This document discusses challenges and opportunities for companies to gain competitive advantage through leveraging big data and data analytics. It notes that (1) enterprises can gain operational advantages by leveraging social, local and mobile technologies to generate insights from individual data, (2) commonly used information architectures do not effectively support collaboration and sharing of all types of information across networks, and (3) companies must address both collaboration/communication and making sense of vast information streams. The document then provides statistics on growth of digital data and challenges of analyzing unstructured data to reveal relevant insights.
Putting Business Intelligence to Work on Hadoop Data StoresDATAVERSITY
An inexpensive way of storing large volumes of data, Hadoop is also scalable and redundant. But getting data out of Hadoop is tough due to a lack of a built-in query language. Also, because users experience high latency (up to several minutes per query), Hadoop is not appropriate for ad hoc query, reporting, and business analysis with traditional tools.
The first step in overcoming Hadoop's constraints is connecting to HIVE, a data warehouse infrastructure built on top of Hadoop, which provides the relational structure necessary for schedule reporting of large datasets data stored in Hadoop files. HIVE also provides a simple query language called Hive QL which is based on SQL and which enables users familiar with SQL to query this data.
But to really unlock the power of Hadoop, you must be able to efficiently extract data stored across multiple (often tens or hundreds) of nodes with a user-friendly ETL (extract, transform and load) tool that will then allow you to move your Hadoop data into a relational data mart or warehouse where you can use BI tools for analysis.
Hadoop World 2011: Completing the Big Data Picture Understanding Why and Not ...Cloudera, Inc.
It's increasingly clear that Big Data is not just about volume – but also the variety, complexity and velocity of enterprise information. Integrating data with insights from unstructured information such as documents, call logs, and web content is essential to driving sustainable business value. Aggregating and analyzing unstructured content is challenging because human expression is diverse, varies by location, and changes over time. To understand the causes of data trends, you need advanced text analytic capabilities. Furthermore, you need a system that provides direct, real-time access to discover hidden insights. In this session, you will learn how united information access (UIA) uniquely completes the picture by integrating Big Data directly with unstructured content and advanced text analytics, and making it directly accessible to business users.
This document summarizes the progress made in strengthening the ICT capacity of the National Institute of Statistics of Rwanda (NISR) with support from UNDP. It describes how the ICT team has expanded, the network infrastructure has been improved with email, file sharing and backup systems, and data processing and dissemination tools like databases, websites and data portals have been developed. New policies around equipment and software management have also been introduced. Staff training on technical skills and certifications has increased the capacity of both the ICT and NISR staff. Key partnerships with organizations like the Ministry of Finance have also been discussed.
This document summarizes how a large retailer used FileBound content management software to digitize over 40 million documents from an acquisition. The retailer was struggling with misfiling documents, spending too much time searching, and backups that took over 24 hours. FileBound provided unlimited users and storage, remote access, integration with core systems, access controls, and reliable backups. Over time, usage grew to over 90 projects and 2,100 active users storing 3 million documents. FileBound helped solve the retailer's information management challenges.
Digitizing a newspaper clippings collection: a case study in small-scale digi...Molly Knapp
This document outlines the process of digitizing a collection of newspaper clippings from 1933 to present day about the history of health sciences in Louisiana. It describes the original collection and its deteriorating condition over time. It then details the timeline and workflow established to scan, process, catalog and make the collection available online through a digital library consortium. This included considering standards, training needs, documentation, challenges of buy-in, sustainability and providing access within copyright restrictions. The results were a searchable online historic archive, increased visibility, opportunities for future projects and mentoring others through the process.
The document provides an overview of knowledge management concepts from several perspectives:
1) It distinguishes between data, information, knowledge, wisdom and discusses the relationships between them.
2) It examines tacit and explicit knowledge and the processes of moving between the two.
3) It explores individual and organizational learning and knowledge acquisition.
4) It introduces knowledge management processes and discusses challenges organizations face in managing knowledge.
This document discusses challenges and opportunities for companies to gain competitive advantage through leveraging big data and data analytics. It notes that (1) enterprises can gain operational advantages by leveraging social, local and mobile technologies to generate insights from individual data, (2) commonly used information architectures do not effectively support collaboration and sharing of all types of information across networks, and (3) companies must address both collaboration/communication and making sense of vast information streams. The document then provides statistics on growth of digital data and challenges of analyzing unstructured data to reveal relevant insights.
