This document introduces big data concepts and Microsoft's solutions for big data. It defines big data as large, complex datasets that are difficult to process using traditional systems. It also describes the 3Vs of big data: volume, velocity, and variety. The document then outlines Microsoft's offerings for big data including HDInsight, .NET SDK for Hadoop, ODBC driver for Hive, and integrations with Excel, SharePoint, and SQL Server. It provides overviews of Hadoop, HDFS, MapReduce, and the Hadoop ecosystem.
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
Great Expectations is an open-source Python library that helps validate, document, and profile data to maintain quality. It allows users to define expectations about data that are used to validate new data and generate documentation. Key features include automated data profiling, predefined and custom validation rules, and scalability. It is used by companies like Vimeo and Heineken in their data pipelines. While helpful for testing data, it is not intended as a data cleaning or versioning tool. A demo shows how to initialize a project, validate sample taxi data, and view results.
This presentation introduces concepts of Big Data in a layman's language. Author does not claim the originality of the content. The presentation is made by compiling from various sources. Author does not claim copyrights or privacy issues.
Big data is exponentially rising in today's age of information and digital shrinkage. This presentation potentially clears the concept and revolving hype around it.
This document introduces big data by defining it as large, complex datasets that cannot be processed by traditional methods due to their size. It explains that big data comes from sources like online activity, social media, science, and IoT devices. Examples are given of the massive scales of data produced each day. The challenges of processing big data with traditional databases and software are illustrated through a fictional startup example. The document argues that new tools and approaches are needed to handle automatic scaling, replication, and fault tolerance. It presents Apache Hadoop and Spark as open-source big data tools that can process petabytes of data across thousands of nodes through distributed and scalable architectures.
Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...yashbheda
Big data is generated from various sources like users, systems, and devices. It has grown exponentially due to factors like volume, velocity, variety, and veracity. Analyzing big data helps optimize network resources, improve security monitoring, enable targeted marketing, and enhance performance evaluation. Implementing big data solutions requires strategies for data collection, analysis, storage, and visualization to extract useful insights at scale.
This document introduces big data concepts and Microsoft's solutions for big data. It defines big data as large, complex datasets that are difficult to process using traditional systems. It also describes the 3Vs of big data: volume, velocity, and variety. The document then outlines Microsoft's offerings for big data including HDInsight, .NET SDK for Hadoop, ODBC driver for Hive, and integrations with Excel, SharePoint, and SQL Server. It provides overviews of Hadoop, HDFS, MapReduce, and the Hadoop ecosystem.
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
Great Expectations is an open-source Python library that helps validate, document, and profile data to maintain quality. It allows users to define expectations about data that are used to validate new data and generate documentation. Key features include automated data profiling, predefined and custom validation rules, and scalability. It is used by companies like Vimeo and Heineken in their data pipelines. While helpful for testing data, it is not intended as a data cleaning or versioning tool. A demo shows how to initialize a project, validate sample taxi data, and view results.
This presentation introduces concepts of Big Data in a layman's language. Author does not claim the originality of the content. The presentation is made by compiling from various sources. Author does not claim copyrights or privacy issues.
Big data is exponentially rising in today's age of information and digital shrinkage. This presentation potentially clears the concept and revolving hype around it.
This document introduces big data by defining it as large, complex datasets that cannot be processed by traditional methods due to their size. It explains that big data comes from sources like online activity, social media, science, and IoT devices. Examples are given of the massive scales of data produced each day. The challenges of processing big data with traditional databases and software are illustrated through a fictional startup example. The document argues that new tools and approaches are needed to handle automatic scaling, replication, and fault tolerance. It presents Apache Hadoop and Spark as open-source big data tools that can process petabytes of data across thousands of nodes through distributed and scalable architectures.
Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...yashbheda
Big data is generated from various sources like users, systems, and devices. It has grown exponentially due to factors like volume, velocity, variety, and veracity. Analyzing big data helps optimize network resources, improve security monitoring, enable targeted marketing, and enhance performance evaluation. Implementing big data solutions requires strategies for data collection, analysis, storage, and visualization to extract useful insights at scale.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Big Data is still a challenge for many companies to collect, process, and analyze large amounts of structured and unstructured data. Hadoop provides an open source framework for distributed storage and processing of large datasets across commodity servers to help companies gain insights from big data. While Hadoop is commonly used, Spark is becoming a more popular tool that can run 100 times faster for iterative jobs and integrates with SQL, machine learning, and streaming technologies. Both Hadoop and Spark often rely on the Hadoop Distributed File System for storage and are commonly implemented together in big data projects and platforms from major vendors.
The document provides an introduction to big data and Hadoop. It describes the concepts of big data, including the four V's of big data: volume, variety, velocity and veracity. It then explains Hadoop and how it addresses big data challenges through its core components. Finally, it describes the various components that make up the Hadoop ecosystem, such as HDFS, HBase, Sqoop, Flume, Spark, MapReduce, Pig and Hive. The key takeaways are that the reader will now be able to describe big data concepts, explain how Hadoop addresses big data challenges, and describe the components of the Hadoop ecosystem.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
This document provides an introduction to a course on big data analytics. It discusses the characteristics of big data, including large scale, variety of data types and formats, and fast data generation speeds. It defines big data as data that requires new techniques to manage and analyze due to its scale, diversity and complexity. The document outlines some of the key challenges in handling big data and introduces Hadoop and MapReduce as technologies for managing large datasets in a scalable way. It provides an overview of what topics will be covered in the course, including programming models for Hadoop, analytics tools, and state-of-the-art research on big data technologies and optimizations.
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Are you having doubts and questions about how to use Big Data in your organizations? The presentation here would clear some of your doubts.
Feel free to comment if you have more queries or write to us at: bigdata@xoriant.com
This document discusses big data analytics. It defines big data as large, complex datasets that come from a variety of sources and are analyzed to reveal insights. It explains that big data is characterized by its volume, variety, velocity, variability, and complexity. The document outlines different types of data (structured, unstructured, semi-structured) and sources of data (internal, external). It also contrasts traditional data analytics with big data analytics and describes various analysis types including basic, advanced, and operationalized analytics. Finally, it provides an overview of common big data approaches like Hadoop, NoSQL databases, and massively parallel analytic databases.
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Big data refers to large volumes of diverse data that traditional database systems cannot effectively handle. With the rise of technologies like social media, sensors, and mobile devices, huge amounts of unstructured data are being generated every day. To gain insights from this "big data", alternative processing methods are needed. Hadoop is an open-source platform that can distribute data storage and processing across many servers to handle large datasets. Facebook uses Hadoop to store over 100 petabytes of user data and gain insights through analysis to improve user experience and target advertising. Organizations must prepare infrastructure like Hadoop to capture value from the growing "data tsunami" and enhance their business with big data analytics.
Big data refers to extremely large and complex datasets that cannot be processed using traditional data processing software. It is characterized by high volume, variety, velocity, veracity, and value. Key concepts for working with big data include clustered, parallel, and distributed computing which involve pooling resources across multiple machines to analyze large datasets simultaneously. Common frameworks and tools are used to break jobs into smaller pieces to run in parallel across distributed systems for batch and real-time processing. Cloud computing provides an effective solution for big data processing by renting servers as needed from leading providers.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
Intro to big data and applications - day 2Parviz Vakili
The document provides an introduction and references for a presentation on big data and applications. It includes sections on data architecture, data governance, data modeling and design, and reference architectures for big data analytics. The presentation template was created by Slidesgo and credits are provided.
BDaas- BigData as a service by "Sherya Pal" from "Saama". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
This document presents an overview of big data. It defines big data as large, diverse data that requires new techniques to manage and extract value from. It discusses the 3 V's of big data - volume, velocity and variety. Examples of big data sources include social media, sensors, photos and business transactions. Challenges of big data include storage, transfer, processing, privacy and data sharing. Past solutions discussed include data sharding, while modern solutions include Hadoop, MapReduce, HDFS and RDF.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
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 big data concepts including:
- Mohamed Magdy's background and credentials in big data engineering and data science.
- Definitions of big data, the three V's of big data (volume, velocity, variety), and why big data analytics is important.
- Descriptions of Hadoop, HDFS, MapReduce, and YARN - the core components of Hadoop architecture for distributed storage and processing of big data.
