This document discusses common patterns of Apache Hadoop use in enterprises. It identifies three main patterns: 1) Hadoop as a data refinery to process large amounts of data and load it into existing data systems, 2) data exploration using Hadoop to directly analyze large amounts of raw data, and 3) application enrichment where data in Hadoop is used to customize applications and user experiences. The document provides examples of each pattern across different industries.
Asterix Solution’s Hadoop Training is designed to help applications scale up from single servers to thousands of machines. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Duration - 25 hrs
Session - 2 per week
Live Case Studies - 6
Students - 16 per batch
Venue - Thane
Optimising Data Lakes for Financial ServicesAndrew Carr
By using a data lake, you can potentially do more with your company’s data than ever before.
You can gather insights by combining previously disparate data sets, optimise your operations, and build new products. However, how you design the architecture and implementation can significantly impact the results. In this white paper, we propose a number of ways to tackle such challenges and optimise the data lake to ensure it fulfils its desired function.
IIA: The Current State of Hadoop in the EnterpriseCoy Dean
The document discusses the current state of Hadoop adoption in enterprises. While interest in Hadoop's potential is growing, actual adoption rates remain modest, with most enterprises in early evaluation or piloting stages. Only around 1,000-1,500 global organizations are estimated to currently use Hadoop in production. However, commercial Hadoop vendors are experiencing healthy revenue growth, indicating broader adoption may be on the horizon. Key drivers for Hadoop adoption include its low-cost, scalable data storage and processing capabilities.
This document discusses building an enterprise data hub using MapR's Hadoop distribution. It describes how unprecedented data volumes from online transactions and clickstream data are driving up data warehousing costs. MapR enhances Hadoop to support features like high availability, disaster recovery and workload management, making it suitable for an enterprise data hub. This data hub would include a data landing zone and data refinery for cleaning, integrating and analyzing data at lower cost than data warehousing. It would also act as a long-term data store and archive to supply new insights to analytical platforms throughout the enterprise.
Influence of Hadoop in Big Data Analysis and Its Aspects IJMER
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
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.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
This document summarizes a project investigating the performance of Apache Tez compared to Hadoop MapReduce. It discusses how Tez uses directed acyclic graphs instead of MapReduce's rigid three stage process. The author modified existing Hive scripts to use Tez and tracked performance metrics to evaluate differences between the two frameworks. Future work includes investigating more errors with Tez and exploring the Presto query engine.
Asterix Solution’s Hadoop Training is designed to help applications scale up from single servers to thousands of machines. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Duration - 25 hrs
Session - 2 per week
Live Case Studies - 6
Students - 16 per batch
Venue - Thane
Optimising Data Lakes for Financial ServicesAndrew Carr
By using a data lake, you can potentially do more with your company’s data than ever before.
You can gather insights by combining previously disparate data sets, optimise your operations, and build new products. However, how you design the architecture and implementation can significantly impact the results. In this white paper, we propose a number of ways to tackle such challenges and optimise the data lake to ensure it fulfils its desired function.
IIA: The Current State of Hadoop in the EnterpriseCoy Dean
The document discusses the current state of Hadoop adoption in enterprises. While interest in Hadoop's potential is growing, actual adoption rates remain modest, with most enterprises in early evaluation or piloting stages. Only around 1,000-1,500 global organizations are estimated to currently use Hadoop in production. However, commercial Hadoop vendors are experiencing healthy revenue growth, indicating broader adoption may be on the horizon. Key drivers for Hadoop adoption include its low-cost, scalable data storage and processing capabilities.
This document discusses building an enterprise data hub using MapR's Hadoop distribution. It describes how unprecedented data volumes from online transactions and clickstream data are driving up data warehousing costs. MapR enhances Hadoop to support features like high availability, disaster recovery and workload management, making it suitable for an enterprise data hub. This data hub would include a data landing zone and data refinery for cleaning, integrating and analyzing data at lower cost than data warehousing. It would also act as a long-term data store and archive to supply new insights to analytical platforms throughout the enterprise.
