Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
The document provides an overview of connecting to and querying a database using ProdigyView. It discusses disabling the database, setting up a connection, creating a table, sanitizing data, executing insert, update and select queries, and iterating through results. Multiple ways to retrieve data from queries are demonstrated, including getting the row count and fields. Connecting to and switching between multiple databases is presented as a challenge.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, and the purpose of database systems. It also covers database definitions and fundamental building blocks like tables and records. Additionally, the document discusses selecting an appropriate database system, database development steps, and considerations for quality control and data entry.
The document discusses database design and the design process. It explains that database design involves determining the logical structure of tables and relationships between data elements. The design process consists of steps like determining relationships between data, dividing information into tables, specifying primary keys, and applying normalization rules. The document also covers entity-relationship diagrams and designing inputs and outputs, including input controls and designing report formats.
Etl Overview (Extract, Transform, And Load)LizLavaveshkul
The document provides an overview of IBM Ascential ETL tools DataStage and QualityStage. QualityStage is used for data cleansing and standardization tasks like investigating data patterns, standardizing formats, matching records, and determining which data survives from source to target. DataStage is used for data transformation and movement between source systems and targets through jobs with stages for extraction, transformation and loading.
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.
The document introduces basic concepts about using databases and SQL for campaigns including:
- Databases store related data in tables that can be queried using SQL statements like SELECT to retrieve and JOIN to combine data from multiple tables.
- Common database terms include tables, records, fields, primary keys, and SQL statements like SELECT, UPDATE, DELETE, and INSERT.
- The document provides examples of basic SELECT queries and directs readers to resources for installing a SQL client and learning more about SQL.
The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
The document provides an overview of connecting to and querying a database using ProdigyView. It discusses disabling the database, setting up a connection, creating a table, sanitizing data, executing insert, update and select queries, and iterating through results. Multiple ways to retrieve data from queries are demonstrated, including getting the row count and fields. Connecting to and switching between multiple databases is presented as a challenge.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, and the purpose of database systems. It also covers database definitions and fundamental building blocks like tables and records. Additionally, the document discusses selecting an appropriate database system, database development steps, and considerations for quality control and data entry.
The document discusses database design and the design process. It explains that database design involves determining the logical structure of tables and relationships between data elements. The design process consists of steps like determining relationships between data, dividing information into tables, specifying primary keys, and applying normalization rules. The document also covers entity-relationship diagrams and designing inputs and outputs, including input controls and designing report formats.
Etl Overview (Extract, Transform, And Load)LizLavaveshkul
The document provides an overview of IBM Ascential ETL tools DataStage and QualityStage. QualityStage is used for data cleansing and standardization tasks like investigating data patterns, standardizing formats, matching records, and determining which data survives from source to target. DataStage is used for data transformation and movement between source systems and targets through jobs with stages for extraction, transformation and loading.
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.
The document introduces basic concepts about using databases and SQL for campaigns including:
- Databases store related data in tables that can be queried using SQL statements like SELECT to retrieve and JOIN to combine data from multiple tables.
- Common database terms include tables, records, fields, primary keys, and SQL statements like SELECT, UPDATE, DELETE, and INSERT.
- The document provides examples of basic SELECT queries and directs readers to resources for installing a SQL client and learning more about SQL.
The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
An introduction to database architecture, design and development, its relation to Object Oriented Analysis & Design in software, Illustration with examples to database normalization and finally, a basic SQL guide and best practices
This document provides an overview of database basics and concepts for business analysts. It covers topics such as the need for databases, different types of database management systems (DBMS), data storage in tables, common database terminology, database normalization, SQL queries including joins and aggregations, and database design concepts.
The document discusses the purpose and importance of a data dictionary for systems analysis and design. It explains that a data dictionary defines the data about data (metadata) and includes information about entities, attributes, relationships, data flows, structures, elements and data stores. It provides examples of how these components are defined and categorized in a data dictionary. The document also outlines the process of analyzing inputs and outputs, developing data stores, and creating the overall data dictionary.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
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.
