Big Data refers to a large amount of data both structured and unstructured. For managing and analyzing this amount of data we need technologies like Hadoop and language like R.
http://www.techsparks.co.in/thesis-in-big-data-with-r/
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
This document discusses in-memory analytics and compares it to traditional disk-based databases. In-memory analytics stores all data in RAM rather than on disk storage, allowing for much faster data access and analytics. Key advantages of in-memory systems include speeds 50-100 times faster than disk-based databases and the ability to perform real-time analytics. The document outlines optimization aspects for in-memory data management like data layout, parallelism, and fault tolerance. It concludes with some common questions around in-memory analytics regarding adoption, performance, skills needs, and data size.
This presentation provides an overview of big data open source technologies. It defines big data as large amounts of data from various sources in different formats that traditional databases cannot handle. It discusses that big data technologies are needed to analyze and extract information from extremely large and complex data sets. The top technologies are divided into data storage, analytics, mining and visualization. Several prominent open source technologies are described for each category, including Apache Hadoop, Cassandra, MongoDB, Apache Spark, Presto and ElasticSearch. The presentation provides details on what each technology is used for and its history.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
This document discusses in-memory analytics and compares it to traditional disk-based databases. In-memory analytics stores all data in RAM rather than on disk storage, allowing for much faster data access and analytics. Key advantages of in-memory systems include speeds 50-100 times faster than disk-based databases and the ability to perform real-time analytics. The document outlines optimization aspects for in-memory data management like data layout, parallelism, and fault tolerance. It concludes with some common questions around in-memory analytics regarding adoption, performance, skills needs, and data size.
This presentation provides an overview of big data open source technologies. It defines big data as large amounts of data from various sources in different formats that traditional databases cannot handle. It discusses that big data technologies are needed to analyze and extract information from extremely large and complex data sets. The top technologies are divided into data storage, analytics, mining and visualization. Several prominent open source technologies are described for each category, including Apache Hadoop, Cassandra, MongoDB, Apache Spark, Presto and ElasticSearch. The presentation provides details on what each technology is used for and its history.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
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.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Introduction To Hadoop | What Is Hadoop And Big Data | Hadoop Tutorial For Be...Simplilearn
This presentation about Hadoop will help you learn the basics of Hadoop and its components. First, you will see what is Big Data and the significant challenges in it. Then, you will understand how Hadoop solved those challenges. You will have a glance at the History of Hadoop, what is Hadoop, the different companies using Hadoop, the applications of Hadoop in different companies, etc. Finally, you will learn the three essential components of Hadoop – HDFS, MapReduce, and YARN, along with their architecture. Now, let us get started with Introduction to Hadoop.
Below topics are explained in this Hadoop presentation:
1. Big Data and its challenges
2. Hadoop as a solution
3. History of Hadoop
4. What is Hadoop
5. Applications of Hadoop
6. Components of Hadoop
7. Hadoop Distributed File System
8. Hadoop MapReduce
9. Hadoop YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/introduction-to-big-data-and-hadoop-certification-training.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
Learn the built-in mathematical functions in R. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document contains information about Apache HBase including links to documentation pages, JIRA issues, and discussions on using HBase. It provides configuration examples for viewing HFile contents, explains how Bloom filters are used in HBase, includes an overview of the HBase data model and comparisons with RDBMS. It also shows an example Git diff of modifying the HBase heap size configuration and provides links to guides on using HBase and documentation on region splitting and merging.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
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Twitter: http://bit.ly/2gFPTi8
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
This document discusses the history and evolution of supercomputer architectures from the 1960s to present. Early supercomputers relied on compact designs and local parallelism. Starting in the 1990s, massively parallel systems with thousands of processors became common. Modern supercomputers can use over 100,000 processors connected by fast interconnects and may utilize GPUs, computer clusters, or distributed computing networks to achieve petaflop-scale performance. Vector processing is also discussed as an important technique used in many historical supercomputers to improve performance.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
1. RAID (Redundant Array of Independent Disks) is a data storage virtualization technology that combines multiple physical disk drive components into one or more logical units for the purposes of data redundancy, performance improvement, or both.
2. There are different RAID levels that provide redundancy through techniques like mirroring, parity, or a combination of both. The most common levels are RAID 0, 1, 5 and 10 but there are also less common levels like RAID 2-4 and 6.
