Data warehouse vs Data marts - Online Training from NEWYORKSYS.COM
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7 data warehouse & marts
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
Data mart
The document discusses two types of data marts: independent and dependent. Independent data marts focus on a single subject area but are not designed enterprise-wide, examples include manufacturing or finance. They are quicker and cheaper to build but can contain duplicate data and inconsistencies. Dependent data marts get their data from an enterprise data warehouse, offering benefits like improved performance, security, and key performance indicator tracking. The document also outlines the key steps in designing, building, populating, accessing, and managing a data mart project.
OLAP technology
A data warehouse uses a multi-dimensional data model to consolidate data from multiple sources and support analysis. It uses a star schema with fact and dimension tables or a snowflake schema that normalizes dimensions. This allows for interactive exploration of data through OLAP operations like roll-up, drill-down, slice and dice to gain business insights. The document provides an overview of data warehousing concepts like schemas, cubes, measures and hierarchies to model and analyze historical data for decision making.
Building Data Marts – a Sprint Not A Marathon (Forward Intelligence) v5
The document discusses agile approaches to data warehousing and business intelligence systems. It advocates for iterative development using short sprints of 2-3 months to deliver useful functionality to users early. This approach focuses on rapid delivery of high priority requirements while still employing good design practices, version control, and testing. The document also outlines the people, processes, and tools needed for agile data warehousing projects, including emphasizing a collaborative team approach and using SAS Enterprise BI tools and Kimball's design patterns.
Customer Centric Data Mining
This document discusses how customer centric data mining can be used as part of a CRM strategy to gain insights into customers. It provides examples of common questions that can be answered through data mining, such as which customers are most profitable or likely to increase purchases. The document then describes how data mining techniques can be applied at different stages of the CRM lifecycle from customer acquisition to retention. Specific data mining methods and techniques are also outlined, including classification, clustering, decision trees and neural networks. Finally, a case study is presented on using decision trees for loan risk analysis at a mortgage company.
Bayes Belief Networks
The document discusses Bayesian belief networks (BBNs), which represent probabilistic relationships between variables. BBNs consist of a directed acyclic graph showing the dependencies between nodes/variables, and conditional probability tables quantifying the effects. They allow representing conditional independence between non-descendant variables given parents. The document provides an example BBN modeling a home alarm system and neighbors calling police. It then shows calculations to find the probability of a burglary given one neighbor called police using the network. Advantages are handling incomplete data, learning causation, and using prior knowledge, while a disadvantage is more complex graph construction.
Neural network
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
Data mining technique (decision tree)
This document discusses decision trees, a classification technique in data mining. It defines classification as assigning class labels to unlabeled data based on a training set. Decision trees generate a tree structure to classify data, with internal nodes representing attributes, branches representing attribute values, and leaf nodes holding class labels. An algorithm is used to recursively split the data set into purer subsets based on attribute tests until each subset belongs to a single class. The tree can then classify new examples by traversing it from root to leaf.
Recommended
7 data warehouse & marts
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
Data mart
The document discusses two types of data marts: independent and dependent. Independent data marts focus on a single subject area but are not designed enterprise-wide, examples include manufacturing or finance. They are quicker and cheaper to build but can contain duplicate data and inconsistencies. Dependent data marts get their data from an enterprise data warehouse, offering benefits like improved performance, security, and key performance indicator tracking. The document also outlines the key steps in designing, building, populating, accessing, and managing a data mart project.
OLAP technology
A data warehouse uses a multi-dimensional data model to consolidate data from multiple sources and support analysis. It uses a star schema with fact and dimension tables or a snowflake schema that normalizes dimensions. This allows for interactive exploration of data through OLAP operations like roll-up, drill-down, slice and dice to gain business insights. The document provides an overview of data warehousing concepts like schemas, cubes, measures and hierarchies to model and analyze historical data for decision making.
Building Data Marts – a Sprint Not A Marathon (Forward Intelligence) v5
The document discusses agile approaches to data warehousing and business intelligence systems. It advocates for iterative development using short sprints of 2-3 months to deliver useful functionality to users early. This approach focuses on rapid delivery of high priority requirements while still employing good design practices, version control, and testing. The document also outlines the people, processes, and tools needed for agile data warehousing projects, including emphasizing a collaborative team approach and using SAS Enterprise BI tools and Kimball's design patterns.
Customer Centric Data Mining
This document discusses how customer centric data mining can be used as part of a CRM strategy to gain insights into customers. It provides examples of common questions that can be answered through data mining, such as which customers are most profitable or likely to increase purchases. The document then describes how data mining techniques can be applied at different stages of the CRM lifecycle from customer acquisition to retention. Specific data mining methods and techniques are also outlined, including classification, clustering, decision trees and neural networks. Finally, a case study is presented on using decision trees for loan risk analysis at a mortgage company.
Bayes Belief Networks
The document discusses Bayesian belief networks (BBNs), which represent probabilistic relationships between variables. BBNs consist of a directed acyclic graph showing the dependencies between nodes/variables, and conditional probability tables quantifying the effects. They allow representing conditional independence between non-descendant variables given parents. The document provides an example BBN modeling a home alarm system and neighbors calling police. It then shows calculations to find the probability of a burglary given one neighbor called police using the network. Advantages are handling incomplete data, learning causation, and using prior knowledge, while a disadvantage is more complex graph construction.