Putting Business Intelligence to Work on Hadoop Data StoresDATAVERSITY
An inexpensive way of storing large volumes of data, Hadoop is also scalable and redundant. But getting data out of Hadoop is tough due to a lack of a built-in query language. Also, because users experience high latency (up to several minutes per query), Hadoop is not appropriate for ad hoc query, reporting, and business analysis with traditional tools.
The first step in overcoming Hadoop's constraints is connecting to HIVE, a data warehouse infrastructure built on top of Hadoop, which provides the relational structure necessary for schedule reporting of large datasets data stored in Hadoop files. HIVE also provides a simple query language called Hive QL which is based on SQL and which enables users familiar with SQL to query this data.
But to really unlock the power of Hadoop, you must be able to efficiently extract data stored across multiple (often tens or hundreds) of nodes with a user-friendly ETL (extract, transform and load) tool that will then allow you to move your Hadoop data into a relational data mart or warehouse where you can use BI tools for analysis.
Hadoop World 2011: Completing the Big Data Picture Understanding Why and Not ...Cloudera, Inc.
It's increasingly clear that Big Data is not just about volume – but also the variety, complexity and velocity of enterprise information. Integrating data with insights from unstructured information such as documents, call logs, and web content is essential to driving sustainable business value. Aggregating and analyzing unstructured content is challenging because human expression is diverse, varies by location, and changes over time. To understand the causes of data trends, you need advanced text analytic capabilities. Furthermore, you need a system that provides direct, real-time access to discover hidden insights. In this session, you will learn how united information access (UIA) uniquely completes the picture by integrating Big Data directly with unstructured content and advanced text analytics, and making it directly accessible to business users.
This document summarizes the progress made in strengthening the ICT capacity of the National Institute of Statistics of Rwanda (NISR) with support from UNDP. It describes how the ICT team has expanded, the network infrastructure has been improved with email, file sharing and backup systems, and data processing and dissemination tools like databases, websites and data portals have been developed. New policies around equipment and software management have also been introduced. Staff training on technical skills and certifications has increased the capacity of both the ICT and NISR staff. Key partnerships with organizations like the Ministry of Finance have also been discussed.
This document summarizes how a large retailer used FileBound content management software to digitize over 40 million documents from an acquisition. The retailer was struggling with misfiling documents, spending too much time searching, and backups that took over 24 hours. FileBound provided unlimited users and storage, remote access, integration with core systems, access controls, and reliable backups. Over time, usage grew to over 90 projects and 2,100 active users storing 3 million documents. FileBound helped solve the retailer's information management challenges.
Digitizing a newspaper clippings collection: a case study in small-scale digi...Molly Knapp
This document outlines the process of digitizing a collection of newspaper clippings from 1933 to present day about the history of health sciences in Louisiana. It describes the original collection and its deteriorating condition over time. It then details the timeline and workflow established to scan, process, catalog and make the collection available online through a digital library consortium. This included considering standards, training needs, documentation, challenges of buy-in, sustainability and providing access within copyright restrictions. The results were a searchable online historic archive, increased visibility, opportunities for future projects and mentoring others through the process.
The document provides an overview of knowledge management concepts from several perspectives:
1) It distinguishes between data, information, knowledge, wisdom and discusses the relationships between them.
2) It examines tacit and explicit knowledge and the processes of moving between the two.
3) It explores individual and organizational learning and knowledge acquisition.
4) It introduces knowledge management processes and discusses challenges organizations face in managing knowledge.
The document discusses the science of debugging by outlining key aspects of the debugging process including understanding bugs, isolating bugs, analyzing bugs, developing solutions, testing fixes, and preventing future bugs. It defines a bug, explains bug attributes like behavior and severity, and stresses the importance of isolation, analysis, and testing in methodically debugging issues. The overall process aims to bring a more scientific approach to debugging rather than relying on intuition alone.
This document provides instructions for integrating SAP Business Intelligence (BI) with an ECC6 system. It outlines the steps to create a remote BI user in the ECC6 system, assign necessary profiles, and replicate the ECC6 system in BI to create an infocube for reporting and analysis. The integration allows BI to access and analyze data from the ECC6 system.
Even better debugging; Equipped yourself with powerful tools.Murshed Ahmmad Khan
This slide was prepared for the phpxperts seminar 2010. As the presentation time was limited so it only touches few primitive approaches and moves to a mighty debugging extension xdebug. The tag line is "Every job has a unique tool", so spend some time to enrich yourself with powerful debug tools rather than coding all of the time which in turns will save your lot of development time in the future.
