The document discusses combiners and partitioners in MapReduce frameworks. It explains that combiners allow for local aggregation of map output key-value pairs before shuffling to reducers. This can significantly reduce the amount of data transferred between maps and reduces. For a combiner to be effective, the reduce operation must be commutative and associative so the local aggregations can be merged. The document provides examples of operations like sum() and max() that qualify for use as combiners. It also discusses factors like serialization overhead that should be considered when deciding whether a combiner will provide benefits for a given job.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
The document discusses the rise of NoSQL databases. It notes that NoSQL databases are designed to run on clusters of commodity hardware, making them better suited than relational databases for large-scale data and web-scale applications. The document also discusses some of the limitations of relational databases, including the impedance mismatch between relational and in-memory data structures and their inability to easily scale across clusters. This has led many large websites and organizations handling big data to adopt NoSQL databases that are more performant and scalable.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
The document discusses combiners and partitioners in MapReduce frameworks. It explains that combiners allow for local aggregation of map output key-value pairs before shuffling to reducers. This can significantly reduce the amount of data transferred between maps and reduces. For a combiner to be effective, the reduce operation must be commutative and associative so the local aggregations can be merged. The document provides examples of operations like sum() and max() that qualify for use as combiners. It also discusses factors like serialization overhead that should be considered when deciding whether a combiner will provide benefits for a given job.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
The document discusses the rise of NoSQL databases. It notes that NoSQL databases are designed to run on clusters of commodity hardware, making them better suited than relational databases for large-scale data and web-scale applications. The document also discusses some of the limitations of relational databases, including the impedance mismatch between relational and in-memory data structures and their inability to easily scale across clusters. This has led many large websites and organizations handling big data to adopt NoSQL databases that are more performant and scalable.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
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.
This document provides an overview of key concepts related to data warehousing including what a data warehouse is, common data warehouse architectures, types of data warehouses, and dimensional modeling techniques. It defines key terms like facts, dimensions, star schemas, and snowflake schemas and provides examples of each. It also discusses business intelligence tools that can analyze and extract insights from data warehouses.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
This document provides an overview of data warehousing. It defines a data warehouse as a central database that includes information from several different sources and keeps both current and historical data to support management decision making. The document describes key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses common data warehouse architectures and applications.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
Big data is high-volume, high-velocity, and high-variety data that is difficult to process using traditional data management tools. It is characterized by 3Vs: volume of data is growing exponentially, velocity as data streams in real-time, and variety as data comes from many different sources and formats. The document discusses big data analytics techniques to gain insights from large and complex datasets and provides examples of big data sources and applications.
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
The key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
Introduction to Data Mining and Data WarehousingKamal Acharya
This document provides details about a course on data mining and data warehousing. The course objectives are to understand the foundational principles and techniques of data mining and data warehousing. The course description covers topics like data preprocessing, classification, association analysis, cluster analysis, and data warehouses. The course is divided into 10 units that cover concepts and algorithms for data mining techniques. Practical exercises are included to apply techniques to real-world data problems.
This document discusses storage management in database systems. It describes the storage device hierarchy from fastest but smallest (cache) to slowest but largest (magnetic tapes). It covers main memory, hard disks, solid state drives and tertiary storage. The document also discusses RAID configurations and how the relational model is represented on secondary storage through records, blocks, files and indexes.
The document provides an introduction to database management systems and databases. It discusses:
1) Why we need DBMS and examples of common databases like bank, movie, and railway databases.
2) The definitions of data, information, databases, and DBMS. A DBMS allows for the creation, storage, and retrieval of data from a database.
3) Different types of file organization methods like heap, sorted, indexed, and hash files and their pros and cons. File organization determines how records are stored and accessed in a database.
1. We provide database administration and management services for Oracle, MySQL, and SQL Server databases.
2. Big Data solutions need to address storing large volumes of varied data and extracting value from it quickly through processing and visualization.
3. Hadoop is commonly used to store and process large amounts of unstructured and semi-structured data in parallel across many servers.