- Explanations of HDFS architecture, data blocks, high availability in HDFS 2/3, and erasure coding in HDFS 3.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Big Data is still a challenge for many companies to collect, process, and analyze large amounts of structured and unstructured data. Hadoop provides an open source framework for distributed storage and processing of large datasets across commodity servers to help companies gain insights from big data. While Hadoop is commonly used, Spark is becoming a more popular tool that can run 100 times faster for iterative jobs and integrates with SQL, machine learning, and streaming technologies. Both Hadoop and Spark often rely on the Hadoop Distributed File System for storage and are commonly implemented together in big data projects and platforms from major vendors.
The document provides an introduction to big data and Hadoop. It describes the concepts of big data, including the four V's of big data: volume, variety, velocity and veracity. It then explains Hadoop and how it addresses big data challenges through its core components. Finally, it describes the various components that make up the Hadoop ecosystem, such as HDFS, HBase, Sqoop, Flume, Spark, MapReduce, Pig and Hive. The key takeaways are that the reader will now be able to describe big data concepts, explain how Hadoop addresses big data challenges, and describe the components of the Hadoop ecosystem.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
This document provides an introduction to a course on big data analytics. It discusses the characteristics of big data, including large scale, variety of data types and formats, and fast data generation speeds. It defines big data as data that requires new techniques to manage and analyze due to its scale, diversity and complexity. The document outlines some of the key challenges in handling big data and introduces Hadoop and MapReduce as technologies for managing large datasets in a scalable way. It provides an overview of what topics will be covered in the course, including programming models for Hadoop, analytics tools, and state-of-the-art research on big data technologies and optimizations.
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Are you having doubts and questions about how to use Big Data in your organizations? The presentation here would clear some of your doubts.
Feel free to comment if you have more queries or write to us at: bigdata@xoriant.com
This document discusses big data analytics. It defines big data as large, complex datasets that come from a variety of sources and are analyzed to reveal insights. It explains that big data is characterized by its volume, variety, velocity, variability, and complexity. The document outlines different types of data (structured, unstructured, semi-structured) and sources of data (internal, external). It also contrasts traditional data analytics with big data analytics and describes various analysis types including basic, advanced, and operationalized analytics. Finally, it provides an overview of common big data approaches like Hadoop, NoSQL databases, and massively parallel analytic databases.
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Big data refers to large volumes of diverse data that traditional database systems cannot effectively handle. With the rise of technologies like social media, sensors, and mobile devices, huge amounts of unstructured data are being generated every day. To gain insights from this "big data", alternative processing methods are needed. Hadoop is an open-source platform that can distribute data storage and processing across many servers to handle large datasets. Facebook uses Hadoop to store over 100 petabytes of user data and gain insights through analysis to improve user experience and target advertising. Organizations must prepare infrastructure like Hadoop to capture value from the growing "data tsunami" and enhance their business with big data analytics.
Big data refers to extremely large and complex datasets that cannot be processed using traditional data processing software. It is characterized by high volume, variety, velocity, veracity, and value. Key concepts for working with big data include clustered, parallel, and distributed computing which involve pooling resources across multiple machines to analyze large datasets simultaneously. Common frameworks and tools are used to break jobs into smaller pieces to run in parallel across distributed systems for batch and real-time processing. Cloud computing provides an effective solution for big data processing by renting servers as needed from leading providers.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
Intro to big data and applications - day 2Parviz Vakili
The document provides an introduction and references for a presentation on big data and applications. It includes sections on data architecture, data governance, data modeling and design, and reference architectures for big data analytics. The presentation template was created by Slidesgo and credits are provided.
BDaas- BigData as a service by "Sherya Pal" from "Saama". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
This document presents an overview of big data. It defines big data as large, diverse data that requires new techniques to manage and extract value from. It discusses the 3 V's of big data - volume, velocity and variety. Examples of big data sources include social media, sensors, photos and business transactions. Challenges of big data include storage, transfer, processing, privacy and data sharing. Past solutions discussed include data sharding, while modern solutions include Hadoop, MapReduce, HDFS and RDF.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
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 big data concepts including:
- Mohamed Magdy's background and credentials in big data engineering and data science.
