Does it make sense to use Google App Engine as a quick prototyping environment for Big Data use cases? It would avoid all the hassles of setting up Hadoop and its bestiary.
The answer is a definite "maybe".
Map Reduce is a simple programming model that is well-suited for distributed computing. Hadoop is an open-source implementation of MapReduce that can run on large clusters of commodity hardware. Amazon Elastic MapReduce (EMR) provides a hosted Hadoop service that simplifies using MapReduce without needing to deploy and manage your own Hadoop cluster. The document discusses using EMR to analyze Facebook data at scale through examples like word counting and analyzing likes.
The document provides an overview of the Hadoop ecosystem. It introduces Hadoop and its core components, including MapReduce and HDFS. It describes other related projects like HBase, Pig, Hive, Mahout, Sqoop, Flume and Nutch that provide data access, algorithms, and data import capabilities to Hadoop. The document also discusses hosted Hadoop frameworks and the major Hadoop providers.
This document provides an introduction to the Pig analytics platform for Hadoop. It begins with an overview of big data and Hadoop, then discusses the basics of Pig including its data model, language called Pig Latin, and components. Key points made are that Pig provides a high-level language for expressing data analysis processes, compiles queries into MapReduce programs for execution, and allows for easier programming than lower-level systems like Java MapReduce. The document also compares Pig to SQL and Hive, and demonstrates visualizing Pig jobs with the Twitter Ambrose tool.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
The Zoo Expands: Labrador *Loves* Elephant, Thanks to HamsterMilind Bhandarkar
The document summarizes Milind Bhandarkar's work developing Hamster, a system for running MPI applications on Hadoop YARN. Some key points:
- Hamster allows MPI applications to run alongside Hadoop dataflow jobs on the same cluster managed by YARN. It implements an MPI runtime on top of YARN.
- Hamster's design leverages OpenMPI's strengths while allowing it to integrate with YARN. It includes an application master, node service, and scheduler component.
- Performance tests show Hamster has low overhead and scales well for large MPI jobs. It introduces only a small performance penalty compared to running MPI natively with OpenMPI.
- Example results are shown
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
Map Reduce is a simple programming model that is well-suited for distributed computing. Hadoop is an open-source implementation of MapReduce that can run on large clusters of commodity hardware. Amazon Elastic MapReduce (EMR) provides a hosted Hadoop service that simplifies using MapReduce without needing to deploy and manage your own Hadoop cluster. The document discusses using EMR to analyze Facebook data at scale through examples like word counting and analyzing likes.
The document provides an overview of the Hadoop ecosystem. It introduces Hadoop and its core components, including MapReduce and HDFS. It describes other related projects like HBase, Pig, Hive, Mahout, Sqoop, Flume and Nutch that provide data access, algorithms, and data import capabilities to Hadoop. The document also discusses hosted Hadoop frameworks and the major Hadoop providers.
This document provides an introduction to the Pig analytics platform for Hadoop. It begins with an overview of big data and Hadoop, then discusses the basics of Pig including its data model, language called Pig Latin, and components. Key points made are that Pig provides a high-level language for expressing data analysis processes, compiles queries into MapReduce programs for execution, and allows for easier programming than lower-level systems like Java MapReduce. The document also compares Pig to SQL and Hive, and demonstrates visualizing Pig jobs with the Twitter Ambrose tool.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
The Zoo Expands: Labrador *Loves* Elephant, Thanks to HamsterMilind Bhandarkar
The document summarizes Milind Bhandarkar's work developing Hamster, a system for running MPI applications on Hadoop YARN. Some key points:
- Hamster allows MPI applications to run alongside Hadoop dataflow jobs on the same cluster managed by YARN. It implements an MPI runtime on top of YARN.
- Hamster's design leverages OpenMPI's strengths while allowing it to integrate with YARN. It includes an application master, node service, and scheduler component.
- Performance tests show Hamster has low overhead and scales well for large MPI jobs. It introduces only a small performance penalty compared to running MPI natively with OpenMPI.