The document provides an introduction to databases and SQL. It defines what a database is as a collection of related data containing information relevant to an enterprise. It then discusses the properties of databases, what a database management system (DBMS) is, the typical functionality of a DBMS including defining, constructing, manipulating databases, and providing security. It also summarizes the components of a database system including fields, records, queries, and reports. The document then introduces SQL and its uses for data manipulation, definition, and administration. It provides examples of SQL statements for creating tables, inserting, querying, updating, and deleting data.
Databases are used to store and organize data for fast retrieval. They have several objectives like speedy retrieval, ordering, and conditional grouping of data. Database management systems (DBMS) help manage databases by defining entities, storage architecture, security, backups and more. Relational database management systems (RDBMS) are most common today and follow Codd's rules. Databases can be classified by usage (operational, data warehouse, analytical), processing type (single, distributed), storage type (flat file, indexed, trees), and content scope (legacy, hypermedia). Database contents include tables with rows and columns to store entity attributes and records. Tables have field/column definitions specifying name, data type, size and other properties
The document discusses key aspects of the ETL (extraction, transformation, and loading) process used to update data warehouses. It describes the two main strategies for building a data warehouse - the enterprise-wide top-down approach and the bottom-up data mart approach. The document also outlines the major steps in ETL including data extraction, transformation, data staging, data cleansing, data loading, and managing metadata.
Week 4 The Relational Data Model & The Entity Relationship Data Modeloudesign
The document discusses the relational data model and relational databases. It explains that the relational model organizes data into tables with rows and columns, and was invented by Edgar Codd. The model uses keys to uniquely identify rows and relationships between tables to link related data. SQL is identified as the most commonly used language for querying and managing data in relational database systems.
This document provides an overview of data dictionaries and data modeling in ABAP. It discusses key concepts like data types, domains, data elements, tables, structures, and aggregated objects. Data dictionaries are described as central sources of information that define and manage metadata. Domains specify value ranges for fields, while data elements describe field roles and semantics. Tables represent database tables, structures are like user-defined types, and views summarize distributed data. Search helps assist with searching and locks synchronize simultaneous data access. Common ABAP dictionary transactions and syntax for declaring variables and structures are also included.
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
The ETL process in data warehousing involves extraction, transformation, and loading of data. Data is extracted from operational databases, transformed to match the data warehouse schema, and loaded into the data warehouse database. As source data and business needs change, the ETL process must also evolve to maintain the data warehouse's value as a business decision making tool. The ETL process consists of extracting data from sources, transforming it to resolve conflicts and quality issues, and loading it into the target data warehouse structures.
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
This document contains questions and answers related to Informatica technical interviews. It discusses concepts like degenerate dimensions, requirements gathering, junk dimensions, staging areas, join types in Informatica and Oracle, file formats for Informatica objects, versioning, tracing levels, performance factors for different join types, databases supported by Informatica server on Windows and UNIX, overview windows, and updating source definitions. The document is a collection of commonly asked Informatica technical interview questions and answers.
This document discusses analyzing systems using data dictionaries. It defines key concepts like data flows, structures, elements and stores. It provides examples of how to define each of these components in a data dictionary, including describing data flows, structures, elements and stores. The purpose of a data dictionary is to document the data in a system to aid in analysis and development.
The document discusses databases and SQL. It defines key concepts like relational data model, relational algebra operations, and advantages of using a DBMS. The three-level architecture of a DBMS separates the internal, conceptual and external views of data. Relational algebra operations like selection, projection and joins are used to manipulate relations/tables in a database.
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
This document discusses database transactions and improving database performance. It defines transactions as operations on a database like create, retrieve, update and delete. Transactions can involve one or more of these operations on one or more tables. The document then provides examples of transactions for an appointment booking system and boat rental system. It discusses using indexes, views and denormalization to improve query performance by reducing the need to join tables. Permissions can be granted and revoked to users and roles to control database access.