3. The presenter discusses the advantages and disadvantages of various RAID levels for improving performance, reliability, and fault tolerance of disk storage systems. RAID can help address issues like increasing storage capacity
Big data solutions are enabling healthcare providers to transform into more patient-centered, collaborative care models driven by analytics. As basic needs are met and advanced applications emerge, new use cases will arise from sources like wearable devices and sensors. Predictive analytics using big data can help fill gaps by predicting things like missed appointments, noncompliance, and patient trajectories in order to proactively manage care. However, barriers to using big data include a lack of expertise and the fact that big data has a different structure and is more unstructured than traditional databases.
This document discusses various page replacement algorithms used in operating systems. It begins with definitions of paging and page replacement in virtual memory systems. There are then overviews of 12 different page replacement algorithms including FIFO, optimal, LRU, NRU, NFU, second chance, clock, and random. The goal of page replacement algorithms is to minimize page faults. The document provides examples and analyses of how each algorithm approaches replacing pages in memory.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
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.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Introduction To Hadoop | What Is Hadoop And Big Data | Hadoop Tutorial For Be...Simplilearn
This presentation about Hadoop will help you learn the basics of Hadoop and its components. First, you will see what is Big Data and the significant challenges in it. Then, you will understand how Hadoop solved those challenges. You will have a glance at the History of Hadoop, what is Hadoop, the different companies using Hadoop, the applications of Hadoop in different companies, etc. Finally, you will learn the three essential components of Hadoop – HDFS, MapReduce, and YARN, along with their architecture. Now, let us get started with Introduction to Hadoop.
Below topics are explained in this Hadoop presentation:
1. Big Data and its challenges
2. Hadoop as a solution
3. History of Hadoop
4. What is Hadoop
5. Applications of Hadoop
6. Components of Hadoop
7. Hadoop Distributed File System
8. Hadoop MapReduce
9. Hadoop YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/introduction-to-big-data-and-hadoop-certification-training.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
Learn the built-in mathematical functions in R. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document contains information about Apache HBase including links to documentation pages, JIRA issues, and discussions on using HBase. It provides configuration examples for viewing HFile contents, explains how Bloom filters are used in HBase, includes an overview of the HBase data model and comparisons with RDBMS. It also shows an example Git diff of modifying the HBase heap size configuration and provides links to guides on using HBase and documentation on region splitting and merging.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
This document discusses the history and evolution of supercomputer architectures from the 1960s to present. Early supercomputers relied on compact designs and local parallelism. Starting in the 1990s, massively parallel systems with thousands of processors became common. Modern supercomputers can use over 100,000 processors connected by fast interconnects and may utilize GPUs, computer clusters, or distributed computing networks to achieve petaflop-scale performance. Vector processing is also discussed as an important technique used in many historical supercomputers to improve performance.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
1. RAID (Redundant Array of Independent Disks) is a data storage virtualization technology that combines multiple physical disk drive components into one or more logical units for the purposes of data redundancy, performance improvement, or both.
2. There are different RAID levels that provide redundancy through techniques like mirroring, parity, or a combination of both. The most common levels are RAID 0, 1, 5 and 10 but there are also less common levels like RAID 2-4 and 6.
3. The presenter discusses the advantages and disadvantages of various RAID levels for improving performance, reliability, and fault tolerance of disk storage systems. RAID can help address issues like increasing storage capacity
Big data solutions are enabling healthcare providers to transform into more patient-centered, collaborative care models driven by analytics. As basic needs are met and advanced applications emerge, new use cases will arise from sources like wearable devices and sensors. Predictive analytics using big data can help fill gaps by predicting things like missed appointments, noncompliance, and patient trajectories in order to proactively manage care. However, barriers to using big data include a lack of expertise and the fact that big data has a different structure and is more unstructured than traditional databases.
This document discusses various page replacement algorithms used in operating systems. It begins with definitions of paging and page replacement in virtual memory systems. There are then overviews of 12 different page replacement algorithms including FIFO, optimal, LRU, NRU, NFU, second chance, clock, and random. The goal of page replacement algorithms is to minimize page faults. The document provides examples and analyses of how each algorithm approaches replacing pages in memory.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
R as supporting tool for analytics and simulationAlvaro Gil
R is a popular open-source language and environment for statistical analysis and visualization. It allows users to perform a wide range of statistical and predictive modeling techniques on data. Many companies use R as their standard tool for analytics due to its extensive library of packages and ability to handle large datasets. R can interface with other languages and platforms, making it a versatile scripting language for data science tasks.
R is a programming language and environment for statistical analysis and graphics. It provides tools for data analysis, visualization, and machine learning. Some key features include statistical functions, graphics, probability distributions, data analysis tools, and the ability to access over 10,000 add-on packages. R can be used across platforms like Windows, Linux, and macOS. It is widely used for complex data analysis in data science and research.