Setting breakpoints and using different debug step functions like F5, F6, F7 and F8 are described for debugging ABAP programs. The different types of breakpoints - debugger breakpoints, session breakpoints and user breakpoints are explained. Methods for debugging remote function modules using transaction SRDEBUG, debugging background jobs using transactions SM37 and SM50, and setting watchpoints to monitor variable changes are provided. Debugging techniques for smart forms using program lines and transaction SFTRACE are also summarized.
This chapter provides an overview of the SAP system architecture and ABAP workbench. It discusses the client/server architecture of SAP systems with presentation, application, and database servers. It describes the environment for ABAP programs including work processes and the dispatcher. It also provides a first look at tools in the ABAP workbench like the ABAP editor, function builder, menu painter and screen painter.
LBSI is a technology services firm that provides services to help clients grow profitably and outpace competition. It has offices in Cleveland, Columbus, Cincinnati, Toledo, Pittsburgh, Philadelphia, and Indianapolis. The document provides tips and shortcuts for using SAP Business One, including how to delete links from user-defined fields, update descriptions, view column totals, and set up recurring postings for monthly payments. It announces upcoming user group meetings and asks attendees to refer new clients to LBSI.
Sap bi roadmap overview 2010 sap inside track stlsjohannes
This document discusses the differences between the roadmaps of SAP Business Warehouse and Business Objects Enterprise. It provides an overview of the key tools in the Business Objects platform, such as Web Intelligence, Crystal Reports, and Xcelsius. The document outlines the roadmaps from 2010-2011, noting the integration of data connection tools, in-memory storage, and semantic layer developments. It concludes by discussing next steps for organizations to define their BI strategy and determine how to connect tools to operational systems and external users.
The document provides an overview of reporting capabilities in SAP BW, including:
1) The Query Designer allows for easier query design and web publishing of reports with new features like variable maintenance and enhanced calculation abilities.
2) Variables can now be defined directly in the query designer using a new wizard. Hierarchies also have improved support.
3) Queries can be published directly to the web in one step and downloaded to Excel.
Finding and fixing bugs is a major chunk of any developers time. This talk describes the basic rules for effective debugging in any language, but shows how the tools available in PHP can be used to find and fix even the most elusive error
The document provides an introduction on how to create and manage variable type customer exits in SAP BI 7.0 queries. It discusses setting up a new project for an SAP enhancement, creating a variable in a BW query and setting its type to customer exit, writing ABAP code to manage the variable's values, and testing the customer exit.
Sap sd interview_questions_answers_and_explanations_espana_norestrictionkdnv
This document contains 75 interview questions for the SAP SD module. It begins with an introduction and table of contents. It then provides the questions, answers, and explanations for each interview question. The questions cover a wide range of SD topics including sales documents, pricing, delivery, billing, master data, and more. The document aims to help screen resources and assess their true understanding of SAP SD.
The document explains how to use a customer exit variable in SAP BW/BI reports to display month-to-date data based on the current date. It describes creating a variable called Z_FDCPM to return the first day of the current or previous month. The code for the variable is provided to calculate this date. Instructions are given on testing the variable, designing a report with it, and executing the report to see month-to-date results.
The document summarizes key concepts from the Udacity CS259 Software Debugging course, including:
1) Three states of errors in programs - defects in code, infections spreading errors, and failures in execution. Understanding how errors propagate through a program is important to debugging.
2) Techniques for debugging like assertions, code coverage, tracing, delta debugging and phi scoring to narrow relevant information and find root causes of failures.
3) Managing bugs in a project through proper tracking of problem lifecycles from new to closed, and use of bug databases linked to version control to map defects to code revisions.
NEWYORKSYS emphasize on training our students / trainees to undergo intensive training under the guidance of special team of experienced IT trainers, placements. NEWYORKSYS firmly believe that each individual student / trainee possess some or other distinct skill.
The document provides an overview of debugging in ABAP, including defining debugging, branching to debugging mode, key concepts such as breakpoints and watchpoints, examining and changing variables, and viewing contents of internal tables. It describes debugging modes, the debugging display, and the most important debugging functions such as single step, continue, breakpoint, watchpoint, and hexadecimal display. It also discusses setting breakpoints and watchpoints and using the ABAP debugger.