The document discusses the phases of database development: planning, analysis, design, implementation, and operation and support. It also covers database design in depth, including conceptual design, logical design, and physical design. Security concepts like authorization, authentication, encryption, and access control are also summarized.
An introduction to database architecture, design and development, its relation to Object Oriented Analysis & Design in software, Illustration with examples to database normalization and finally, a basic SQL guide and best practices
This document provides an overview of database basics and concepts for business analysts. It covers topics such as the need for databases, different types of database management systems (DBMS), data storage in tables, common database terminology, database normalization, SQL queries including joins and aggregations, and database design concepts.
The document discusses the purpose and importance of a data dictionary for systems analysis and design. It explains that a data dictionary defines the data about data (metadata) and includes information about entities, attributes, relationships, data flows, structures, elements and data stores. It provides examples of how these components are defined and categorized in a data dictionary. The document also outlines the process of analyzing inputs and outputs, developing data stores, and creating the overall data dictionary.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
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.
The document provides an introduction to databases and SQL. It defines what a database is as a collection of related data containing information relevant to an enterprise. It then discusses the properties of databases, what a database management system (DBMS) is, the typical functionality of a DBMS including defining, constructing, manipulating databases, and providing security. It also summarizes the components of a database system including fields, records, queries, and reports. The document then introduces SQL and its uses for data manipulation, definition, and administration. It provides examples of SQL statements for creating tables, inserting, querying, updating, and deleting data.
Databases are used to store and organize data for fast retrieval. They have several objectives like speedy retrieval, ordering, and conditional grouping of data. Database management systems (DBMS) help manage databases by defining entities, storage architecture, security, backups and more. Relational database management systems (RDBMS) are most common today and follow Codd's rules. Databases can be classified by usage (operational, data warehouse, analytical), processing type (single, distributed), storage type (flat file, indexed, trees), and content scope (legacy, hypermedia). Database contents include tables with rows and columns to store entity attributes and records. Tables have field/column definitions specifying name, data type, size and other properties
The document discusses key aspects of the ETL (extraction, transformation, and loading) process used to update data warehouses. It describes the two main strategies for building a data warehouse - the enterprise-wide top-down approach and the bottom-up data mart approach. The document also outlines the major steps in ETL including data extraction, transformation, data staging, data cleansing, data loading, and managing metadata.
Week 4 The Relational Data Model & The Entity Relationship Data Modeloudesign
The document discusses the relational data model and relational databases. It explains that the relational model organizes data into tables with rows and columns, and was invented by Edgar Codd. The model uses keys to uniquely identify rows and relationships between tables to link related data. SQL is identified as the most commonly used language for querying and managing data in relational database systems.
This document provides an overview of data dictionaries and data modeling in ABAP. It discusses key concepts like data types, domains, data elements, tables, structures, and aggregated objects. Data dictionaries are described as central sources of information that define and manage metadata. Domains specify value ranges for fields, while data elements describe field roles and semantics. Tables represent database tables, structures are like user-defined types, and views summarize distributed data. Search helps assist with searching and locks synchronize simultaneous data access. Common ABAP dictionary transactions and syntax for declaring variables and structures are also included.
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
The ETL process in data warehousing involves extraction, transformation, and loading of data. Data is extracted from operational databases, transformed to match the data warehouse schema, and loaded into the data warehouse database. As source data and business needs change, the ETL process must also evolve to maintain the data warehouse's value as a business decision making tool. The ETL process consists of extracting data from sources, transforming it to resolve conflicts and quality issues, and loading it into the target data warehouse structures.