- Example results are shown
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
Hive on Tez provides significant performance improvements over Hive on MapReduce by leveraging Apache Tez for query execution. Key features of Hive on Tez include vectorized processing, dynamic partitioned hash joins, and broadcast joins which avoid unnecessary data writes to HDFS. Test results show Hive on Tez queries running up to 100x faster on datasets ranging from terabytes to petabytes in size. Hive on Tez also handles concurrency well, with the ability to run 20 queries concurrently on a 30TB dataset and finish within 27.5 minutes.
Hadoop in Practice (SDN Conference, Dec 2014)Marcel Krcah
You sit on a big pile of data and want to know how to leverage it in your company? Interested in use-cases, examples and practical demos about the full Hadoop stack? Looking for big-data inspiration?
In this talk we will cover:
- Use-cases how implementing a Hadoop stack in TheNewMotion drastically helped us, software engineers, with our everyday challenges. And how Hadoop enables our management team, marketing and operations to become more data-driven.
- Practical introduction into our data warehouse, analytical and visualization stack: Apache Pig, Impala, Hue, Apache Spark, IPython notebook and Angular with D3.js.
- Easy deployment of the Hadoop stack to the cloud.
- Hermes - our homegrown command-line tool which helps us automate data-related tasks.
- Examples of exciting machine learning challenges that we are currently tackling
- Hadoop with Azure and Microsoft stack.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.
The document discusses Apache Tez, a framework for building data processing applications on Hadoop. It provides an introduction to Tez and describes key features like expressing computations as directed acyclic graphs (DAGs), container reuse, dynamic parallelism, integration with YARN timeline service, and recovery from failures. The document also outlines improvements to Tez around performance, debuggability, and status/roadmap.
Apache Hadoop project, and the Hadoop ecosystem has been designed be extremely flexible, and extensible. HDFS, Yarn, and MapReduce combined have more that 1000 configuration parameters that allow users to tune performance of Hadoop applications, and more importantly, extend Hadoop with application-specific functionality, without having to modify any of the core Hadoop code.
In this talk, I will start with simple extensions, such as writing a new InputFormat to efficiently process video files. I will provide with some extensions that boost application performance, such as optimized compression codecs, and pluggable shuffle implementations. With refactoring of MapReduce framework, and emergence of YARN, as a generic resource manager for Hadoop, one can extend Hadoop further by implementing new computation paradigms.
I will discuss one such computation framework, that allows Message Passing applications to run in the Hadoop cluster alongside MapReduce. I will conclude by outlining some of our ongoing work, that extends HDFS, by removing namespace limitations of the current Namenode implementation.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of petabytes of data. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. Many large companies use Hadoop for applications such as log analysis, web indexing, and data mining of large datasets.
This document provides an overview of Hadoop, MapReduce, and HDFS. It discusses how Hadoop uses a cluster of commodity hardware and HDFS to reliably store and process large amounts of data in a distributed manner. MapReduce is the programming model used by Hadoop to process data in parallel across nodes. The document describes the core Hadoop modules and architecture, how HDFS stores and retrieves data blocks, and how MapReduce distributes work and aggregates results. Examples of using MapReduce for word counting and inverted indexes are also presented.
This document introduces Pig, an open source platform for analyzing large datasets that sits on top of Hadoop. It provides an example of using Pig Latin to find the top 5 most visited websites by users aged 18-25 from user and website data. Key points covered include who uses Pig, how it works, performance advantages over MapReduce, and upcoming new features. The document encourages learning more about Pig through online documentation and tutorials.
Pig programming is more fun: New features in Pigdaijy
In the last year, we add lots of new language features into Pig. Pig programing is much more easier than before. With Pig Macro, we can write functions for Pig and we can modularize Pig program. Pig embedding allow use to embed Pig statement into Python and make use of rich language features of Python such as loop and branch. Java is no longer the only choice to write Pig UDF, we can write UDF in Python, Javascript and Ruby. Nested foreach and cross gives us more ways to manipulate data, which is not possible before. We also add tons of syntax sugar to simplify the Pig syntax. For example, direct syntax support for map, tuple and bag, project range expression in foreach, etc. We also revive the support for illustrate command to ease the debugging. In this paper, I will give an overview of all these features and illustrate how to use these features to program more efficiently in Pig. I will also give concrete example to demonstrate how Pig language evolves overtime with these language improvements.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
The document discusses functional programming concepts and their application to big data problems. It provides an overview of functional programming foundations and languages. Key functional programming concepts discussed include first-class functions, pure functions, recursion, and immutability. These concepts are well-suited for data-centric applications like Hadoop MapReduce. The document also presents a case study comparing an imperative approach to a transaction processing problem to a functional approach, showing that the functional version was faster and avoided side effects.