This document discusses the rise of open source analytics tools and languages. It notes that SAS and SPSS previously dominated the market but were very expensive. R, Python, and Hadoop have provided lower-cost open source alternatives for data storage, querying, visualization, and statistical analysis. The document reviews popular open source tools like R, Python, RapidMiner, and Hadoop ecosystems. It also discusses commercial offerings that build on open source like Revolution Analytics. Overall, open source has helped reduce the costs of analytics software and enabled more organizations to benefit from data-driven insights.
Integrating R & Hadoop - Text Mining & Sentiment AnalysisAravind Babu
The document discusses integrating R and Hadoop for big data analytics. It notes that existing statistical applications like R are incapable of handling big data, while data management tools lack analytical capabilities. Integrating R with Hadoop bridges this gap by leveraging R's analytics and statistics functionality with Hadoop's ability to process and store distributed data. RHadoop is introduced as an open source project that allows R programmers to directly use MapReduce functionality in R code. Specific RHadoop packages like rhdfs and rmr2 are described that enable interacting with HDFS and performing statistical analysis via MapReduce on Hadoop clusters. Text analytics use cases with R and Hadoop like sentiment analysis are also briefly outlined.
This document provides an agenda for a presentation on big data and big data analytics using R. The presentation introduces the presenter and has sections on defining big data, discussing tools for storing and analyzing big data in R like HDFS and MongoDB, and presenting case studies analyzing social network and customer data using R and Hadoop. The presentation also covers challenges of big data analytics, existing case studies using tools like SAP Hana and Revolution Analytics, and concerns around privacy with large-scale data analysis.
This document summarizes a research paper on analyzing and visualizing Twitter data using the R programming language with Hadoop. The goal was to leverage Hadoop's distributed processing capabilities to support analytical functions in R. Twitter data was analyzed and visualized in a distributed manner using R packages that connect to Hadoop. This allowed large-scale Twitter data analysis and visualizations to be built as a R Shiny application on top of results from Hadoop.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
This document compares Python and R for use in data science. Both languages are popular among data scientists, though Python has broader usage among professional developers overall. Python is a general purpose language while R is specialized for statistical computing. Both have extensive libraries for data manipulation, analysis, and visualization. The best choice depends on factors like familiarity, project requirements, and team preferences as both are capable of most data science tasks.
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
This document discusses big data and use cases. It begins by reviewing the history and evolution of big data and advanced analytics. It then explains how technologies like Hadoop, stream processing, and in-memory computing support big data solutions. The document presents two use cases - analyzing credit risk by examining customer transaction data to improve credit offers, and detecting fraud by analyzing financial transactions for unusual patterns that could indicate suspicious activity. It describes how these use cases leverage technologies like Oracle R Connector for Hadoop to run analytics and machine learning algorithms on large datasets.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
This document discusses how Microsoft R Server accelerates predictive analytics by enabling R users to leverage the performance of Apache Spark on Hadoop clusters without having to learn Spark or Hadoop. It summarizes that Microsoft R Server for Hadoop allows R users to conduct data exploration, transformation, modeling and other analytics on large datasets in Hadoop using R, while Microsoft R Server handles the parallelization of R scripts across Spark to provide significantly faster performance compared to traditional Hadoop approaches.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
Available Research Topics in Machine LearningTechsparks
Due to the continuous the development in IT sector, research students have good chance in preparing their research papers in the field of the computer science. Although there are many subject areas that students opt for preparing their research papers, the most leading one is machine learning. What is the Machine Learning and why it is a leading subject area? Machine learning is an approach to analyzing the data. It is the applicable to automate construction of an analytical system. Considered one of the best sub-fields of artificial intelligence, machine learning allows systems to gain knowledge from the given data, recognize the patterns, and act accordingly without any human interference. Basically, machines are trained on how to learn and recognize various patterns in a given dataset, hence its name-'machine learning'. Both-small and big companies are using set of rules to develop models for getting better at the decision-making process without any human interference.
The document provides tips for completing a thesis fast, including studying submission guidelines, creating a realistic timeline and schedule, keeping the topic clear with specific details, finding support from a study group in addition to an advisor, and getting an initial draft written to seek feedback rather than pursuing perfection which takes more time.