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
This document contains questions and answers related to Informatica technical interviews. It discusses concepts like degenerate dimensions, requirements gathering, junk dimensions, staging areas, join types in Informatica and Oracle, file formats for Informatica objects, versioning, tracing levels, performance factors for different join types, databases supported by Informatica server on Windows and UNIX, overview windows, and updating source definitions. The document is a collection of commonly asked Informatica technical interview questions and answers.
This document discusses analyzing systems using data dictionaries. It defines key concepts like data flows, structures, elements and stores. It provides examples of how to define each of these components in a data dictionary, including describing data flows, structures, elements and stores. The purpose of a data dictionary is to document the data in a system to aid in analysis and development.
The document discusses databases and SQL. It defines key concepts like relational data model, relational algebra operations, and advantages of using a DBMS. The three-level architecture of a DBMS separates the internal, conceptual and external views of data. Relational algebra operations like selection, projection and joins are used to manipulate relations/tables in a database.
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
This document discusses database transactions and improving database performance. It defines transactions as operations on a database like create, retrieve, update and delete. Transactions can involve one or more of these operations on one or more tables. The document then provides examples of transactions for an appointment booking system and boat rental system. It discusses using indexes, views and denormalization to improve query performance by reducing the need to join tables. Permissions can be granted and revoked to users and roles to control database access.
The document discusses the phases of database development: planning, analysis, design, implementation, and operation and support. It also covers database design in depth, including conceptual design, logical design, and physical design. Security concepts like authorization, authentication, encryption, and access control are also summarized.
This chapter will help you to demonstrate the working of the online blood bank system with the help of the diagrams, it includes DFD's ,architecture,block diagrams,ER-diagrams and state transition,table structure Etc.
This document provides examples and explanations of various SQL concepts including:
1. It describes the advantages of DBMS such as minimizing redundancy, eliminating redundancy, sharing data securely, improving flexibility, and ensuring data integrity.
2. It explains different types of SQL commands - DDL for defining database schema, DML for manipulating data, and DCL for controlling access. Examples are provided for commands like CREATE, ALTER, DROP, SELECT, INSERT, UPDATE, DELETE, GRANT, REVOKE.
3. It defines joins and explains different types of joins like inner join, outer joins, self join and cartesian joins that are used to combine data from multiple tables.
This document contains questions and answers related to database testing. It discusses testing data validity, integrity, performance, procedures, triggers and functions. It also describes primary keys, foreign keys, NULL values, differences between Oracle, SQL and SQL Server. Database indexing, isolation levels, and creating indexes on all columns are also covered.
Database testing involves validating that database operations produce the correct results according to requirements. This includes ensuring data accuracy, proper handling of insertions/deletions/updates, and that the database schema correctly models real-world data. Key aspects to test include data mapping between the user interface and database, adherence to ACID properties like atomicity and isolation, data integrity across interfaces, and conformity to business rules using techniques like constraint and trigger testing. Thorough database testing from the early stages is important for complex systems, as backend/database bugs could cause whole projects to fail.
It 302 computerized accounting (week 2) - sharifahalish sha
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.
-
This document discusses function-oriented design and compares it to object-oriented design. It begins by explaining function-oriented design and how a system is decomposed into a set of interacting functions that share a centralized system state. It then covers the key aspects of function-oriented design process, including:
1. Using data flow diagrams to model how data passes through the system.
2. Developing structure charts to show how high-level functions call lower-level subfunctions.
3. Creating detailed design descriptions and interface specifications for each function in a data dictionary.
An example of an ATM system is used to illustrate the function-oriented design process. The document concludes by comparing function-oriented and
Recipes 8 of Data Warehouse and Business Intelligence - Naming convention tec...Massimo Cenci
The naming convention is a key component of any IT project.