Getting started with Hadoop, Hive, and Elastic MapReduceobdit
1) The document provides an overview of tools for distributed computing including MapReduce, Hadoop, Hive, and Elastic MapReduce.
2) It discusses getting started with Elastic MapReduce using Python with mrjob or the AWS command line and challenges with getting started with Hive.
3) Potential pitfalls with EMR are also outlined such as JVM memory issues and problems with multiple small output files.
"Big Data" is a much-hyped term nowadays in Business Computing. However, the core concept of collaborative environments conducting experiments over large shared data repositories has existed for decades. In this talk, I will outline how recent advances in Cloud Computing, Big Data processing frameworks, and agile application development platforms enable Data Intensive Cloud Applications. I will provide a brief history of efforts in building scalable & adaptive run-time environments, and the role these runtime systems will play in new Cloud Applications. I will present a vision for cloud platforms for science, where data-intensive frameworks such as Apache Hadoop will play a key role.
Hadoop and Hive are used at Facebook for large scale data processing and analytics using commodity hardware and open source software. Hive provides an SQL-like interface to query large datasets stored in Hadoop and translates queries into MapReduce jobs. It is used for daily/weekly data aggregations, ad-hoc analysis, data mining, and other tasks using datasets exceeding petabytes in size stored on Hadoop clusters.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
- Hadoop was created to allow processing of large datasets in a distributed, fault-tolerant manner. It was originally developed by Doug Cutting and Mike Cafarella at Nutch in response to the growing amounts of data and computational needs at Google and other companies.
- The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for distributed processing. It also includes utilities like Hadoop Common for file system access and other basic functionality.
- Hadoop's goals were to process multi-petabyte datasets across commodity hardware in a reliable, flexible and open source way. It assumes failures are expected and handles them to provide fault tolerance.
OpenLSH - a framework for locality sensitive hashingJ Singh
The document discusses limitations of the k-means clustering algorithm and proposes alternatives like locality-sensitive hashing (LSH) for clustering large document collections. LSH hashes documents into "buckets" based on similarity so that similar documents are hashed to the same buckets, allowing efficient retrieval of nearest neighbors. The document demonstrates LSH using minhashing, which represents documents as sets of "shingles" or fragments, and hashes the minimum value found. It also describes an open-source implementation of LSH called OpenLSH that works with large-scale databases like Cassandra.
Tableau reseller partner in Australia Bilytica Best business Intelligence com...Carie John
Email: info@bilytica.com
Bilytica provides best in class services in Business Intelligence, Data-warehousing, Data Governance, Big Data management, Enterprise Applications, Enterprise Performance Management, Mobile Applications & Gaming and Business Consulting Services. Being a Tableau preferred reseller and consulting partner for Middle East, Europe, Turkey, Asia & Russia. Bilytica has helped 500+ small to large enterprises in Tableau implementation and training. We provide End to end Tableau consulting and training services including Tableau Proof of Concepts, Tableau Software license sales ,Tableau dashboard design Services , Onsite and remote Tableau consulting ,Customized onsite Tableau training , Tableau Server hosting ,Tableau integration services, Tableau advanced analytic & Tableau managed services.
Hive on Tez provides significant performance improvements over Hive on MapReduce by leveraging Apache Tez for query execution. Key features of Hive on Tez include vectorized processing, dynamic partitioned hash joins, and broadcast joins which avoid unnecessary data writes to HDFS. Test results show Hive on Tez queries running up to 100x faster on datasets ranging from terabytes to petabytes in size. Hive on Tez also handles concurrency well, with the ability to run 20 queries concurrently on a 30TB dataset and finish within 27.5 minutes.