Influence of Hadoop in Big Data Analysis and Its Aspects IJMER
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
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.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
This document summarizes a project investigating the performance of Apache Tez compared to Hadoop MapReduce. It discusses how Tez uses directed acyclic graphs instead of MapReduce's rigid three stage process. The author modified existing Hive scripts to use Tez and tracked performance metrics to evaluate differences between the two frameworks. Future work includes investigating more errors with Tez and exploring the Presto query engine.
This document provides an overview of the Actian DataFlow software. It discusses how Hadoop holds promise for large-scale data analytics but has limitations around performance speed, skill requirements, and incorporating other data sources. Actian DataFlow addresses these challenges by automatically optimizing workloads for high performance on Hadoop through a scale up/out architecture and pipeline/data parallelism. It also enables joining data from multiple sources and shortens analytics project timelines through its visual interface and optimization of the data preparation and analysis process.
Hadoop is an open source platform for storing and processing large amounts of data across distributed systems. The document evaluates nine major Hadoop solutions based on 32 criteria. It finds that Hadoop is becoming widely adopted in enterprises due to its ability to cost-effectively manage both structured and unstructured data at large scales. While Hadoop itself is free to use, many vendors add proprietary features and support to their commercial distributions, creating competition in the growing Hadoop market. The evaluation identifies leaders and strong performers among the solutions for meeting enterprise data and analytics needs.
Battling the disrupting Energy Markets utilizing PURE PLAY Cloud ComputingEdwin Poot
Disruption can be intimidating. You may even be losing business to one or more rising competitors. You may be wondering how you could possibly compete. Rest assured, this disruption doesn’t mean you need to turn your business upside down. But just be smart in how you engage your business using innovation without the need for huge changes, high risks or large investments.
This document discusses big data analytics techniques like Hadoop MapReduce and NoSQL databases. It begins with an introduction to big data and how the exponential growth of data presents challenges that conventional databases can't handle. It then describes Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers using a simple programming model. Key aspects of Hadoop covered include MapReduce, HDFS, and various other related projects like Pig, Hive, HBase etc. The document concludes with details about how Hadoop MapReduce works, including its master-slave architecture and how it provides fault tolerance.
Hadoop essentials by shiva achari - sample chapterShiva Achari
Sample chapter of Hadoop Ecosystem
Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem
For more information: http://bit.ly/1AeruBR
WP_Impetus_2016_Guide_to_Modernize_Your_Enterprise_Data_Warehouse_JRobertsJane Roberts
The document discusses modernizing enterprise data warehouses to handle big data by migrating workloads to a Hadoop-based data lake. It describes challenges with existing data warehouses and outlines Impetus's automated data warehouse workload migration tool which can help organizations migrate schemas, data, queries and access controls to Hadoop to realize the benefits of big data analytics while protecting existing investments.
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 discusses how big data analytics can transform the travel and transportation industry. It notes that these industries generate huge amounts of structured and unstructured data from various sources that can provide insights if analyzed properly. Hadoop is one tool that can help manage and process large datasets in parallel across clusters of servers. The document discusses how sensors in vehicles and infrastructure can provide real-time data on performance, maintenance needs, inventory levels, and more. This data, combined with analytics, can help optimize operations, improve customer experiences, predict issues, and increase efficiency across the transportation sector. It emphasizes that companies must develop data science skills and implement new technologies to fully leverage big data for strategic advantage.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
This document discusses Hortonworks and big data. It provides an overview of Hortonworks' history and role in developing Apache Hadoop. Key points include: Hortonworks was created in 2011 to focus on enterprise Hadoop and started with 24 engineers from Yahoo; Hortonworks develops, distributes and supports the only 100% open source enterprise Hadoop distribution; and Hortonworks aims to drive innovation in Apache Hadoop projects and enable ecosystem interoperability.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
This document provides a playbook for how Hadoop can support and extend an enterprise data warehouse (EDW) ecosystem. It outlines six common "plays" including using Hadoop to stage structured data, process structured and unstructured data, archive all data, and access data via both the EDW and Hadoop. The plays demonstrate how Hadoop can handle growing volumes of data more cost effectively than solely relying on the EDW. Specifically, Hadoop can be used to load, transform, and analyze structured, unstructured, and archived data, as well as offload processing tasks from the EDW.