INTRODUCTION TO BIG DATA AND HADOOP
9
Introduction to Big Data, Types of Digital Data, Challenges of conventional systems - Web data, Evolution of analytic processes and tools, Analysis Vs reporting - Big Data Analytics, Introduction to Hadoop - Distributed Computing
Challenges - History of Hadoop, Hadoop Eco System - Use case of Hadoop – Hadoop Distributors – HDFS – Processing Data with Hadoop – Map Reduce.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
The document discusses the growth of data and how SAP products can help manage and analyze large amounts of data. It provides the following key details:
- The amount of data in the world has grown dramatically to 1.8 zettabytes in 2011 and 90% of the data today was created in the last two years.
- SAP offers solutions like HANA, BusinessObjects, and big data applications to help organizations capture, store, manage and analyze massive amounts of structured and unstructured data from various sources.
- HANA provides an in-memory database platform for real-time analytics while integrating with Hadoop for infinite storage and processing of large unstructured data sets.
SAP HANA and Apache Hadoop for Big Data Management (SF Scalable Systems Meetup)Will Gardella
In this presentation I argue that the future of data management may see a split between (1) real-time in-memory systems such as SAP HANA for most enterprise workloads (2) disk-based free and open-source Apache Hadoop for certain specialized big data uses.
The presentation starts with a definition of what is intended by the term big data, then talks about SAP HANA and Apache Hadoop from the perspective of suitability for enterprise use with a special concentration on Hadoop. (The basics of SAP HANA were covered in the immediately preceding session). This is followed by a description of currently available SAP support for Apache Hadoop in SAP BI 4.0 and SAP Data Services / EIM. Due to time constraints I did not discuss Apache Hadoop support built into Sybase IQ.
The document discusses the science of debugging by outlining key aspects of the debugging process including understanding bugs, isolating bugs, analyzing bugs, developing solutions, testing fixes, and preventing future bugs. It defines a bug, explains bug attributes like behavior and severity, and stresses the importance of isolation, analysis, and testing in methodically debugging issues. The overall process aims to bring a more scientific approach to debugging rather than relying on intuition alone.
This document provides instructions for integrating SAP Business Intelligence (BI) with an ECC6 system. It outlines the steps to create a remote BI user in the ECC6 system, assign necessary profiles, and replicate the ECC6 system in BI to create an infocube for reporting and analysis. The integration allows BI to access and analyze data from the ECC6 system.
Even better debugging; Equipped yourself with powerful tools.Murshed Ahmmad Khan
This slide was prepared for the phpxperts seminar 2010. As the presentation time was limited so it only touches few primitive approaches and moves to a mighty debugging extension xdebug. The tag line is "Every job has a unique tool", so spend some time to enrich yourself with powerful debug tools rather than coding all of the time which in turns will save your lot of development time in the future.
Setting breakpoints and using different debug step functions like F5, F6, F7 and F8 are described for debugging ABAP programs. The different types of breakpoints - debugger breakpoints, session breakpoints and user breakpoints are explained. Methods for debugging remote function modules using transaction SRDEBUG, debugging background jobs using transactions SM37 and SM50, and setting watchpoints to monitor variable changes are provided. Debugging techniques for smart forms using program lines and transaction SFTRACE are also summarized.
This chapter provides an overview of the SAP system architecture and ABAP workbench. It discusses the client/server architecture of SAP systems with presentation, application, and database servers. It describes the environment for ABAP programs including work processes and the dispatcher. It also provides a first look at tools in the ABAP workbench like the ABAP editor, function builder, menu painter and screen painter.
LBSI is a technology services firm that provides services to help clients grow profitably and outpace competition. It has offices in Cleveland, Columbus, Cincinnati, Toledo, Pittsburgh, Philadelphia, and Indianapolis. The document provides tips and shortcuts for using SAP Business One, including how to delete links from user-defined fields, update descriptions, view column totals, and set up recurring postings for monthly payments. It announces upcoming user group meetings and asks attendees to refer new clients to LBSI.
Sap bi roadmap overview 2010 sap inside track stlsjohannes
This document discusses the differences between the roadmaps of SAP Business Warehouse and Business Objects Enterprise. It provides an overview of the key tools in the Business Objects platform, such as Web Intelligence, Crystal Reports, and Xcelsius. The document outlines the roadmaps from 2010-2011, noting the integration of data connection tools, in-memory storage, and semantic layer developments. It concludes by discussing next steps for organizations to define their BI strategy and determine how to connect tools to operational systems and external users.