The document provides guidelines for formatting a research paper to publish in IEEE format. This style is commonly used in technical fields like computer science. Key requirements include formatting the paper in a two-column layout with the title centered at the top in 24-point type. The abstract should be a single paragraph of 200 words that precisely summarizes the paper's contents. Sections and subsections can increase readability, and elements like equations, figures and tables should be numbered separately but centered in their columns.
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This document provides an overview of key topics in software engineering including the software development life cycle (SDLC), common software development models, software testing, the unified modeling language (UML), software maintenance, and case tools. It also outlines potential thesis, research, and project topics such as data modeling, UML, SDLC methodologies, software quality, and software project management. The document introduces software engineering principles and describes why software engineering practices are required to manage large, complex software projects and products.
Cloud computing and Cloud Security - Basics and TerminologiesTechsparks
Cloud Computing is a new trending field these days and is an Internet-based service. It is based on the concept of virtualization.
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How to write a thesis - Guidelines to Thesis WritingTechsparks
A thesis is an important part of the academics of the master's students. Without the submission of the thesis, a degree is not conferred to a student. Follow the slides to know the procedure of thesis writing.
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Matlab is programming language developed by MathWorks that provides a computing environment for programming.
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Digital Communication simply means devices communicating with each other in through digital signals. The signals are digitized and then the information is transferred through these digitized signals from source to destination.
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How to get published in Scopus/ IEEE journalsTechsparks
The document provides guidance on publishing research theses in scholarly journals. It discusses reasons for disseminating research, choosing the right journal, preparing articles for submission, and the editorial review process. The key steps are evaluating a journal's reputation and scope, structuring the article appropriately, and addressing any revisions requested during peer review to improve the work for resubmission or publication in another journal if rejected. Getting published involves persistence in responding constructively to reviewer feedback.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Climate Impact of Software Testing at Nordic Testing Days
Big Data - Analytics with R
1. Big Data with R
Big Data refers to the large volume of data which may be organized or unorganized. This big
data is very essential for large organizations and businesses for valuable insights to determine
futuristic trends. Big Data is defined in terms of 3Vs which are as follows:
Volume – Volume refers to the quantity and amount of data and this data is increasing day by
day. Facebook has more number of users than the entire population of China. Its data is also
huge. The data is in the form of images, music, videos and all such stuff.
Velocity – Velocity refers to the rate at which the data is generated. Again taking the example of
Facebook, a huge amount of data is uploaded, shared each second on Facebook. People on social
media want new information and content each time they log in to social media. Old obsolete
news and information does not matter to them. Thus new information is shared at every second
on social media.
Variety – Coming to the third V of Big Data i.e. variety. Variety means diverse type of data.
There are multiple formats of data that can be stored. The data can be in the form of image,
video, text, pdf or excel. Big Data has a big challenge of managing this different type of data. An
organization need to arrange similar format of data together in order to extract important
information out of that.
Why is Big Data Analytics important?
Big Data and its analytics are important on account of the following reasons:
Reduction in Cost – Big Data Analytics offer cost advantages using technologies like
Hadoop and Cloud Computing. These technologies help in storing and managing large
amount of data.
Better Decision making – Using Hadoop and analytics, organizations and businesses are
able to make better and faster decisions by analyzing different sources of data.
2. New services and product development – With the help of big data analytics,
companies can measure customer behavior and needs. Using these parameters, companies
can launch new products and services that will satisfy user needs.
R Programming Language
R is an open-source programming language and software environment for statistical study,
graphical representation, and reporting. The R language is extensively used by statisticians and
data miners for data analysis and software statistics. Robert Gentleman and Ross Ihaka are the
two authors of this language. The language is named ‘R’ from the first letter of the name of these
authors.
R software environment’s source code is written mainly in C, FORTRAN, and R language. R is
a GNU Package and is freely available under GNU General Public License.
What is GNU?
GNU is an acronym for “GNU’s Not Unix!” It is an operating system and is a collection of
computer software. Its design is like UNIX but it differs from UNIX in the sense that it is a free
software and do not contain any UNIX code in it.
Features of R
R programming language has the following main features:
It is a simple and well-defined programming language that includes conditions, loops,
and recursive functions.
It has data handling and data storage facility.
It provides operators for array, matrices and vector calculations.
It provides integrated set of tools for data analysis.
It also provides static graphics to produce dynamic and interactive graphs.
3. Basic Syntax of R
For working with R, you first need to set up the environment for R. After the R environment is
set, you are ready to work with R command prompt. To start the R command prompt, type the
following command:
$ R
R interpreter will be launched where you will type your program with prompt > as follows:
Mystring <- “Hello World!”
Print(Mystring)
[1] “Hello World!”