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesJyrki Määttä
This document provides an overview of how Hadoop can be used to support and extend existing enterprise data warehouse (EDW) systems. It describes six common "plays" or ways that Hadoop interacts with the EDW. The first play is to use Hadoop as a data staging platform to load and transform structured data from applications into the EDW more quickly and at lower cost than using the EDW alone. This allows the EDW resources to focus on analysis while Hadoop handles the processing and storage of large amounts of source data.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
This document discusses combining Hadoop with big data analytics. It begins by exploring how Hadoop has become a popular framework for handling big data challenges. It then discusses some of the key skills needed for successful big data analytics programs, including technical skills with tools like Hadoop as well as business knowledge. Specifically, it recommends including business analysts, BI developers, predictive model builders, data architects, data integration developers, and technology architects on any big data analytics team.
Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
The book sold on:
http://www.amazon.co.uk/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.com/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.in/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
This document contains contact and background information for Nada Zahreddine, an architect from Lebanon. It includes her education history, skills, experience, and representative projects. She has over 15 years of experience working on a variety of project types in Lebanon and other countries. Her experience includes work on residential, commercial, and mixed-use buildings, as well as master planning projects.
Workshop serves to educate individuals about the importance of preparing a last will and testament; protecting their loved ones and resources available to assist with estate planning.
Claudia Jane Akker is currently enrolled in a Bachelor of Social Work program at the University of South Australia, where she has maintained a high GPA of 4.84. She has completed courses each year of the program from 2013 to 2016, obtaining grades ranging from Pass to Distinction. Her academic record shows consistent strong performance across core social work and related subjects.
This document provides an overview of the Actian DataFlow software. It discusses how Hadoop holds promise for large-scale data analytics but has limitations around performance speed, skill requirements, and incorporating other data sources. Actian DataFlow addresses these challenges by automatically optimizing workloads for high performance on Hadoop through a scale up/out architecture and pipeline/data parallelism. It also enables joining data from multiple sources and shortens analytics project timelines through its visual interface and optimization of the data preparation and analysis process.
Hadoop is an open source platform for storing and processing large amounts of data across distributed systems. The document evaluates nine major Hadoop solutions based on 32 criteria. It finds that Hadoop is becoming widely adopted in enterprises due to its ability to cost-effectively manage both structured and unstructured data at large scales. While Hadoop itself is free to use, many vendors add proprietary features and support to their commercial distributions, creating competition in the growing Hadoop market. The evaluation identifies leaders and strong performers among the solutions for meeting enterprise data and analytics needs.
Battling the disrupting Energy Markets utilizing PURE PLAY Cloud ComputingEdwin Poot
Disruption can be intimidating. You may even be losing business to one or more rising competitors. You may be wondering how you could possibly compete. Rest assured, this disruption doesn’t mean you need to turn your business upside down. But just be smart in how you engage your business using innovation without the need for huge changes, high risks or large investments.
This document discusses big data analytics techniques like Hadoop MapReduce and NoSQL databases. It begins with an introduction to big data and how the exponential growth of data presents challenges that conventional databases can't handle. It then describes Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers using a simple programming model. Key aspects of Hadoop covered include MapReduce, HDFS, and various other related projects like Pig, Hive, HBase etc. The document concludes with details about how Hadoop MapReduce works, including its master-slave architecture and how it provides fault tolerance.
Hadoop essentials by shiva achari - sample chapterShiva Achari
Sample chapter of Hadoop Ecosystem
Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem
For more information: http://bit.ly/1AeruBR
WP_Impetus_2016_Guide_to_Modernize_Your_Enterprise_Data_Warehouse_JRobertsJane Roberts
The document discusses modernizing enterprise data warehouses to handle big data by migrating workloads to a Hadoop-based data lake. It describes challenges with existing data warehouses and outlines Impetus's automated data warehouse workload migration tool which can help organizations migrate schemas, data, queries and access controls to Hadoop to realize the benefits of big data analytics while protecting existing investments.
