepts related to databases and database management systems (DBMS). It defines metadata as data about data, such as information about a database's structure, that is stored in the data dictionary. It describes problems with traditional two-file processing systems and how the database approach integrates data and reduces data duplication. The core functions of a DBMS are presented, including its role in translating requests between users and the database. Advantages and common applications of DBMS are outlined.
Uses of dbms
The document provides an overview of database management systems, including what they are, their benefits, examples, and types of database models. It discusses that a database is a structured collection of records stored in a computer system, and a database management system (DBMS) is software used to organize, analyze, and modify the stored data. Benefits of DBMS include increased productivity, consolidated data, and the ability to easily change information systems. Examples provided are Oracle, Microsoft Access, and SQL Server. Types of database models described are distributed, network, object-oriented, hierarchical, and relational. The document also briefly mentions data security.
Structure of Database MAnagement System
Structure and characteristics of DBMS with graph.
The Functionality of DBMS and it's working methodology
and brief description of all components of DBMS.
Database performance tuning and query optimization
● DBMS Architecture.
● Performance Tuning: Client and Server.
● SQL Execution Phase.
● Query Processing Bottlenecks and Query Optimization.
● DBMS Performance Tuning.
Database Life Cycle
A & S Database Consulting Inc. is a database consulting company that offers various database services including business/data analysis, database design and modeling, database implementation, reporting and application development, and maintenance plans. The company follows a distinct database development life cycle consisting of strategy and analysis, user requirements specification, process modeling, data modeling, design, build, test and implementation, and documentation and maintenance.
Elimination of data redundancy before persisting into dbms using svm classifi...
Elimination of data redundancy before persisting into dbms using svm classification,
Data Base Management System is one of the
growing fields in computing world. Grid computing, internet
sharing, distributed computing, parallel processing and cloud
are the areas store their huge amount of data in a DBMS to
maintain the structure of the data. Memory management is
one of the major portions in DBMS due to edit, delete, recover
and commit operations used on the records. To improve the
memory utilization efficiently, the redundant data should be
eliminated accurately. In this paper, the redundant data is
fetched by the Quick Search Bad Character (QSBC) function
and intimate to the DB admin to remove the redundancy.
QSBC function compares the entire data with patterns taken
from index table created for all the data persisted in the
DBMS to easy comparison of redundant (duplicate) data in
the database. This experiment in examined in SQL server
software on a university student database and performance is
evaluated in terms of time and accuracy. The database is
having 15000 students data involved in various activities.
Keywords—Data redundancy, Data Base Management System,
Support Vector Machine, Data Duplicate.
I. INTRODUCTION
The growing (prenominal) mass of information
present in digital media has become a resistive problem for
data administrators. Usually, shaped on data congregate
from distinct origin, data repositories such as those used by
digital libraries and e-commerce agent based records with
disparate schemata and structures. Also problems regarding
to low response time, availability, security and quality
assurance become more troublesome to manage as the
amount of data grow larger. It is practicable to specimen
that the peculiarity of the data that an association uses in its
systems is relative to its efficiency for offering beneficial
services to their users. In this environment, the
determination of maintenance repositories with “dirty” data
(i.e., with replicas, identification errors, equal patterns,
etc.) goes greatly beyond technical discussion such as the
everywhere quickness or accomplishment of data
administration systems.
Nalini.M, nalini.tptwin@gmail.com, Anbu.S, anomaly detection,
data mining
big data
dbms
intrusion detection
dublicate detection
data cleaning
data redundancy
data replication, redundancy removel, QSBC, Duplicate detection, error correction, de-duplication, Data cleaning, Dbms, Data sets
Library management system
This library management system is a web application developed in ASP.NET 2.0 using C# and SQL Server 2005. It allows librarians to perform operations like issuing books, returning books, adding members, and searching for books. The application code is organized into folders for pages, themes, code files and the database. It uses tables like USERS, SUBJECTS, MEMBERS, TITLES, ISSUES and RETURNS to manage member, book and transaction data.