The purpose of this article is to suggest a standard for a practical and effective Data Warehouse design in Oracle environment
PostgreSQL Performance Tables Partitioning vs. Aggregated Data TablesSperasoft
Table partitioning and aggregated data tables (such as materialized views) are two approaches to improve PostgreSQL database performance as data volumes grow large over time. Table partitioning involves splitting a large table into multiple smaller tables (partitions) based on a partition function and key, while aggregated data tables pre-compute query results to avoid repeated computation. Both can improve query performance but come with caveats such as increased planning time for partitions or expensive refresh costs for materialized views. The best approach depends on each unique situation and data access patterns.
This document provides an overview of SQL programming. It covers the history of SQL and SQL Server, SQL fundamentals including database design principles like normalization, and key SQL statements like SELECT, JOIN, UNION and stored procedures. It also discusses database objects, transactions, and SQL Server architecture concepts like connections. The document is intended as a training guide, walking through concepts and providing examples to explain SQL programming techniques.
This presentation features the fundamentals of SQL tunning like SQL Processing, Optimizer and Execution Plan, Accessing Tables, Performance Improvement Consideration Partition Technique. Presented by Alphalogic Inc : https://www.alphalogicinc.com/
SQL is a language used to store, retrieve, and manage data in relational database management systems. It contains commands like SELECT, INSERT, UPDATE, DELETE to query and manipulate data. SQL also allows functions, operators, transactions, and other capabilities to ensure data integrity and security. Integrity constraints like primary keys and foreign keys help maintain relational integrity between tables.
The document discusses denormalization in database design. It begins with an introduction to normalization and outlines the normal forms from 1NF to BCNF. It then describes the denormalization process and different denormalization strategies like pre-joined tables, report tables, mirror tables, and split tables. The document discusses the pros and cons of denormalization and emphasizes the need to weigh performance needs against data integrity. It concludes by stating that selective denormalization is often required to achieve efficient performance.
This document provides an overview of the database management system used by Mawid Service. Mawid Service is a framework that allows patients to book appointments and reserve services at hospitals and clinics. It uses a database managed through SQL to store user, patient, and appointment data. The document describes common SQL operations like insert, update, delete used to manage the data. Screenshots are included showing interfaces for logging in, viewing patient records and wait status, and searching or updating data from the database. It concludes that understanding SQL is important for administrators to effectively manage the large amounts of data stored in the database.
This document provides definitions and explanations of key concepts in database management systems. It defines DBMS, RDBMS, SQL, databases, tables, fields, primary keys, unique keys, foreign keys, joins, normalization, denormalization, indexes, views, stored procedures, triggers, and more. It also explains differences between concepts like DELETE vs TRUNCATE and local vs global variables.
Brad McGehee's presentation on "How to Interpret Query Execution Plans in SQL Server 2005/2008".
Presented to the San Francisco SQL Server User Group on March 11, 2009.
The physical data model includes tables, columns, relationships, and database properties to implement the logical data model in a database. It considers performance, indexing, storage, and denormalization. The transformation from logical to physical model imposes database rules, referential integrity, and other aspects. DDL scripts are used to create the required database objects like tables, constraints, indexes, sequences, and triggers.
The document discusses Data Mining Query Language (DMQL), which is based on SQL and allows users to define data mining tasks. DMQL can be used to specify data mining primitives and define data warehouses and data marts. The syntax of DMQL is presented, including commands to use a database or data warehouse, specify attributes or dimensions, and filter from relations using conditions. An example DMQL query is provided to select from multiple tables and group results by date. References on data warehousing, OLAP, and data mining concepts are also listed.
This document discusses classification and prediction in data analysis. It defines classification as predicting categorical class labels, such as predicting if a loan applicant is risky or safe. Prediction predicts continuous numeric values, such as predicting how much a customer will spend. The document provides examples of classification, including a bank predicting loan risk and a company predicting computer purchases. It also provides an example of prediction, where a company predicts customer spending. It then discusses how classification works, including building a classifier model from training data and using the model to classify new data. Finally, it discusses decision tree induction for classification and the k-means algorithm.