Hadoop in Practice (SDN Conference, Dec 2014)Marcel Krcah
You sit on a big pile of data and want to know how to leverage it in your company? Interested in use-cases, examples and practical demos about the full Hadoop stack? Looking for big-data inspiration?
In this talk we will cover:
- Use-cases how implementing a Hadoop stack in TheNewMotion drastically helped us, software engineers, with our everyday challenges. And how Hadoop enables our management team, marketing and operations to become more data-driven.
- Practical introduction into our data warehouse, analytical and visualization stack: Apache Pig, Impala, Hue, Apache Spark, IPython notebook and Angular with D3.js.
- Easy deployment of the Hadoop stack to the cloud.
- Hermes - our homegrown command-line tool which helps us automate data-related tasks.
- Examples of exciting machine learning challenges that we are currently tackling
- Hadoop with Azure and Microsoft stack.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.
The document discusses Apache Tez, a framework for building data processing applications on Hadoop. It provides an introduction to Tez and describes key features like expressing computations as directed acyclic graphs (DAGs), container reuse, dynamic parallelism, integration with YARN timeline service, and recovery from failures. The document also outlines improvements to Tez around performance, debuggability, and status/roadmap.
Apache Hadoop project, and the Hadoop ecosystem has been designed be extremely flexible, and extensible. HDFS, Yarn, and MapReduce combined have more that 1000 configuration parameters that allow users to tune performance of Hadoop applications, and more importantly, extend Hadoop with application-specific functionality, without having to modify any of the core Hadoop code.
In this talk, I will start with simple extensions, such as writing a new InputFormat to efficiently process video files. I will provide with some extensions that boost application performance, such as optimized compression codecs, and pluggable shuffle implementations. With refactoring of MapReduce framework, and emergence of YARN, as a generic resource manager for Hadoop, one can extend Hadoop further by implementing new computation paradigms.
I will discuss one such computation framework, that allows Message Passing applications to run in the Hadoop cluster alongside MapReduce. I will conclude by outlining some of our ongoing work, that extends HDFS, by removing namespace limitations of the current Namenode implementation.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of petabytes of data. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. Many large companies use Hadoop for applications such as log analysis, web indexing, and data mining of large datasets.
This document provides an overview of Hadoop, MapReduce, and HDFS. It discusses how Hadoop uses a cluster of commodity hardware and HDFS to reliably store and process large amounts of data in a distributed manner. MapReduce is the programming model used by Hadoop to process data in parallel across nodes. The document describes the core Hadoop modules and architecture, how HDFS stores and retrieves data blocks, and how MapReduce distributes work and aggregates results. Examples of using MapReduce for word counting and inverted indexes are also presented.
This document introduces Pig, an open source platform for analyzing large datasets that sits on top of Hadoop. It provides an example of using Pig Latin to find the top 5 most visited websites by users aged 18-25 from user and website data. Key points covered include who uses Pig, how it works, performance advantages over MapReduce, and upcoming new features. The document encourages learning more about Pig through online documentation and tutorials.
Pig programming is more fun: New features in Pigdaijy
In the last year, we add lots of new language features into Pig. Pig programing is much more easier than before. With Pig Macro, we can write functions for Pig and we can modularize Pig program. Pig embedding allow use to embed Pig statement into Python and make use of rich language features of Python such as loop and branch. Java is no longer the only choice to write Pig UDF, we can write UDF in Python, Javascript and Ruby. Nested foreach and cross gives us more ways to manipulate data, which is not possible before. We also add tons of syntax sugar to simplify the Pig syntax. For example, direct syntax support for map, tuple and bag, project range expression in foreach, etc. We also revive the support for illustrate command to ease the debugging. In this paper, I will give an overview of all these features and illustrate how to use these features to program more efficiently in Pig. I will also give concrete example to demonstrate how Pig language evolves overtime with these language improvements.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
The document discusses functional programming concepts and their application to big data problems. It provides an overview of functional programming foundations and languages. Key functional programming concepts discussed include first-class functions, pure functions, recursion, and immutability. These concepts are well-suited for data-centric applications like Hadoop MapReduce. The document also presents a case study comparing an imperative approach to a transaction processing problem to a functional approach, showing that the functional version was faster and avoided side effects.