Materials Project Validation, Provenance, and Sandboxes by Dan Gunter
Summary of Goals, Progress, and Next steps for these three aspects of the Materials Project (materialsproject.org) infrastructure
* Validation: constantly guard against bugs in core data and imported data
* Provenance: know how data came to be
* Sandboxes: combine public and non-public data; "good fences make good neighbors"
Presenter: Dan Gunter, LBNL
Secrets of Enterprise Data Mining 201310
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2012 tools including SSMS, SSIS, and SSDT.
Oracle 11g certified associate (oca)
This course provides hands-on training to effectively plan, install, configure, administer, query, troubleshoot and manage Oracle databases. Students will learn how to install and manage Oracle databases, perform administrative tasks such as backup and recovery, and tune database performance. The course prepares students for the Oracle 11g Certified Associate certification exam.
Azure Fundamentals.pdf
This document provides an agenda for a Microsoft Azure Virtual Training Day on data fundamentals. The training will cover core data concepts, relational and non-relational data services in Azure, and data analytics. It will include modules on relational data with SQL, non-relational data with Azure Storage and Cosmos DB, large-scale data warehousing, streaming analytics, and data visualization with Power BI. Demos will illustrate how to provision Azure database and storage services and visualize data. The goal is to describe fundamental data services and concepts for working with structured, semi-structured and unstructured data on Azure.
Resume
Jitendra Gupta has worked as a database analyst for HCL Technologies in Noida, India since 2013. He has experience managing over 200 Oracle databases, performing tasks such as monitoring, maintenance, cloning, and tuning. He also has experience working as an EMAT technology analyst for a GE project in Calgary, Canada, where he classified data, performed data analysis and reporting, and maintained the database. Jitendra holds certifications in Oracle DBA, Lean Six Sigma Yellow Belt, and was trained in embedded systems and industrial training in Hindustan Aeronautics Limited.
How to Build and Promote a Successful MDM Solution on a Shoestring
Implementing a Master Data Management (MDM) sometimes seems like a daunting, expensive proposition. Many MDM efforts end being discredited and discarded in the long run.
A team of two engineers designed, developed, and implemented a MDM in our organization with a small budget. After three years, this MDM is successfully sharing enterprise data to over 40 consumers, and growing in popularity, with minimum maintenance.
Data Management
This document provides an introduction to data management. It discusses the importance of data management for making informed decisions and gaining a competitive advantage. It also outlines some key benefits of good data management, such as improved data quality and decision making, and costs of poor data management like wasted time and money. Additionally, it describes different approaches to data management like file-based and database management systems, and covers concepts such as data modeling, databases, and different database models.
What's new in the world of the Autonomous Database in 2023
This session covers the new features and happenings in the Autonomous database world and will help answer more questions DBAs, Developers will have on the Autonomous Database, from provisioning to backups, troubleshooting, tips and tricks, security, multicloud and HA. This is a good introduction for on-prem DBAs who want to learn how to migrate their databases to Cloud. Questions like how to scale up and down, how to secure their environment, how to use mtls, how to use implement data connections and equivalence between Azure and to move data between clouds, all in a quick 45-minute session which might take weeks to pick up reading documentation or spanning several presentations.
What's new in Autonomous Database - OCYatra2023 - Sandesh Rao.pdf
This session covers the new features and happenings in the Autonomous database world and will help answer more questions DBAs, Developers will have on the Autonomous Database, from provisioning to backups, troubleshooting, tips and tricks, security, multicloud and HA. This is a good introduction for on-prem DBAs who want to learn how to migrate their databases to Cloud. Questions like how to scale up and down, how to secure their environment, how to use mtls, how to use implement data connections and equivalence between Azure and to move data between clouds, all in a quick 45-minute session which might take weeks to pick up reading documentation or spanning several presentations.
Informatica MDM online Training | Informatica MDM Training Classes by Sujeet ...
The document defines various terms related to Informatica MDM online training including cell, cleanse, cleanse function, cleanse list, column, and conditional mapping. It provides the definitions and descriptions of each term and links to the online training course.
Building a Federated Data Directory Platform for Public Health
Healthcare directories underpin most healthcare systems around the world and is often a core component that enables initiatives like ‘Care Coordination’.