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 discusses how big data analytics can transform the travel and transportation industry. It notes that these industries generate huge amounts of structured and unstructured data from various sources that can provide insights if analyzed properly. Hadoop is one tool that can help manage and process large datasets in parallel across clusters of servers. The document discusses how sensors in vehicles and infrastructure can provide real-time data on performance, maintenance needs, inventory levels, and more. This data, combined with analytics, can help optimize operations, improve customer experiences, predict issues, and increase efficiency across the transportation sector. It emphasizes that companies must develop data science skills and implement new technologies to fully leverage big data for strategic advantage.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
This document discusses Hortonworks and big data. It provides an overview of Hortonworks' history and role in developing Apache Hadoop. Key points include: Hortonworks was created in 2011 to focus on enterprise Hadoop and started with 24 engineers from Yahoo; Hortonworks develops, distributes and supports the only 100% open source enterprise Hadoop distribution; and Hortonworks aims to drive innovation in Apache Hadoop projects and enable ecosystem interoperability.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
This document provides a playbook for how Hadoop can support and extend an enterprise data warehouse (EDW) ecosystem. It outlines six common "plays" including using Hadoop to stage structured data, process structured and unstructured data, archive all data, and access data via both the EDW and Hadoop. The plays demonstrate how Hadoop can handle growing volumes of data more cost effectively than solely relying on the EDW. Specifically, Hadoop can be used to load, transform, and analyze structured, unstructured, and archived data, as well as offload processing tasks from the EDW.
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesJyrki Määttä
This document provides an overview of how Hadoop can be used to support and extend existing enterprise data warehouse (EDW) systems. It describes six common "plays" or ways that Hadoop interacts with the EDW. The first play is to use Hadoop as a data staging platform to load and transform structured data from applications into the EDW more quickly and at lower cost than using the EDW alone. This allows the EDW resources to focus on analysis while Hadoop handles the processing and storage of large amounts of source data.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
This document discusses combining Hadoop with big data analytics. It begins by exploring how Hadoop has become a popular framework for handling big data challenges. It then discusses some of the key skills needed for successful big data analytics programs, including technical skills with tools like Hadoop as well as business knowledge. Specifically, it recommends including business analysts, BI developers, predictive model builders, data architects, data integration developers, and technology architects on any big data analytics team.
Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
The book sold on:
http://www.amazon.co.uk/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.com/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.in/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
This document contains contact and background information for Nada Zahreddine, an architect from Lebanon. It includes her education history, skills, experience, and representative projects. She has over 15 years of experience working on a variety of project types in Lebanon and other countries. Her experience includes work on residential, commercial, and mixed-use buildings, as well as master planning projects.
Workshop serves to educate individuals about the importance of preparing a last will and testament; protecting their loved ones and resources available to assist with estate planning.
Claudia Jane Akker is currently enrolled in a Bachelor of Social Work program at the University of South Australia, where she has maintained a high GPA of 4.84. She has completed courses each year of the program from 2013 to 2016, obtaining grades ranging from Pass to Distinction. Her academic record shows consistent strong performance across core social work and related subjects.
This document contains contact and background information for Nada Zahreddine, an architect from Lebanon. It includes her education history, skills, experience, and representative projects. She has over 15 years of experience working on a variety of project types in Lebanon and other countries. Her experience includes work on residential, commercial, and mixed-use buildings, as well as master planning projects. Representative projects listed include high-rise residential and commercial buildings in Lebanon, resort developments in Morocco, and projects in Syria, UAE, Saudi Arabia, and Algeria.
This document contains contact and background information for Nada Zahreddine, an architect from Lebanon. It includes her education history, skills, experience, and representative projects. She has over 15 years of experience working on a variety of project types in Lebanon and other countries. Her experience includes work on residential, commercial, and mixed-use buildings, as well as master planning projects.
This document summarizes the key details of an international services provider company:
- Founded in 1993, it now operates in 24 countries across 4 continents, generating over $700 million in annual revenue while serving millions of customers.
- The company offers telecommunications, energy, security, and other services to both residential and business customers.