R Script File
The programs are written in script files and then executed at command prompt using R
interpreter called Rscript.
In R language, variables are assigned R-Objects which are as follows:
Vectors
Lists
Matrices
Arrays
Factors
Data Frames
4. Working with Big Data in R
R language has been there for the last 20 years but it gained attention recently due to its capacity
to handle Big Data. R language provides series of packages and an environment for statistical
computation of Big Data. The project of programming with Big Data in R was developed a few
years ago. This project is mainly used for data profiling and distributed computing. R packages
and functions are available to load data from any source.
Hadoop is a Big Data technology to handle large amount of data. R and Hadoop can be
integrated together for Big Data analytics.
Why integrate R with Hadoop?
R is a very good programming language for statistical data analysis and to convert this data
analysis to interactive graphs. Although R is preferred programming language for statistics and
analysis, there are some drawbacks of this language also. In R programming language, a single
machine contains all the objects in the main memory. Large size of data cannot be loaded into
the RAM memory. Also, R is not scalable and this cause only limited amount of data to be
processed at a time. For this case, Hadoop is the perfect choice.
Hadoop is a distributed processing framework to perform operations and handle large datasets.
Hadoop already is a popular framework for Big Data processing and integrating it with R will
work wonders. This will make data analytics highly scalable such that the analytics platform can
be scaled up and scaled down depending upon the datasets. It will also provide cost value return.
How to integrate R with Hadoop?
R packages and R scripts are used by data scientists for data processing. These R packages and R
scripts need to be rewritten in Java language or any such programming language that implements
Hadoop MapReduce algorithm to use these scripts and packages with Hadoop. A software
written in R language is required with data stored on distributed storage Hadoop. Following are
some of the methods to integrate R with Hadoop:
1. RHADOOP – It is the most commonly used solution to integrate R with Hadoop. This
analytics solution allows user to directly take data from HBase database systems and
5. HDFS file systems. It also offers the advantage of simplicity and cost. It is a collection of
5 packages to manage and analyze data using programming language R. Following are
the 5 packages:
Rhbase – This provides database management functions for HBase within R.
Rhdfs – This package provides connectivity to Hadoop distributed file system.
Plyrmr – This package provides data manipulation operations on large datasets.
Ravro – This allows users to read and write Avro files from HDFS.
Rmr2 – This is used to perform statistical analysis on data stored in Hadoop.
2. RHIPE – It is an acronym for R and Hadoop Integrated Programming Environment. It is
an R library that provides users the ability to MapReduce within R. It provides data
distribution scheme and integrates well with Hadoop.
3. R and Hadoop Streaming – Hadoop Streaming makes it possible for the user to run
MapReduce using any executable script. This script reads data from standard input and
writes data as a mapper or reducer. Hadoop Streaming can be integrated with R
programming scripts.
4. RHIVE – It is based on installing R on workstations and connecting to data in Hadoop.
RHIVE is the package to launch Hive queries. It has functions to retrieve metadata from
Apache Hive like database names, column names, and table names. RHIVE also provides
libraries and algorithms in R to the data stored in Hadoop. The main advantage of this is
parallelizing of operations.
5. ORCH – It is an acronym for Oracle Connector for Hadoop. It allows users to test
MapReduce program’s ability without any need of learning a new programming
language.
Considering all this, combination of R and Hadoop is a must to work with Big Data for faster,
better, and predictive analytics along with performance, scalability and flexibility.
Strategies of Big Data in R
Big Data can be tackled with R with the following strategies:
6. Sampling – The size of data can be reduced using sampling if it is too big to be analyzed.
Sampling also decreases the performance in some cases.
Bigger Hardware – R keeps all the objects in a single memory. Problem occurs if the
data is very large. To resolve this issue, machine’s memory can be increased and Big
Data can be handled easily.
Storing objects on hard drive – Instead of storing data in memory, data objects can be
stored on hard disc using packages that are available. This data can be analyzed block
wise which leads to parallelization. This can be performed with only those algorithms
that are specifically designed for this purpose. ‘FF’ and ‘ffbase’ are the main packages
for this purpose.
Integration of high performing programming languages – For better performance,
high performing programming languages can be integrated with R. Small components of
the program are transferred from R language to another language to prevent any risks. In
order to implement this strategy, developers need to be efficient in some other
programming language like Java and C++.
Alternative Interpreters – To deal with Big Data, alternative interpreters can be used.
One such interpreter is pqR(pretty quick R). Another alternative is the Renjin which can
run on the JVM(Java Virtual Machine).