More Related Content
What's hot
Databse management system
The document discusses information system architecture, data structures, data analysis, data validation, and database management systems. It provides examples of common data structures like arrays and linked lists. It also describes different methods of data validation such as format checks, range checks, and uniqueness checks. Finally, it outlines key features of database management systems including querying, backup and replication, rule enforcement, and security.
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
Library management sytem
This document provides an overview of a proposed library management system. It describes the current manual system, limitations of the current system, and proposed automated system with advantages like faster retrieval and storage of information. It includes entity relationship diagram, data flow diagram, system flowchart, menu tree, report formats and Gantt chart for the project.
Structure of dbms
The document discusses the architecture and components of a database management system (DBMS). It describes that a DBMS is divided into modules including a query processor and storage manager. The query processor receives and optimizes SQL queries, while the storage manager is responsible for storing, retrieving, and updating data through components like a buffer manager, file manager, and transaction manager. The document also outlines some common data structures used in a DBMS like data files, data dictionaries, and indices.
Dbms
This document discusses key concepts related to databases and database management systems (DBMS). It defines metadata as data about data, such as information about a database's structure, that is stored in the data dictionary. It describes problems with traditional two-file processing systems and how the database approach integrates data and reduces data duplication. The core functions of a DBMS are presented, including its role in translating requests between users and the database. Advantages and common applications of DBMS are outlined.
Uses of dbms
The document provides an overview of database management systems, including what they are, their benefits, examples, and types of database models. It discusses that a database is a structured collection of records stored in a computer system, and a database management system (DBMS) is software used to organize, analyze, and modify the stored data. Benefits of DBMS include increased productivity, consolidated data, and the ability to easily change information systems. Examples provided are Oracle, Microsoft Access, and SQL Server. Types of database models described are distributed, network, object-oriented, hierarchical, and relational. The document also briefly mentions data security.
Structure of Database MAnagement System
Structure and characteristics of DBMS with graph.
The Functionality of DBMS and it's working methodology
and brief description of all components of DBMS.
Database performance tuning and query optimization
● DBMS Architecture.
● Performance Tuning: Client and Server.
● SQL Execution Phase.
● Query Processing Bottlenecks and Query Optimization.
● DBMS Performance Tuning.
Database Life Cycle
A & S Database Consulting Inc. is a database consulting company that offers various database services including business/data analysis, database design and modeling, database implementation, reporting and application development, and maintenance plans. The company follows a distinct database development life cycle consisting of strategy and analysis, user requirements specification, process modeling, data modeling, design, build, test and implementation, and documentation and maintenance.
Elimination of data redundancy before persisting into dbms using svm classifi...
Elimination of data redundancy before persisting into dbms using svm classification,
Data Base Management System is one of the
growing fields in computing world. Grid computing, internet
sharing, distributed computing, parallel processing and cloud
are the areas store their huge amount of data in a DBMS to
maintain the structure of the data. Memory management is
one of the major portions in DBMS due to edit, delete, recover
and commit operations used on the records. To improve the
memory utilization efficiently, the redundant data should be
eliminated accurately. In this paper, the redundant data is
fetched by the Quick Search Bad Character (QSBC) function
and intimate to the DB admin to remove the redundancy.
QSBC function compares the entire data with patterns taken
from index table created for all the data persisted in the
DBMS to easy comparison of redundant (duplicate) data in
the database. This experiment in examined in SQL server
software on a university student database and performance is
evaluated in terms of time and accuracy. The database is
having 15000 students data involved in various activities.
Keywords—Data redundancy, Data Base Management System,
Support Vector Machine, Data Duplicate.
I. INTRODUCTION
The growing (prenominal) mass of information
present in digital media has become a resistive problem for
data administrators. Usually, shaped on data congregate
from distinct origin, data repositories such as those used by
digital libraries and e-commerce agent based records with
disparate schemata and structures. Also problems regarding
to low response time, availability, security and quality
assurance become more troublesome to manage as the
amount of data grow larger. It is practicable to specimen
that the peculiarity of the data that an association uses in its
systems is relative to its efficiency for offering beneficial
services to their users. In this environment, the
determination of maintenance repositories with “dirty” data
(i.e., with replicas, identification errors, equal patterns,
etc.) goes greatly beyond technical discussion such as the
everywhere quickness or accomplishment of data
administration systems.