- It utilizes a direct selling model where independent business owners acquire new customers and build a team to earn residual income from services. Extensive training and support is provided to business owners.
This resume is for Anjan Banerjee, who currently works as the L&D/OD Manager (Global) for Tech Mahindra Business Services Group, based in India. He has over 17 years of work experience, including 9 years in learning and development. Some of his responsibilities include leading an global L&D team, developing training programs, designing course content, and conducting organizational development initiatives. He has received several awards and recognitions for his work in areas such as leadership development, process transition, and automation projects.
A.Karthikeyan is a software developer with over 4 years of experience seeking a challenging role. He has experience developing projects using technologies like AngularJS, PHP, MySQL, and Microsoft SQL Server. Some of the projects he has worked on include a purchase application for Isha Foundation using AngularJS, a fundraising application for Isha Foundation using SuiteCRM, and a resident database system for Isha Foundation using Yii framework. He holds an M.Sc. in Information Technology and a B.Sc. in Physics.
Deanna L. Smith has over 15 years of experience in retail sales, customer service, and administrative roles. She has a Associate of Fine Arts degree and skills in areas such as sales, customer service, event planning, and Microsoft Office. Her professional experience includes roles as a Customer Service Specialist, Sales Leader, and Assistant Store Manager at various retailers, where she assisted customers, managed store operations, and helped reach sales goals. She is currently looking for new opportunities to continue developing her career.
This document discusses how Apache Hadoop provides a solution for enterprises facing challenges from the massive growth of data. It describes how Hadoop can integrate with existing enterprise data systems like data warehouses to form a modern data architecture. Specifically, Hadoop provides lower costs for data storage, optimization of data warehouse workloads by offloading ETL tasks, and new opportunities for analytics through schema-on-read and multi-use data processing. The document outlines the core capabilities of Hadoop and how it has expanded to meet enterprise requirements for data management, access, governance, integration and security.
The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
This document discusses big data business opportunities and solutions. It notes that big data solutions are tailored to specific data types and workloads. Common business domains for big data include web analytics, clickstream analysis using the ELK stack, and big data in the cloud to provide auto-scaling, low costs, and use of cloud services. Effective big data solutions require data governance, cluster modeling, and analytics and visualization.
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
2Running Head BIG DATA PROCESSING OF SOFTWARE AND TOOLS2BIG.docxlorainedeserre
2
Running Head: BIG DATA PROCESSING OF SOFTWARE AND TOOLS
2
BIG DATA PROCESSING OF SOFTWARE AND TOOLS
University of the Cumberlands
Big Data Processing of Software and Tools
Data Science & Big Data Analytics
ITS 836-21 Group-1
Prof: Gamini Bulumulle
Date submitted: 02/23/2020
Submitted By:
Table of contents
Abstract.......................................................................................................................3
Executive summary.....................................................................................................4
Big data analytics software.........................................................................................6
· Apache Hadoop.........................................................................................6
· CDH…………………………..................................................................7
· Casandra....................................................................................................7
· Knime........................................................................................................7
· Datawrapper..............................................................................................8
· MongoDB..................................................................................................8
· Lumify.......................................................................................................9
· HPCC.........................................................................................................9
· Storm........................................................................................................10
· Apache SAMOA......................................................................................10
· Talend.......................................................................................................10
· RapidMiner...............................................................................................11
Analyzing the data sets using R language…………………………………………..12
Conclusion..................................................................................................................12
References...................................................................................................................14
Abstract
The concept of big data analytics has been used over the years and most companies have embraced the idea, to harness data that is being used in their day to day company routines. Companies can apply analytics and receive huge benefits from it, back in the 1950s, companies were using big data in in terms of spreadsheet analysis. This was a crude form of big data analytics used to reveal small bits of data and data patterns. Nowadays companies use big data analytics software to handle huge chunks of data because it has a variety of benefits to businesses. Some of the advantages of big data analytics include: the speed in handling data, effic ...