Big data analytics: Technology's bleeding edgeBhavya Gulati
There can be data without information , but there can not be information without data.
Companies without Big Data Analytics are deaf and dumb , mere wanderers on web.
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.
This document provides an overview of key concepts related to data warehousing including what a data warehouse is, common data warehouse architectures, types of data warehouses, and dimensional modeling techniques. It defines key terms like facts, dimensions, star schemas, and snowflake schemas and provides examples of each. It also discusses business intelligence tools that can analyze and extract insights from data warehouses.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
This document provides an overview of data warehousing. It defines a data warehouse as a central database that includes information from several different sources and keeps both current and historical data to support management decision making. The document describes key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses common data warehouse architectures and applications.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
The document discusses deductive databases and how they differ from conventional databases. Deductive databases contain facts and rules that allow implicit facts to be deduced from the stored information. This reduces the amount of storage needed compared to explicitly storing all facts. Deductive databases use logic programming through languages like Datalog to specify rules that define virtual relations. The rules allow new facts to be inferred through an inference engine even if they are not explicitly represented.
Big data is high-volume, high-velocity, and high-variety data that is difficult to process using traditional data management tools. It is characterized by 3Vs: volume of data is growing exponentially, velocity as data streams in real-time, and variety as data comes from many different sources and formats. The document discusses big data analytics techniques to gain insights from large and complex datasets and provides examples of big data sources and applications.
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
The key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
Introduction to Data Mining and Data WarehousingKamal Acharya
This document provides details about a course on data mining and data warehousing. The course objectives are to understand the foundational principles and techniques of data mining and data warehousing. The course description covers topics like data preprocessing, classification, association analysis, cluster analysis, and data warehouses. The course is divided into 10 units that cover concepts and algorithms for data mining techniques. Practical exercises are included to apply techniques to real-world data problems.
This document discusses storage management in database systems. It describes the storage device hierarchy from fastest but smallest (cache) to slowest but largest (magnetic tapes). It covers main memory, hard disks, solid state drives and tertiary storage. The document also discusses RAID configurations and how the relational model is represented on secondary storage through records, blocks, files and indexes.
The document provides an introduction to database management systems and databases. It discusses:
1) Why we need DBMS and examples of common databases like bank, movie, and railway databases.
2) The definitions of data, information, databases, and DBMS. A DBMS allows for the creation, storage, and retrieval of data from a database.
3) Different types of file organization methods like heap, sorted, indexed, and hash files and their pros and cons. File organization determines how records are stored and accessed in a database.
1. We provide database administration and management services for Oracle, MySQL, and SQL Server databases.
2. Big Data solutions need to address storing large volumes of varied data and extracting value from it quickly through processing and visualization.
3. Hadoop is commonly used to store and process large amounts of unstructured and semi-structured data in parallel across many servers.
Big data analytics: Technology's bleeding edgeBhavya Gulati
There can be data without information , but there can not be information without data.
Companies without Big Data Analytics are deaf and dumb , mere wanderers on web.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
One Size Doesn't Fit All: The New Database Revolutionmark madsen
Slides from a webcast for the database revolution research report (report will be available at http://www.databaserevolution.com)
Choosing the right database has never been more challenging, or potentially rewarding. The options available now span a wide spectrum of architectures, each of which caters to a particular workload. The range of pricing is also vast, with a variety of free and low-cost solutions now challenging the long-standing titans of the industry. How can you determine the optimal solution for your particular workload and budget? Register for this Webcast to find out!
Robin Bloor, Ph.D. Chief Analyst of the Bloor Group, and Mark Madsen of Third Nature, Inc. will present the findings of their three-month research project focused on the evolution of database technology. They will offer practical advice for the best way to approach the evaluation, procurement and use of today’s database management systems. Bloor and Madsen will clarify market terminology and provide a buyer-focused, usage-oriented model of available technologies.
Webcast video and audio will be available on the report download site as well.