Nalini.M, nalini.tptwin@gmail.com, Anbu.S, anomaly detection,
data mining
big data
dbms
intrusion detection
dublicate detection
data cleaning
data redundancy
data replication, redundancy removel, QSBC, Duplicate detection, error correction, de-duplication, Data cleaning, Dbms, Data sets
Library management system
This library management system is a web application developed in ASP.NET 2.0 using C# and SQL Server 2005. It allows librarians to perform operations like issuing books, returning books, adding members, and searching for books. The application code is organized into folders for pages, themes, code files and the database. It uses tables like USERS, SUBJECTS, MEMBERS, TITLES, ISSUES and RETURNS to manage member, book and transaction data.
What's hot (11)
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]
Database performance tuning and query optimization
Database performance tuning and query optimization
Elimination of data redundancy before persisting into dbms using svm classifi...
Elimination of data redundancy before persisting into dbms using svm classifi...
Similar to Informatica MDM online Training | Informatica MDM Training Classes by Sujeet Patel| Informatica MDM
Materials Project Validation, Provenance, and Sandboxes by Dan Gunter
Summary of Goals, Progress, and Next steps for these three aspects of the Materials Project (materialsproject.org) infrastructure
* Validation: constantly guard against bugs in core data and imported data
* Provenance: know how data came to be
* Sandboxes: combine public and non-public data; "good fences make good neighbors"
Presenter: Dan Gunter, LBNL
Secrets of Enterprise Data Mining 201310
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2012 tools including SSMS, SSIS, and SSDT.
Oracle 11g certified associate (oca)
This course provides hands-on training to effectively plan, install, configure, administer, query, troubleshoot and manage Oracle databases. Students will learn how to install and manage Oracle databases, perform administrative tasks such as backup and recovery, and tune database performance. The course prepares students for the Oracle 11g Certified Associate certification exam.
Azure Fundamentals.pdf
This document provides an agenda for a Microsoft Azure Virtual Training Day on data fundamentals. The training will cover core data concepts, relational and non-relational data services in Azure, and data analytics. It will include modules on relational data with SQL, non-relational data with Azure Storage and Cosmos DB, large-scale data warehousing, streaming analytics, and data visualization with Power BI. Demos will illustrate how to provision Azure database and storage services and visualize data. The goal is to describe fundamental data services and concepts for working with structured, semi-structured and unstructured data on Azure.
Resume
Jitendra Gupta has worked as a database analyst for HCL Technologies in Noida, India since 2013. He has experience managing over 200 Oracle databases, performing tasks such as monitoring, maintenance, cloning, and tuning. He also has experience working as an EMAT technology analyst for a GE project in Calgary, Canada, where he classified data, performed data analysis and reporting, and maintained the database. Jitendra holds certifications in Oracle DBA, Lean Six Sigma Yellow Belt, and was trained in embedded systems and industrial training in Hindustan Aeronautics Limited.
How to Build and Promote a Successful MDM Solution on a Shoestring
Implementing a Master Data Management (MDM) sometimes seems like a daunting, expensive proposition. Many MDM efforts end being discredited and discarded in the long run.
A team of two engineers designed, developed, and implemented a MDM in our organization with a small budget. After three years, this MDM is successfully sharing enterprise data to over 40 consumers, and growing in popularity, with minimum maintenance.
Data Management
This document provides an introduction to data management. It discusses the importance of data management for making informed decisions and gaining a competitive advantage. It also outlines some key benefits of good data management, such as improved data quality and decision making, and costs of poor data management like wasted time and money. Additionally, it describes different approaches to data management like file-based and database management systems, and covers concepts such as data modeling, databases, and different database models.
What's new in the world of the Autonomous Database in 2023
This session covers the new features and happenings in the Autonomous database world and will help answer more questions DBAs, Developers will have on the Autonomous Database, from provisioning to backups, troubleshooting, tips and tricks, security, multicloud and HA. This is a good introduction for on-prem DBAs who want to learn how to migrate their databases to Cloud. Questions like how to scale up and down, how to secure their environment, how to use mtls, how to use implement data connections and equivalence between Azure and to move data between clouds, all in a quick 45-minute session which might take weeks to pick up reading documentation or spanning several presentations.