The document provides an overview of reporting capabilities in SAP BW, including:
1) The Query Designer allows for easier query design and web publishing of reports with new features like variable maintenance and enhanced calculation abilities.
2) Variables can now be defined directly in the query designer using a new wizard. Hierarchies also have improved support.
3) Queries can be published directly to the web in one step and downloaded to Excel.
Finding and fixing bugs is a major chunk of any developers time. This talk describes the basic rules for effective debugging in any language, but shows how the tools available in PHP can be used to find and fix even the most elusive error
The document provides an introduction on how to create and manage variable type customer exits in SAP BI 7.0 queries. It discusses setting up a new project for an SAP enhancement, creating a variable in a BW query and setting its type to customer exit, writing ABAP code to manage the variable's values, and testing the customer exit.
Sap sd interview_questions_answers_and_explanations_espana_norestrictionkdnv
This document contains 75 interview questions for the SAP SD module. It begins with an introduction and table of contents. It then provides the questions, answers, and explanations for each interview question. The questions cover a wide range of SD topics including sales documents, pricing, delivery, billing, master data, and more. The document aims to help screen resources and assess their true understanding of SAP SD.
The document explains how to use a customer exit variable in SAP BW/BI reports to display month-to-date data based on the current date. It describes creating a variable called Z_FDCPM to return the first day of the current or previous month. The code for the variable is provided to calculate this date. Instructions are given on testing the variable, designing a report with it, and executing the report to see month-to-date results.
The document summarizes key concepts from the Udacity CS259 Software Debugging course, including:
1) Three states of errors in programs - defects in code, infections spreading errors, and failures in execution. Understanding how errors propagate through a program is important to debugging.
2) Techniques for debugging like assertions, code coverage, tracing, delta debugging and phi scoring to narrow relevant information and find root causes of failures.
3) Managing bugs in a project through proper tracking of problem lifecycles from new to closed, and use of bug databases linked to version control to map defects to code revisions.
NEWYORKSYS emphasize on training our students / trainees to undergo intensive training under the guidance of special team of experienced IT trainers, placements. NEWYORKSYS firmly believe that each individual student / trainee possess some or other distinct skill.
The document provides an overview of debugging in ABAP, including defining debugging, branching to debugging mode, key concepts such as breakpoints and watchpoints, examining and changing variables, and viewing contents of internal tables. It describes debugging modes, the debugging display, and the most important debugging functions such as single step, continue, breakpoint, watchpoint, and hexadecimal display. It also discusses setting breakpoints and watchpoints and using the ABAP debugger.
INTRODUCTION TO BIG DATA AND HADOOP
9
Introduction to Big Data, Types of Digital Data, Challenges of conventional systems - Web data, Evolution of analytic processes and tools, Analysis Vs reporting - Big Data Analytics, Introduction to Hadoop - Distributed Computing
Challenges - History of Hadoop, Hadoop Eco System - Use case of Hadoop – Hadoop Distributors – HDFS – Processing Data with Hadoop – Map Reduce.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
The document discusses the growth of data and how SAP products can help manage and analyze large amounts of data. It provides the following key details:
- The amount of data in the world has grown dramatically to 1.8 zettabytes in 2011 and 90% of the data today was created in the last two years.
- SAP offers solutions like HANA, BusinessObjects, and big data applications to help organizations capture, store, manage and analyze massive amounts of structured and unstructured data from various sources.
- HANA provides an in-memory database platform for real-time analytics while integrating with Hadoop for infinite storage and processing of large unstructured data sets.
SAP HANA and Apache Hadoop for Big Data Management (SF Scalable Systems Meetup)Will Gardella
In this presentation I argue that the future of data management may see a split between (1) real-time in-memory systems such as SAP HANA for most enterprise workloads (2) disk-based free and open-source Apache Hadoop for certain specialized big data uses.
The presentation starts with a definition of what is intended by the term big data, then talks about SAP HANA and Apache Hadoop from the perspective of suitability for enterprise use with a special concentration on Hadoop. (The basics of SAP HANA were covered in the immediately preceding session). This is followed by a description of currently available SAP support for Apache Hadoop in SAP BI 4.0 and SAP Data Services / EIM. Due to time constraints I did not discuss Apache Hadoop support built into Sybase IQ.
Roland Haeve (Atos): 'Using the Cloud for Big Data Analytics'AlmereDataCapital
Presentatie van Roland Haeve (Atos): 'Using the Cloud for Big Data Analytics' tijdens het Big Data Analytics seminar 14 juni van Almere DataCapital in Almere.