Getting started with Hadoop, Hive, and Elastic MapReduceobdit
1) The document provides an overview of tools for distributed computing including MapReduce, Hadoop, Hive, and Elastic MapReduce.
2) It discusses getting started with Elastic MapReduce using Python with mrjob or the AWS command line and challenges with getting started with Hive.
3) Potential pitfalls with EMR are also outlined such as JVM memory issues and problems with multiple small output files.
"Big Data" is a much-hyped term nowadays in Business Computing. However, the core concept of collaborative environments conducting experiments over large shared data repositories has existed for decades. In this talk, I will outline how recent advances in Cloud Computing, Big Data processing frameworks, and agile application development platforms enable Data Intensive Cloud Applications. I will provide a brief history of efforts in building scalable & adaptive run-time environments, and the role these runtime systems will play in new Cloud Applications. I will present a vision for cloud platforms for science, where data-intensive frameworks such as Apache Hadoop will play a key role.
Hadoop and Hive are used at Facebook for large scale data processing and analytics using commodity hardware and open source software. Hive provides an SQL-like interface to query large datasets stored in Hadoop and translates queries into MapReduce jobs. It is used for daily/weekly data aggregations, ad-hoc analysis, data mining, and other tasks using datasets exceeding petabytes in size stored on Hadoop clusters.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
- Hadoop was created to allow processing of large datasets in a distributed, fault-tolerant manner. It was originally developed by Doug Cutting and Mike Cafarella at Nutch in response to the growing amounts of data and computational needs at Google and other companies.
- The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for distributed processing. It also includes utilities like Hadoop Common for file system access and other basic functionality.
- Hadoop's goals were to process multi-petabyte datasets across commodity hardware in a reliable, flexible and open source way. It assumes failures are expected and handles them to provide fault tolerance.
OpenLSH - a framework for locality sensitive hashingJ Singh
The document discusses limitations of the k-means clustering algorithm and proposes alternatives like locality-sensitive hashing (LSH) for clustering large document collections. LSH hashes documents into "buckets" based on similarity so that similar documents are hashed to the same buckets, allowing efficient retrieval of nearest neighbors. The document demonstrates LSH using minhashing, which represents documents as sets of "shingles" or fragments, and hashes the minimum value found. It also describes an open-source implementation of LSH called OpenLSH that works with large-scale databases like Cassandra.
Tableau reseller partner in Australia Bilytica Best business Intelligence com...Carie John
Email: info@bilytica.com
Bilytica provides best in class services in Business Intelligence, Data-warehousing, Data Governance, Big Data management, Enterprise Applications, Enterprise Performance Management, Mobile Applications & Gaming and Business Consulting Services. Being a Tableau preferred reseller and consulting partner for Middle East, Europe, Turkey, Asia & Russia. Bilytica has helped 500+ small to large enterprises in Tableau implementation and training. We provide End to end Tableau consulting and training services including Tableau Proof of Concepts, Tableau Software license sales ,Tableau dashboard design Services , Onsite and remote Tableau consulting ,Customized onsite Tableau training , Tableau Server hosting ,Tableau integration services, Tableau advanced analytic & Tableau managed services.
2016 Standardization of Laboratory Test Coding - PHI ConferenceMegan Sawchuk
1) Several projects were presented that aim to standardize laboratory test coding through collaboration to improve semantic interoperability.
2) The LOINC Common Name Project developed rules to establish common names for laboratory tests to enhance understanding and usability.
3) The LOINC Order Code Value Set Project identified codes for commonly performed tests to facilitate computerized test ordering between EHRs and labs.
4) CDC has taken steps to standardize coding of its laboratory developed tests so results can be reported across sites and to support public health surveillance.
Whitepaper2012 "Virtual Laboratory for Analytic Geometry" UNAMmetagraphos
This report provides some background and results of an educative technology project that was developed at the School of Engineering of the National Autonomous University of Mexico (UNAM).