This document summarizes a study on the role of Hadoop in information technology. It discusses how Hadoop provides a flexible and scalable architecture for processing large datasets in a distributed manner across commodity hardware. It overcomes limitations of traditional data analytics architectures that could only analyze a small percentage of data due to restrictions in data storage and retrieval speeds. Key features of Hadoop include being economical, scalable, flexible and reliable for storing and processing large amounts of both structured and unstructured data from multiple sources in a fault-tolerant manner.
This document provides an overview of a data analytics session covering big data architecture, connecting and extracting data from storage, traditional processing with a bank use case, Hadoop-HDFS solutions, and HDFS working. The key topics covered include big data architecture layers, structured and unstructured data extraction, comparisons of storage media, traditional versus Hadoop approaches, HDFS basics including blocks and replication across nodes. The session aims to help learners understand efficient analytics systems for handling large and diverse data sources.
Cloudera Breakfast: Advanced Analytics Part II: Do More With Your DataCloudera, Inc.
This document discusses how Cloudera Enterprise Data Hub (EDH) can be used for advanced analytics. EDH allows users to perform diverse concurrent analytics on large datasets without moving the data. It includes tools for machine learning, graph analytics, search, and statistical analysis. EDH protects data through security features and system change tracking. The document argues that EDH is the only platform that can support all these analytics capabilities in a single, integrated system. It provides several examples of how advanced analytics on EDH have helped organizations like the government address important problems.
This document discusses using Apache Hadoop and SQL Server to analyze large datasets. It finds that SQL Server struggles to efficiently query and analyze datasets with over 100 million rows, with query times increasing substantially with larger datasets. Apache Hadoop provides a more scalable solution by distributing data processing across a cluster. The document evaluates Hadoop and MongoDB for big data analysis, and chooses Hadoop for its ability to process large amounts of data for analytical purposes. It then discusses implementing Hortonworks Data Platform with Apache Ambari to analyze a 97GB population dataset using Hadoop.
1) The document discusses using decision tables to drive the development of data-driven applications. Decision trees are converted to tables stored in a database to define application logic and behavior.
2) Storing decision tables in the database allows changing application behavior by altering the tables. It also enables fast queries by searching for results matching conditions.
3) The author finds decision table-driven development helps testing, maintains application logic separately from code, and eases maintenance through the lifetime of an application.
SQL vs NoSQL: Big Data Adoption & Success in the EnterpriseAnita Luthra
Overview of SQL vs NoSQL. When to use NoSQL vs structured databases. Shows roadmap and considerations for defining success of implementation of Big Data in the enterprise. This presentation also provides a quick overview of the different types of Big-Data databases
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
This document discusses big data workflows. It begins by defining big data and workflows, noting that workflows are task-oriented processes for decision making. Big data workflows require many servers to run one application, unlike traditional IT workflows which run on one server. The document then covers the 5Vs and 1C characteristics of big data: volume, velocity, variety, variability, veracity, and complexity. It lists software tools for big data platforms, business analytics, databases, data mining, and programming. Challenges of big data are also discussed: dealing with size and variety of data, scalability, analysis, and management issues. Major application areas are listed in private sector domains like retail, banking, manufacturing, and government.
Here are the key points about the application and utility of database management systems based on the article:
- Database management systems allow for efficient storage, organization and retrieval of large amounts of data. They help businesses and organizations manage their data in a centralized and structured manner.
- Teaching accounting information systems (AIS) courses effectively requires hands-on experience with database software like Microsoft Access. Simply lecturing from textbooks is not sufficient in today's environment.
- Incorporating database software into the AIS curriculum gives students practical experience building and working with databases. This helps demonstrate real-world applications of concepts like database design, queries, forms and reports.
- Hands-on learning with databases helps reinforce topics covered in A
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
The document provides an introduction to big data and Hadoop. It defines big data as large datasets that cannot be processed using traditional computing techniques due to the volume, variety, velocity, and other characteristics of the data. It discusses traditional data processing versus big data and introduces Hadoop as an open-source framework for storing, processing, and analyzing large datasets in a distributed environment. The document outlines the key components of Hadoop including HDFS, MapReduce, YARN, and Hadoop distributions from vendors like Cloudera and Hortonworks.