Association rule mining is used to find interesting relationships among data items in large datasets. It can help with business decision making by analyzing customer purchasing patterns. For example, market basket analysis looks at what items are frequently bought together. Association rules use support and confidence metrics, where support is the probability an itemset occurs and confidence is the probability that a rule is correct. The Apriori algorithm is commonly used to generate association rules by first finding frequent itemsets that meet a minimum support threshold across multiple passes of the data. It then generates rules from those itemsets if they meet a minimum confidence. Association rule mining has various applications and can provide useful insights but also has computational limitations.
The document defines data mining as extracting useful information from large datasets. It discusses two main types of data mining tasks: descriptive tasks like frequent pattern mining and classification/prediction tasks like decision trees. Several data mining techniques are covered, including association, classification, clustering, prediction, sequential patterns, and decision trees. Real-world applications of data mining are also outlined, such as market basket analysis, fraud detection, healthcare, education, and CRM.
The document discusses minterms, maxterms, and their representation using shorthand notation in digital logic. It also covers the steps to obtain the shorthand notation for minterms and maxterms. Standard forms such as SOP and POS are introduced along with methods to simplify boolean functions into canonical forms using Karnaugh maps. The implementation of boolean functions using NAND and NOR gates is also described through examples.
Sequential circuits are circuits whose outputs depend not only on present inputs but also on past inputs or states. There are two types: synchronous use a clock signal to synchronize state changes, asynchronous can change state at any time. Common memory elements are flip-flops including RS, D, JK, and T flip-flops. The master-slave flip-flop construction using two flip-flops avoids unpredictable states by separating the sampling and output functions.
This document discusses combinational logic circuits using MSI (Medium Scale Integration) and LSI (Large Scale Integration) components. It covers various MSI components like adders, decoders, encoders, multiplexers that are used as basic building blocks. Specific circuits discussed include 4-bit parallel adder, BCD adder, magnitude comparator, priority encoder, octal to binary encoder, decoder and their applications in implementing Boolean functions using multiplexers.
This document summarizes key concepts about combinational logic circuits. It defines combinational logic as circuits whose outputs depend only on the current inputs, in contrast to sequential logic which also depends on prior inputs. Common combinational circuits are described like half and full adders used for arithmetic, as well as decoders. The design process for combinational circuits is outlined involving specification, formulation, optimization and technology mapping. Implementation of functions using NAND and NOR gates is also discussed.
This document discusses Boolean algebra and logic gates. It defines Boolean algebra as a mathematical system using two values, typically true/false or 1/0. Boolean expressions are created using common operators like AND, OR, and NOT. Truth tables define the outputs of these operators. Logic gates are physical implementations of Boolean operators, including AND, OR, NAND, and NOR gates. Laws like De Morgan's theorem and the properties of universal gates like NAND and NOR are also covered.
Digital systems process and store information in digital form using discrete values, usually binary digits 0 and 1. A computer manipulates information in binary form using transistors in on or off states. Digital systems are found in a wide range of applications and have advantages over analog systems like lower cost, greater reliability, and flexibility. Digital computers represent numbers, instructions, and data using binary numbers and perform arithmetic and logical operations on them.
Introduction to Electronic Commerce: Introduction of commerce, Electronic
commerce framework, electronic commerce and media convergence, the anatomy
of e-commerce application,The Network for Electronic Commerce: Need of network, market forces
influencing the I-way, components of I-way, network access equipment, and
global information distribution network.
The Internet as a Network Infrastructure: Introduction, the Internet terminology,
NSFNET: Architecture and Components, Internet governance: The Internet
Society.
This document discusses evaluating software development through testing functionality, efficiency, reliability, and usability. Evaluation ensures software meets high standards and improves productivity. Techniques like questionnaires, observations, and interviews identify any problems. Criteria for evaluation includes information provided, interaction effectiveness, and technical appropriateness. Testing functionality checks commands work correctly, while efficiency examines consistent performance and automation. Reliability verifies expected behavior and security. Usability considers ease of use, navigation, help, and readability. Successful interaction has no weaknesses or improvement recommendations. Suggested enhancements may increase efficiency or reliability.