Tableau reseller partner in Cape Verde Bilytica Best business Intelligence Co...Carie John
Bilytica is an analytics and software company that provides business intelligence, data warehousing, mobile applications, and ERP solutions. It has offices in Australia, Turkey, Pakistan, and the United States. The document discusses Bilytica's portfolio of products and services, including Tableau insights, Erpisto ERP software, AppsOut mobile applications, and CloudPital healthcare information systems. It also shares client testimonials praising Bilytica's work and solutions.
Wolfgang Hoeck presented on interactive visual data analytics at Amgen. He discussed how Amgen uses visualizations to analyze complex scientific data from sources like gene expression studies and compound profiling. Hoeck described the process of integrating data from different sources and formats into interactive visualizations to enable exploration and analysis of relationships within the data. He emphasized that interactive visualizations are key to making complex biological and chemical data understandable and accessible to scientists.
Checking in on Healthcare Data AnalyticsCybera Inc.
Data science and the use of big data in healthcare delivery could revolutionize the field by decreasing costs and vastly improving efficiency and outcomes. There is an abundance of healthcare data in Canada, but it is mostly siloed and difficult to access due to privacy and security challenges.
INCREASING LABORATORY EFFICIENCY AND VALUE OF LABORATORY DATA BY MAXIMISING ...Keynetix
Managing laboratory test data can be time consuming and expensive, especially if inefficient systems are resulting in double or triple entry of data. The introduction and requirement to produce AGS data on the majority of construction projects in the UK, coupled with tough economic conditions have forced UK laboratories to increase their efficiency dramatically.
This paper and accompanying presentation will discuss the merits of AGS data, introduce the two golden rules for data entry and illustrate how AGS data and the golden rules can help laboratories increase their efficiency if implemented correctly. Finally, the benefits will be demonstrated using KeyLAB, the UK's leading geotechnical laboratory management package.
Powerpoint presentation given at conference
This document summarizes a presentation exploring the role of information technology systems in preventing and managing pre-analytic laboratory errors. The presentation aimed to establish how IT systems influence errors. Interviews found that while IT systems can help reduce errors, human and organizational factors also play a role. Conclusions determined that the pre-analytic stage is critical for quality, and IT systems can help reduce errors if integrated properly considering organizational contexts and limitations. Future research recommendations include further exploring the impacts of IT systems and optimizing their use.
This document outlines 10 essential ingredients for streamlining process improvement in a laboratory. It discusses: 1) the need for organizational leadership and alignment; 2) understanding customer needs; 3) using a rigorous methodology like DMAIC; 4) understanding testing demands through data analysis; 5) optimizing space design; 6) ensuring proper instrumentation; 7) eliminating bottlenecks; 8) establishing metrics; 9) reducing variation; and 10) using visualization to improve performance. Case studies and examples from a pediatric laboratory are provided to illustrate challenges in siloed organizations and how applying these 10 ingredients can transform processes and productivity.
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
Advanced laboratory analytics can provide a disruptive solution for health systems facing challenges under value-based care models. Laboratory data is well-suited for advanced analytics due to its timeliness, structured format, ubiquity across settings and providers, and predictive potential. Laboratory-based predictive algorithms and clinical decision support tools can help optimize outcomes like readmissions, adverse events, costs, and disease management. By leveraging laboratory data and analytics, health systems can better manage patient populations, make personalized medical decisions, and support value-based care goals of improving quality while reducing costs.
Spotfire is used across many departments in Amgen Research, including high throughput screening, research informatics, therapeutic areas, and more. It allows for interactive data exploration through visualizations, zooming, and linking multiple data sources. The Spotfire Cockpit provides guided workflows and visualizations for tasks like exploring compound data in lead discovery and toxicology. Amgen is also interested in ontologies to represent relationships between targets, diseases, anatomy, and more to aid in data exploration and knowledge discovery.
The Evolution of Laboratory Data Systems: Replacing Paper, Streamlining Proce...IDBS
The laboratory has become an increasingly electronic environment. It’s not just that the volume of data is greater than ever before, it’s also being generated at ever-increasing speeds. As companies move towards a fully integrated lab environment there are benefits and pitfalls along the way. Successful projects start with a solid foundation, and keep a clear vision in mind.