The document discusses NoSQL databases as an alternative to traditional SQL databases. It provides an overview of NoSQL databases, including their key features, data models, and popular examples like MongoDB and Cassandra. Some key points:
- NoSQL databases were developed to overcome limitations of SQL databases in handling large, unstructured datasets and high volumes of read/write operations.
- NoSQL databases come in various data models like key-value, column-oriented, and document-oriented. Popular examples discussed are MongoDB and Cassandra.
- MongoDB is a document database that stores data as JSON-like documents. It supports flexible querying. Cassandra is a column-oriented database developed by Facebook that is highly scalable
The document provides an overview of Hadoop and its core components. It discusses:
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers.
- The two core components of Hadoop are HDFS for distributed storage, and MapReduce for distributed processing. HDFS stores data reliably across machines, while MapReduce processes large amounts of data in parallel.
- Hadoop can operate in three modes - standalone, pseudo-distributed and fully distributed. The document focuses on setting up Hadoop in standalone mode for development and testing purposes on a single machine.
This document discusses databases and their importance in information systems. It begins by defining data, information, and knowledge, explaining how data is transformed into useful information and knowledge through organization and context. It then describes different types of databases, focusing on flat file databases and relational databases. Flat file databases store all data in one file but have limitations around data duplication, searchability, and concurrent access. Relational databases break data into normalized tables with relationships between them, addressing those limitations through their structure and use of queries. The document provides examples to illustrate key differences between the two database types.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
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
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
3. Problems while handling large data:
A large volume of data poses new challenges, such as overloaded memory and
algorithms that never stop running. It forces you to adapt and expand your repertoire of
techniques. But even when you can perform your analysis, you should take care of
issues such as I/O (input/output) and CPU starvation, because these can cause speed
issues.
4. General Techniques for handling large data:
Never-ending algorithms, out-of-memory errors, and speed
issues are the most common challenges you face when working with large
data. In this section, we’ll investigate solutions to overcome or alleviate these
problems.
5. Choosing the right algorithm:
Choosing the right algorithm can solve more problems than adding
more or better hardware. An algorithm that’s well suited for handling large data doesn’t
need to load the entire data set into memory to make predictions. Ideally, the algorithm
also supports parallelized calculations.
Some of the three algorithms are,
Online Algorithms,
Block Matrices,
MapReduce.
6. Choosing the right data structure:
Algorithms can make or break your program, but the way you store
your data is of equal importance. Data structures have different storage requirements,
but also influence the performance of CRUD (create, read, update, and delete) and other
operations on the data set.
8. Selecting the right tools:
With the right class of algorithms and data structures in place, it’s time to choose the
right tool for the job. The right tool can be a Python library or at least a tool that’s
controlled from Python. The number of helpful tools available is enormous, so we’ll
look at only a handful of them.
9. General programming tips for dealing with large data sets:
The tricks that work in a general programming context still apply for
data science. Several might be worded slightly differently, but the principles are
essentially the same for all programmers. This section recapitulates those tricks that are
important in a data science context.
Don’t reinvent the wheel
Get the most out of your hardware
Reduce your computing needs
10. Case study 1: Predicting malicious URLs:
The internet is probably one of the greatest inventions of modern
times. It has boosted humanity’s development, but not everyone uses this great
invention with honorable intentions. Many companies (Google, for one) try to protect
us from fraud by detecting malicious websites for us. Doing so is no easy task, because
the internet has billions of web pages to scan. In this case study we’ll show how to work
with a data set that no longer fits in memory.