This document discusses various techniques for working with macros in Microsoft Excel, including reading the code generated by macros, using absolute and relative cell references in macros, assigning icons to run macros, automating printing with a macro, adding digital signatures to macros, and references for further reading on the topic. Specific steps are outlined for creating macros that format cells as currency, bold a column, and automate printing. The importance of examining the Visual Basic code that macros generate is also emphasized.
Macros can automate tasks in Excel like data entry, validation, selection, formatting, and navigation. Macros are created by recording user actions or writing VBA code. To record a macro, the recorder is turned on, the macro is named and saved, then actions are performed and recorded. Recorded macros can then be run to automate the recorded tasks. Common uses of macros include formatting templates, entering dates and times, validating user input, and displaying messages and feedback. Key objects and statements in VBA include Range, Variables, If/Then, and loops for programming macros.
Macros automate repetitive tasks in Microsoft Access by recording and playing back a series of commands. They can be run from buttons or menus. Macros are created using either built-in or user-written commands in the VBA programming language. Common uses of macros include data entry validation, opening forms, filtering records, printing, and navigating between records. Macros contain actions applied to database objects that are triggered by events like button clicks. They allow automation of tasks like form validation, data insertion, loading, updating, and deletion.
This document discusses software testing and quality. It defines software quality as conformance to functional and performance requirements as well as development standards. It also discusses why testing is needed to ensure software works correctly and avoids costly errors. The document outlines the stages of testing including test planning, running tests, comparing results, and correcting errors. It describes different types of testing like white box, black box, and GUI testing. It provides examples of test plans and reports and emphasizes the importance of documentation. Overall, the document provides an overview of software testing processes and techniques.
This document discusses application software and business processes. It defines application software as programs that perform specific functions for users. The basic types of application software include word processing, spreadsheets, databases, and presentations. Application software is used across many industries and functions like finance, human resources, logistics, and marketing. The document also discusses bespoke vs commercial application software and how application software can enhance business processes by automating tasks, exchanging data quickly, and improving customer service.
This document provides an introduction to macros and Visual Basic for Applications (VBA). It defines macros as recorded sequences of instructions that can automate repetitive tasks. Macros can be developed using the macro recorder or by writing code in VBA. The Visual Basic editor allows writing and editing of macro code and provides windows for the project, code, and properties. Examples show how to write, run, comment on, and save a simple macro in Excel to add two numbers.
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
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You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
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Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
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Let me tell you what we see.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Transactions
A transaction is an operation carried out on the database.
Transactions can generally be identified as retrievals, inserts, updates
and deletes. This is remembered by the acronym CRUD (Create,
Retrieve, Update and Delete).
Transactions can be made up of one or more operations.
3. Identify Transactions
What do they do?
What tables do they affect?
What attributes do they affect?
How often do they run?
How many rows do they affect?
4. Transactions of Appointment System
Transaction 1 – Add a new patient
Transaction 2 – Delete a patient
Transaction 3 – Record a appointment
Transaction 4 – Show a detail list of patient and the appointments they
have had with the doctors
Transaction 5 – Show a list of patients
Transaction 6 – Update a patient record to change their address
The tables required for this system are Patient, Appointment and
Doctor.
5. CRUD Matrix of Appointment System (Blank)
Transaction
Relation
Patient Appointment Doctor
T1
T2
T3
T4
T5
T6
6. CRUD Matrix of Appointment System
Transaction
Relation
Patient Appointment Doctor
T1 C
T2 D
T3 C
T4 R R R
T5 R
T6 U
7. Transactions in the Boat Hire System
a. Enter the details of all the boats. Update any details for boats. Delete boats.
b. Enter the details for customers. Update any details for customers.
c. Enter the details for hiring of boats.
d. Enter the details for any damage to boats.
e. List the details of all the boats.
f. List the details of all the customers; their hire and for which boats.
g. List the details for damage, to which boats, during which hire periods and for which
customers.
h. Provide a summary of the hires for a particular period.