This document discusses SAP's data services for processing unstructured data. It notes that most business information exists outside standard databases as unstructured data like documents, emails and sensor data. SAP BO Data Services provides a single solution for both structured and unstructured data with text analytics capabilities. It allows extraction of entities from unstructured text sources like emails through linguistic processing and stores binary files like images as binary large objects for querying, reporting and analytics. A proof of concept demonstrates processing an email message file and image file as unstructured text and binary sources respectively.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
The document discusses business intelligence for big data using Hadoop. It describes how 90% of companies are using or plan to use Hadoop to transform structured or semi-structured data for analysis and reporting. While Hadoop provides scalability through distributed processing and storage, its MapReduce programming model makes data transformation difficult for developers accustomed to graphical tools. The document traces how Google and Yahoo developed MapReduce for specific use cases of indexing the internet at massive scales, and how it has since been generalized beyond those specific needs.
This document discusses big data and Hadoop. It begins by describing the rapid growth of data from sources around the world. Hadoop provides a solution to challenges in storing and processing large volumes of unstructured data across distributed systems. The document then discusses key aspects of big data including the five V's (volume, velocity, variety, value and veracity). It provides examples of large companies using Hadoop and big data like Google, Facebook, Amazon and Twitter. The document concludes that Hadoop is well-suited for batch processing large datasets and provides advantages over relational database management systems.
SAP HANA is an in-memory database and platform that allows for real-time analytics on large datasets. It utilizes columnar storage, massive parallelization across cores and servers, and in-memory computing to enable interactive queries and analysis of big data without the latency of disk access. SAP HANA provides a single system for both transaction processing and analytics, combining structured and unstructured data on a scalable platform.
This document provides an overview of big data concepts and Hadoop. It discusses the characteristics of big data including volume, variety and velocity. It compares traditional data warehouses to Hadoop and explains when each is best suited. Use cases of big data from various companies are presented. The document also summarizes a survey on big data adoption trends and priorities across industries. Finally, it provides details on the Hadoop framework and its key components.
The document discusses the growing trend of big data and how cloud storage is a viable option for enterprise data storage needs. It notes that while cloud storage adoption has been slow, offerings continue to mature to handle larger data volumes, varieties, and velocities. The document recommends that organizations prepare their storage environments, evaluate emerging big data solutions, and rationalize their data to take advantage of next generation cloud-based storage architectures optimized for big data.
The document discusses data tagging and its benefits. It notes that data tagging provides a standardized way to compare spending and outlays, and structures information about recipients, agencies, and the Treasury. Data tagging allows for immediate impact on transparency and reducing fraud, waste, and abuse, while also significantly lowering costs for growing and innovating. The document demonstrates how data tagging solutions can access and transform various data sources for reporting and answering business questions.
The document discusses big data and MapReduce frameworks like Hadoop. It provides an overview of MapReduce and how it allows distributed processing of large datasets using simple map and reduce functions. The document also covers several common design patterns for MapReduce jobs, including filtering, sorting, joins, and computing statistics.
Enterprise Information Management (EIM) involves managing and governing all types of data and information throughout its lifecycle from creation to retirement. EIM covers both structured and unstructured data, including documents, emails, and multimedia content. SAP's EIM solutions are designed to manage information as it moves through its natural lifecycle. EIM impacts SAP's strategy by supporting its applications and software portfolio through services that integrate, cleanse, and govern data to ensure high quality information is available across the enterprise.
Introducing the Big Data Ecosystem with Caserta Concepts & TalendCaserta
This document summarizes a webinar presented by Talend and Caserta Concepts on the big data ecosystem. The webinar discussed how Talend provides an open source integration platform that scales to handle large data volumes and complex processes. It also overviewed Caserta Concepts' expertise in data management, big data analytics, and industries like financial services. The webinar covered topics like traditional vs big data, Hadoop and NoSQL technologies, and common integration patterns between traditional data warehouses and big data platforms.
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.
Big Data and Implications on Platform ArchitectureOdinot Stanislas
This document discusses big data and its implications for data center architecture. It provides examples of big data use cases in telecommunications, including analyzing calling patterns and subscriber usage. It also discusses big data analytics for applications like genome sequencing, traffic modeling, and spam filtering on social media feeds. The document outlines necessary characteristics for data platforms to support big data workloads, such as scalable compute, storage, networking and high memory capacity.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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:
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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