Step 1: Defining the research goal
Step 2: Acquiring the URL data
Step 4: Data exploration
Step 5: Model building
11. Case study 2: Building a recommender system inside a
database:
In reality most of the data you work with is stored in a relational database, but most
databases aren’t suitable for data mining. But as shown in this example, it’s possible to
adapt our techniques so you can do a large part of the analysis inside the database itself,
thereby profiting from the database’s query optimizer, which will optimize the code for
you. In this example we’ll go into how to use the hash table data structure and how to
use Python to control other tools.
Tools and techniques needed
Step 1: Research question
Step 3: Data preparation
Step 5: Model building
Step 6: Presentation and automation
13. Distributing data storage and processing with frameworks:
New big data technologies such as Hadoop and Spark make it much
easier to work with and control a cluster of computers. Hadoop can scale up to
thousands of computers, creating a cluster with petabytes of storage. This enables
businesses to grasp the value of the massive amount of data available.
Hadoop: a framework for storing and processing large data sets
Apache Hadoop is a framework that simplifies working with a cluster
of computers. It aims to be all of the following things and more:
Reliable,
Fault Tolerant,
Scalable,
Portable.
14. An example for MapReduce flow for counting the color in input text:
15. Spark: replacing MapReduce for better performance
Data scientists often do interactive analysis and rely
on algorithms that are inherently iterative; it can take awhile until an algorithm
converges to a solution. As this is a weak point of the MapReduce framework, we’ll
introduce the Spark Framework to overcome it. Spark improves the performance on
such tasks by an order of magnitude.
16. Case study: Assessing risk when loaning money
Enriched with a basic understanding of Hadoop and Spark, we’re now
ready to get our hands dirty on big data. The goal of this case study is to have a first
experience with the technologies we introduced earlier in this chapter, and see that for a
large part you can (but don’t have to) work similarly as with other technologies.
Step 1: The research goal,
Step 2: Data retrieval,
Step 3: Data preparation
Steps 4 & 6: Exploration and report creation
18. Introduction to NoSQL:
As you’ve read, the goal of NoSQL databases isn’t only to offer a
way to partition databases successfully over multiple nodes, but also to present
fundamentally different ways to model the data at hand to fit its structure to its
use case and not to how a relational database requires it to be modeled.
ACID: the core principal of relational Database,
CAP Theorm: the problem with DBs on many nodes,
The BASE principal of NoSQL Database,
NoSQL Database types,
19. ACID: the core principle of relational databases:
The main aspects of a traditional relational database can be
summarized by the concept ACID:
Atomicity ,
Consistency,
Isolation,
Durability.
20. CAP Theorem: the problem with DBs on many nodes
Once a database gets spread out over different servers, it’s difficult to
follow the ACID principle because of the consistency ACID promises; the CAP
Theorem points out why this becomes problematic. The CAP Theorem states that a
database can be any two of the following things but never all three:
Partition tolerant
Available,
Consistent
22. The BASE principles of NoSQL databases
RDBMS follows the ACID principles; NoSQL databases that don’t
follow ACID, such as the document stores and key-value stores, follow BASE. BASE is
a set of much softer database promises:
Basically available,
Soft State,
Eventual Consistent,
23.
24. NoSQL database types
As you saw earlier, there are four big NoSQL types: key-value store,
document store, column-oriented database, and graph database. Each type solves a
problem that can’t be solved with relational databases. Actual implementations are often
combinations of these. OrientDB, for example, is a multi-model database, combining
NoSQL types. OrientDB is a graph database where each node is a document.
Normalization,
Many to many relationship
25. Case Study: What disease is that?
Step-1: Setting the research goal,
Step-2 & 3: Data Retrieval and Preparation,
Step-4: Data Exploration,
Step-3 revisited: Data Preparation for disease
profiling,
Step-4 revisited: Data Exploration for disease
profiling,
Step-6: Presentation and Automation
26. Step 1: Setting the research goal
Steps 2 and 3: Data retrieval and preparation
Data retrieval and data preparation are two distinct steps in the data science
process, and even though this remains true for the case study, we’ll explore both in the same section.
This way you can avoid setting up local intermedia storage and immediately do data preparation while
the data is being retrieved.