What's new in Autonomous Database - OCYatra2023 - Sandesh Rao.pdf
This session covers the new features and happenings in the Autonomous database world and will help answer more questions DBAs, Developers will have on the Autonomous Database, from provisioning to backups, troubleshooting, tips and tricks, security, multicloud and HA. This is a good introduction for on-prem DBAs who want to learn how to migrate their databases to Cloud. Questions like how to scale up and down, how to secure their environment, how to use mtls, how to use implement data connections and equivalence between Azure and to move data between clouds, all in a quick 45-minute session which might take weeks to pick up reading documentation or spanning several presentations.
Informatica MDM online Training | Informatica MDM Training Classes by Sujeet ...
The document defines various terms related to Informatica MDM online training including cell, cleanse, cleanse function, cleanse list, column, and conditional mapping. It provides the definitions and descriptions of each term and links to the online training course.
Building a Federated Data Directory Platform for Public Health
Healthcare directories underpin most healthcare systems around the world and is often a core component that enables initiatives like ‘Care Coordination’.
Cognos framework manager
Cognos Framework Manager is a metadata modeling tool.Cognos Framework Manager provides the metadata model development environment for Cognos 8.A model is a business presentation of the information from one or more data sources. The model provides a business presentation of the metadata.The model is packaged and published for report authors and query users
Live online IT Training with MaxOnlineTraining.com is an easy, effective way to maximize your skills without the travel.
Call us at For any queries, please contact:
+1 940 440 8084 / +91 953 383 7156 TODAY to join our Online IT Training course & find out how Max Online Training.com can help you embark on an exciting and lucrative IT career.
Visit www.maxonlinetraining.com
Physical Database Requirements.pdf
The document discusses physical database requirements and defines three stages of database design: conceptual, logical, and physical. It provides details on each stage, including that physical database design implements the logical data model in a DBMS and involves selecting file storage and ensuring efficient access. The document also covers database architectures, noting that a three-tier architecture separates the user applications from the physical database.
It 302 computerized accounting (week 2) - sharifah
Here are some potential ways to represent relational databases other than using tables and relationships:
- Graph databases: Represent data as nodes, edges, and properties. Nodes represent entities, edges represent relationships between entities. Good for highly connected data.
- Document databases: Store data in flexible, JSON-like documents rather than rigid tables. Good for semi-structured or unstructured data.
- Multidimensional databases (OLAP cubes): Represent data in cubes with dimensions and measures. Good for analytical queries involving aggregation and slicing/dicing of data.
- Network/graph databases: Similar to graph databases but focus more on network properties like paths, connectivity etc. Good for social networks, recommendation systems.
-
AWS July Webinar Series: Amazon Redshift Optimizing Performance
This document provides an overview and best practices for optimizing performance on Amazon Redshift. It discusses topics like data distribution, sort keys, compression, loading data efficiently, vacuum operations, and query processing. The webinar agenda covers architecture, distribution styles, sort keys, compression, workload management and more. Examples are provided to demonstrate how different techniques can significantly improve query performance. Administrative scripts and views are also recommended as helpful tools.
DBMS ppt
This document discusses database management systems and their basic concepts. It defines a database as a collection of data that can be used alone or combined to answer a user's questions. A database management system (DBMS) provides programs to manage databases, control data access, and include a query language. The document emphasizes important design considerations like making specific data easily accessible, being able to answer different user questions, minimizing storage space, and avoiding unnecessary and redundant data. It also outlines the steps in database design like requirement analysis, conceptual design using an entity-relationship model, and physical implementation using a DBMS. Key terminology explained includes entities, attributes, records, keys, and relationships between tables.
data base manage ment
This document provides an introduction to database systems for a BS in IT degree. It discusses key concepts like the difference between data and information, the historical roots of databases in file systems, database management systems and their functions, different database models including hierarchical, network and relational models, and the evolution of database models over time. It also defines important terms and describes the roles of different users in a database system environment.