2Running Head BIG DATA PROCESSING OF SOFTWARE AND TOOLS2BIG.docxBHANU281672
This document discusses and compares various big data analytics software and tools. It begins with an abstract describing how companies now use big data analytics software to handle large amounts of data. The document then provides an executive summary of a research study analyzing how over 50 companies use big data analytics. The main body compares the features and benefits of various popular big data analytics software, including Apache Hadoop, CDH, Cassandra, Knime, Datawrapper, MongoDB, Lumify, HPCC, Storm, SAMOA, Talend, and RapidMiner. It also discusses analyzing data sets using the R programming language. The conclusion emphasizes how big data tools help transform large amounts of raw data into useful analytics and insights.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
Big data: Descoberta de conhecimento em ambientes de big data e computação na...Rio Info
This document discusses big data and intensive data processing. It defines big data and compares it to traditional analytics. It discusses technologies used for big data like Hadoop, MapReduce, and machine learning. It also discusses frameworks for analyzing big data like Apache Mahout and how Mahout is moving away from MapReduce to platforms like Apache Spark.
Big data refers to large volumes of structured and unstructured data that are difficult to process using traditional database and software techniques. It encompasses the 3Vs - volume, velocity, and variety. Hadoop is an open-source framework that stores and processes big data across clusters of commodity servers using the MapReduce algorithm. It allows applications to work with huge amounts of data in parallel. Organizations use big data and analytics to gain insights for reducing costs, optimizing offerings, and making smarter decisions across industries like banking, government, and education.
This document provides an overview of Hadoop and big data use cases. It discusses the evolution of business analytics and data processing, as well as the architecture of traditional RDBMS systems compared to Hadoop. Examples of how companies have used Hadoop include a bank improving risk modeling by combining customer data, a telecom reducing churn by analyzing call logs, and a retailer targeting promotions by analyzing point-of-sale transactions. Hadoop allows these companies to gain valuable business insights from large and diverse data sources.
Learn About Big Data and Hadoop The Most Significant ResourceAssignment Help
Data is now one of the most significant resources for businesses all around the world because of the digital revolution. However, the ability to gather, organize, process, and evaluate huge volumes of data has altered the way businesses function and arrive at educated decisions. Managing and gleaning information from the ever-expanding marine environments of information is impossible without Big Data and Hadoop. Both of which are at the vanguard of this data revolution.
If you have selected a programming language, and have difficulties writing the best assignment, get the assistance of assessment help experts to learn more about it. In this blog, we will look at the basics of Big Data and Hadoop and how they work. However, we will also explore the nature of Big Data. Also, its defining features, and the difficulties it provides. We'll also take a look at how Hadoop, an open-source platform, has become a frontrunner in the race to solve the challenges posed by Big Data. These fully appreciate the potential for change of Big Data and Hadoop for businesses across a wide range of sectors. It is necessary first to grasp the central position that they play in current data-driven decision-making.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for 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.
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.Jennifer Walker
The document discusses how Hadoop is often used primarily as a data storage system rather than an agile analytics platform. It argues that for Hadoop to enable productive analytics, companies need to transform Hadoop into a system that allows for iterative exploration of diverse data sources through intuitive interfaces that leverage machine learning. This requires addressing challenges such as a lack of data understanding, scarce expertise, and time-consuming data preparation processes. Adopting platforms that provide self-service access and leverage business context can help democratize data access and analysis.
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 provides an overview of HDInsight and Hadoop. It defines big data and Hadoop, describing HDInsight as Microsoft's implementation of Hadoop in the cloud. It outlines the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig and Sqoop. It discusses advantages of using HDInsight in the cloud and provides information on working with HDInsight clusters, loading and querying data, and different approaches to big data solutions.
This document provides an introduction to big data and Hadoop. It defines big data as large, complex datasets that are difficult to manage and analyze using traditional methods. Hadoop is an open-source software framework used to store and process big data across distributed systems. It includes components like HDFS for scalable storage, MapReduce for parallel processing, Hive for data summarization, and Pig for creating MapReduce programs. The document discusses how Hadoop offers advantages like scalability, ease of use, cost-effectiveness and flexibility for big data processing. It provides examples of Hadoop's real-world use in healthcare, finance, retail and social media. The future of big data and Hadoop is also examined.
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