The tables required for this system are Boat, Customer , Hire and Damage.
10. Literary agent
Fill in the CRUD matrix below to show the following transactions.
Transaction 1. Add a new Author.
Transaction 2. Create a new agent and set up an appointment for her.
Transaction 3. Delete an author and all the appointments they have
had.
Transaction 4. Show a list of Agents details and the Appointments they
have had and with which Authors.
Transaction 5. Update an Agent’s address
Transaction 6. Delete an Appointment.
11. Roles in a System
Not every user is the same.
Users will need to access different parts of the system and access it
in different ways.
12. Boat Hire System - Roles
Manager – should be able to access all parts of the system, because
their role means that they might have to add and delete any data and
be able to see anything.
Admin Assistant – just carries out routine tasks, such as adding any
new customers and recording damage to boats.
Table/User Boat Customer Rental
Manager CRUD CRUD CRUD
Admin
Assistant
R CRU CRU
13. SQL Facilities to Manage Roles
Grant – gives a particular role or user in the database system access
to an object (such as a table).
Revoke – removes access to an object (such as a table) from a
particular role or user in the database system.
14. Grant
GRANT CREATE ON Boat TO Admin;
This command will give the role of Admin the right to create data on
the table Boat.
GRANT SELECT,INSERT,UPDATE,DELETE ON Boat TO smith;
GRANT ALL ON Boat TO Manager;
This command will give the role of Manager the right to carry out any
operation on the table Boat.
15. Revoke
REVOKE ALL ON Boat FROM Admin;
– this command will take away any access rights from the role of Admin
on the table Boat.
REVOKE DELETE ON Boat FROM Manager;
– this command will take away the right to delete data from the Boat
table by the Manager.
16. Performance
The term ‘Performance’ is generally used by database professionals to
refer to the way in which a query behaves when run against a
database.
Increasingly, databases contain large amounts of data...
The rate at which a query can return an answer can be slowed when it
has to sort though large numbers of records.
Performance becomes an issue...
17. Indexes
An index is a structure in a database that helps queries run more
quickly.
An index is a data structure that stores the values for a specific
column in a table that makes easier to find a record.
Improves performance
Index can also be unique which will prevent a duplicate value from
being added to that column.
18. Clustered Indexes
A clustered index alters the way that the rows are physically stored.
When you create a clustered index on a column (or a number of
columns), the database server sorts the table’s rows by that column(s).
It is like a dictionary, where all words are sorted in an alphabetical order.
(**) Note, that only one clustered index can be created per table i.e.
Primarary Key. It alters the way the table is physically stored, it couldn’t
be otherwise.
19. Non-Clustered Indexes
It creates a completely different object within the table, that contains the
column(s) selected for indexing and a pointer back to the table’s rows
containing the data.
It is like an index in the last pages of a book. All keywords are sorted and
contain a reference back to the appropriate page number. A non-
clustered index on the computer_id column, in the previous example,
would look like the table below:
20. De-Normalisation
Normalising our data model means we will have the minimum amount
of redundancy.
If we are running a query that joins tables, this will be slower than
running a query against a single table or view. This can have an effect
on performance.
Denormalisation can be done by including an attribute in a table that
should not be there according to the rules of normalisation.
21. Improving Performance with the Use of Views
View of
selected rows
or columns of
these tables
Table 1
Table 2
Table 3
Query
22. View
A view is a virtual table which
completely acts as a real table.
The use of view as a way to improve
performance.
Views can be used to combine tables,
so that instead of joining tables in a
query, the query will just access the
view and thus be quicker.
23. View
We can perform different SQL queries.
DESC department_worker_view;