Neural network
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
Data mining technique (decision tree)
This document discusses decision trees, a classification technique in data mining. It defines classification as assigning class labels to unlabeled data based on a training set. Decision trees generate a tree structure to classify data, with internal nodes representing attributes, branches representing attribute values, and leaf nodes holding class labels. An algorithm is used to recursively split the data set into purer subsets based on attribute tests until each subset belongs to a single class. The tree can then classify new examples by traversing it from root to leaf.
Neural Network Classification and its Applications in Insurance Industry
This document summarizes the use of neural networks for classification tasks. It discusses the advantages and disadvantages of neural networks for classification. It also presents a case study on using a neural network to classify insurance customers as likely to renew or terminate their policies based on attributes like age and zip code. The neural network achieved higher accuracy than decision trees and regression analysis on the insurance data set.
data warehouse , data mart, etl
This document defines key concepts in data warehousing including data warehouses, data marts, and ETL (extract, transform, load). It states that a data warehouse is a non-volatile collection of integrated data from multiple sources used to support management decision making. A data mart contains a single subject area of data. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
Hadoop and Enterprise Data Warehouse
This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.
Dbms architecture
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
Distributed & parallel system
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Belief Networks & Bayesian Classification
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
Decision Trees
This document provides an overview of decision trees, including definitions, key terms, algorithms, and advantages/limitations. It defines a decision tree as a model that classifies instances by sorting them from the root to a leaf node. Important terms are defined like root node, branches, and leaf nodes. Popular algorithms like CART and C5.0 are described. Advantages are that decision trees are fast, robust, and require little experimentation. Limitations include class imbalance and overfitting with too many records and few attributes.
Distributed database
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
Introduction to ETL and Data Integration
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Distributed Database System
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
DATA WAREHOUSING
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
ETL Process
ETL stands for Extraction, Transformation, and Loading. The document describes an example ETL process to load master customer data from an Excel file into an SAP BI platform. First, the data is extracted from the Excel file into the BI data warehouse using a data source and info package. Next, the data in the persistent staging area is transformed by defining the customer ID and name fields as characteristic info objects. Finally, a data transfer process loads the mapped data from the source into the appropriate info objects, completing the ETL process.
Data mart
El documento describe un data mart, que es una base de datos departamental especializada en almacenar datos de un área específica de negocio. Un data mart puede alimentarse desde un data warehouse o integrar múltiples fuentes de información. Los data marts tienen características como ser poblados por usuarios finales, actualizarse constantemente, contener información detallada y orientarse a un tema en particular. Entre los beneficios se incluyen acelerar consultas, estructurar datos para su acceso y segmentar datos en diferentes plataformas hardware. El documento también
What is a Data Warehouse and How Do I Test It?
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
Data Warehouse Modeling
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Building an Effective Data Warehouse Architecture
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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This document summarizes the use of neural networks for classification tasks. It discusses the advantages and disadvantages of neural networks for classification. It also presents a case study on using a neural network to classify insurance customers as likely to renew or terminate their policies based on attributes like age and zip code. The neural network achieved higher accuracy than decision trees and regression analysis on the insurance data set.
data warehouse , data mart, etl
This document defines key concepts in data warehousing including data warehouses, data marts, and ETL (extract, transform, load). It states that a data warehouse is a non-volatile collection of integrated data from multiple sources used to support management decision making. A data mart contains a single subject area of data. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
Hadoop and Enterprise Data Warehouse
This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.
Dbms architecture
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
Distributed & parallel system
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Belief Networks & Bayesian Classification
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
Decision Trees
This document provides an overview of decision trees, including definitions, key terms, algorithms, and advantages/limitations. It defines a decision tree as a model that classifies instances by sorting them from the root to a leaf node. Important terms are defined like root node, branches, and leaf nodes. Popular algorithms like CART and C5.0 are described. Advantages are that decision trees are fast, robust, and require little experimentation. Limitations include class imbalance and overfitting with too many records and few attributes.
Distributed database
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
Introduction to ETL and Data Integration
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Distributed Database System
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
DATA WAREHOUSING
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
ETL Process
ETL stands for Extraction, Transformation, and Loading. The document describes an example ETL process to load master customer data from an Excel file into an SAP BI platform. First, the data is extracted from the Excel file into the BI data warehouse using a data source and info package. Next, the data in the persistent staging area is transformed by defining the customer ID and name fields as characteristic info objects. Finally, a data transfer process loads the mapped data from the source into the appropriate info objects, completing the ETL process.
Data mart
El documento describe un data mart, que es una base de datos departamental especializada en almacenar datos de un área específica de negocio. Un data mart puede alimentarse desde un data warehouse o integrar múltiples fuentes de información. Los data marts tienen características como ser poblados por usuarios finales, actualizarse constantemente, contener información detallada y orientarse a un tema en particular. Entre los beneficios se incluyen acelerar consultas, estructurar datos para su acceso y segmentar datos en diferentes plataformas hardware. El documento también
What is a Data Warehouse and How Do I Test It?
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
Data Warehouse Modeling
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Building an Effective Data Warehouse Architecture
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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