- Definitions of big data, the three V's of big data (volume, velocity, variety), and why big data analytics is important.
- Descriptions of Hadoop, HDFS, MapReduce, and YARN - the core components of Hadoop architecture for distributed storage and processing of big data.
- Explanations of HDFS architecture, data blocks, high availability in HDFS 2/3, and erasure coding in HDFS 3.
This document provides an overview of how to prepare for a career in data science. It discusses the author's own career path, which included degrees in bioinformatics and machine learning as well as jobs as a data scientist. It then outlines the typical data science workflow, including identifying problems, accessing and cleaning data, exploratory analysis, modeling, and deploying results. It emphasizes that data science is an iterative process and stresses the importance of communication skills. Finally, it discusses how data science fits within business contexts and the value of working on teams with complementary skills.
Building a Data Platform Strata SF 2019mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Big Data is no longer considered a hype according to research firm Gartner, but it is an emerging trend that is here to stay. While Hadoop is commonly associated with Big Data, Big Data encompasses more than just Hadoop. Big Data requires not only technical changes but also cultural changes in how organizations approach data. Example applications of Big Data were presented, including a cloud-based electronic traceability system for semiconductor manufacturing and a research project aiming to profitably share vehicle diagnostic data across automotive partners while protecting private data. In conclusion, while Big Data applications are still developing, the concepts of collecting all available data now with the goal of analyzing it later have taken hold as storage and processing capabilities increase.
The document is a project report submitted by Suraj Sawant to his college on the topic of "Map Reduce in Big Data". It discusses the objectives, introduction and importance of big data and MapReduce. MapReduce is a programming model used for processing large datasets in a distributed manner. The document provides details about the various stages of MapReduce including mapping, shuffling and reducing data. It also includes diagrams to explain the execution process and parallel processing in MapReduce.
This document outlines the course content for a Big Data Analytics course. The course covers key concepts related to big data including Hadoop, MapReduce, HDFS, YARN, Pig, Hive, NoSQL databases and analytics tools. The 5 units cover introductions to big data and Hadoop, MapReduce and YARN, analyzing data with Pig and Hive, and NoSQL data management. Experiments related to big data are also listed.
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.
Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
This document provides an overview and guide for implementing a successful big data project. It discusses common reasons why big data projects fail, such as having vague goals, mismanaged expectations, going over budget/timeline, and an inability to scale. The document then provides tips for ensuring a big data project succeeds, such as setting clear objectives and metrics to demonstrate the project's value, and using tools to automate processes rather than relying solely on manual coding. The overall aim is to help readers establish focus, prove practical impact, and deliver sustainable value from their big data initiative.
Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
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.
The white paper discusses how enterprises are facing exponentially growing amounts of data that is breaking down traditional storage architectures. It outlines NetApp's approach to addressing big data challenges through what it calls the "Big Data ABCs" - analytics, bandwidth, and content. This allows customers to gain insights from massive data sets, move data quickly for high-performance applications, and store large amounts of content for long periods without increasing complexity. NetApp provides solutions to help enterprises take advantage of big data and turn it into business value.
Drinking from the Fire Hose: Practical Approaches to Big Data Preparation and...Inside Analysis
The Briefing Room with Robin Bloor and Pervasive Software
Slides from the Live Webcast on May 1, 2012
The old methods of delivering data for analysts and other business users will simply not scale to meet new demands. Hadoop is rapidly emerging as a powerful and economic platform for storing and processing Big Data. And yet, the biggest obstacle to implementing Hadoop solutions is the scarcity of Hadoop programming skills.
Check out this episode of The Briefing Room to learn from veteran Analyst Robin Bloor, who will explain why modern information architectures must embrace the new, massively parallel world of computing as it relates to several enterprise roles: traditional business analysts, data scientists, and line-of-business workers. He'll be briefed by David Inbar and Jim Falgout of Pervasive Software, who will explain how Pervasive RushAnalyzer™ was designed to accommodate the new reality of Big Data.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
Course 8 : How to start your big data project by Eric Rodriguez Betacowork
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
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"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.
It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.
This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.
No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
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For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
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!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
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.