SA Chapter 10
The document discusses database design at the conceptual, logical, and physical levels. At the conceptual level, entity-relationship diagrams are used to show data organization and relationships without attribute details. The logical model adds attributes and normalizes relationships into tables. The physical model specifies tables, columns, and relationships between tables based on performance factors. It may involve denormalization to improve efficiency. The key steps are: 1) Create a conceptual model from requirements; 2) Design the logical model with attributes and keys; 3) Transform to relations and normalize; 4) Design the physical model with tables and columns.
Lecture 3 note.pptx
This document provides an overview of organizing data and information. It discusses key concepts such as data, databases, database management systems, data modeling, and different database models including hierarchical, network, and relational models. Common database terminology is explained including entities, attributes, keys, and relationships. The advantages of the database approach over the traditional file-based approach are outlined. Structured Query Language and database management systems are also summarized.
Similar to Informatica MDM online Training | Informatica MDM Training Classes by Sujeet Patel| Informatica MDM (20)
Materials Project Validation, Provenance, and Sandboxes by Dan Gunter
Materials Project Validation, Provenance, and Sandboxes by Dan Gunter
How to Build and Promote a Successful MDM Solution on a Shoestring
How to Build and Promote a Successful MDM Solution on a Shoestring
What's new in the world of the Autonomous Database in 2023
What's new in the world of the Autonomous Database in 2023
What's new in Autonomous Database - OCYatra2023 - Sandesh Rao.pdf
What's new in Autonomous Database - OCYatra2023 - Sandesh Rao.pdf
Informatica MDM online Training | Informatica MDM Training Classes by Sujeet ...
Informatica MDM online Training | Informatica MDM Training Classes by Sujeet ...
Building a Federated Data Directory Platform for Public Health
Building a Federated Data Directory Platform for Public Health
It 302 computerized accounting (week 2) - sharifah
It 302 computerized accounting (week 2) - sharifah
AWS July Webinar Series: Amazon Redshift Optimizing Performance
AWS July Webinar Series: Amazon Redshift Optimizing Performance
Recently uploaded
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
BBR 2024 Summer Sessions Interview Training
Qualitative research interview training by Professor Katrina Pritchard and Dr Helen Williams
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Gender and Mental Health - Counselling and Family Therapy Applications and In...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Benner "Expanding Pathways to Publishing Careers"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneWhat is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Recently uploaded (20)
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Informatica MDM online Training | Informatica MDM Training Classes by Sujeet Patel| Informatica MDM
- 1. INDIA : +91-9015376513 Email : sk123.patel@gmail.com cross-reference table A type of system table in an ORS that Siperian Hub automatically creates for a base object. For each record of the base object, the cross-reference table contains one record per source system. This record contains the primary key from the source system and the most recent value that the source system has provided for each cell in the base object table. http://tutors99.com/en/online_training_content/course_content /2/Informatica-MDM-Online-Training
- 2. INDIA : +91-9015376513 Email : sk123.patel@gmail.com Customer Data Integration (CDI) A discipline within Master Data Management (MDM) that focuses on customer master data and its related attributes http://tutors99.com/en/online_training_content/course_content/2/Info rmatica-MDM-Online-Training
- 3. INDIA : +91-9015376513 Email : sk123.patel@gmail.com Data cleansing Process of standardizing data content and layout, decomposing/parsing text values into identifiable elements, verifying identifiable values (such as postal codes) against data libraries, and replacing incorrect values with correct values from data libraries. http://tutors99.com/en/online_training_content/course_c ontent/2/Informatica-MDM-Online-Training
- 4. INDIA : +91-9015376513 Email : sk123.patel@gmail.com Data steward Siperian Hub user who has the primary responsibility for data quality. Data stewards access Siperian Hub through the Hub Console, and use Siperian Hub tools to configure the objects in the Hub Store. http://tutors99.com/en/online_training_content/course_c ontent/2/Informatica-MDM-Online-Training
- 5. INDIA : +91-9015376513 Email : sk123.patel@gmail.com Data type Defines the characteristics of permitted values in a table column—characters, numbers, dates, binary data, and so on. Siperian Hub uses a common set of data types for columns that map directly data types for the database platform (Oracle or DB2) used in your Siperian Hub implementation. http://tutors99.com/en/online_training_content/course_c ontent/2/Informatica-MDM-Online-Training