Clinical data analytics is an exciting new area of healthcare data analytics. This presentation presents a brief overview of the topic as an introduction and whetting the curiosity of the reader.
This document discusses several topics related to big data in healthcare, including:
1) Using existing clinical records and health data to improve care delivery through better analysis and insights.
2) The need for healthcare to embrace digital technologies and use data more effectively, rather than just increasing spending.
3) Examples of digital health projects in Australia, including analyzing clinical notes, nursing handovers, and sports performance tracking.
Basics of laboratory internal quality control, Ola Elgaddar, 2012Ola Elgaddar
Total Quality Management (TQM) is a continuous approach to improve quality and performance. It requires integrating quality functions throughout an organization with involvement from management, employees, suppliers, and customers. For medical laboratories, quality control has three main stages - pre-analytical, analytical, and post-analytical. Analytical quality control involves internal quality control (IQC) using control materials and external quality assessment (EQA) to monitor quality and compare results between laboratories. IQC follows procedures like plotting daily control results on Levey-Jennings charts and evaluating them using Westgard rules to detect errors.
Quality control in the medical laboratoryAdnan Jaran
This document discusses quality control in medical laboratories. It emphasizes that quality is achieved through determining customer requirements, ensuring necessary resources are available, planning management procedures, training staff, undertaking tasks correctly, taking corrective action when errors occur, conducting regular reviews and audits, and total management commitment. The quality assurance cycle involves various steps from patient preparation to reporting. Achieving high quality requires addressing all aspects of the laboratory, including organization, personnel, equipment, purchasing, process control, information management, documents, occurrence management, assessment, process improvement, customer service, and facilities/safety. The goal is to detect and prevent errors through a quality management system.
This document discusses descriptive and inferential statistics used in nursing research. It defines key statistical concepts like levels of measurement, measures of central tendency, descriptive versus inferential statistics, and commonly used statistical tests. Nominal, ordinal, interval and ratio are the four levels of measurement, with ratio allowing the most data manipulation. Descriptive statistics describe sample data while inferential statistics allow estimating population parameters and testing hypotheses. Common descriptive statistics include mean, median and mode, while common inferential tests are t-tests, ANOVA, chi-square and correlation. Type I errors incorrectly reject the null hypothesis.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
The document discusses big data in healthcare, where it currently stands and its future potential uses. It explains that while big data is not necessary for most healthcare organizations currently, emerging technologies like wearable devices and whole genome sequencing will generate large amounts of diverse data requiring big data solutions. It also outlines some barriers to big data adoption in healthcare like a lack of security and need for data science expertise. The document envisions future applications of big data like predictive analytics, using additional data sources to better predict patient outcomes and needs.
The document provides an overview of an experimentation platform built on Hadoop. It discusses experimentation workflows, why Hadoop was chosen as the framework, the system architecture, and challenges faced and lessons learned. Key points include:
- The platform supports A/B testing and reporting on hundreds of metrics and dimensions for experiments.
- Data is ingested from various sources and stored in Hadoop for analysis using technologies like Hive, Spark, and Scoobi.
- Challenges included optimizing joins and jobs for large datasets, addressing data skew, and ensuring job resiliency. Tuning configuration parameters and job scheduling helped improve performance.
Experimentation plays a vital role in business growth at eBay by providing valuable insights and prediction on how users will reach to changes made to the eBay website and applications. On a given day, eBay has several hundred experiments running at the same time. Our experimentation data processing pipeline handles billions of rows user behavioral and transactional data per day to generate detailed reports covering 100+ metrics over 50 dimensions.
In this session, we will share our journey of how we moved this complex process from Data warehouse to Hadoop. We will give an overview of the experimentation platform and data processing pipeline. We will highlight the challenges and learnings we faced implementing this platform in Hadoop and how this transformation led us to build a scalable, flexible and reliable data processing workflow in Hadoop. We will cover our work done on performance optimizations, methods to establish resilience and configurability, efficient storage formats and choices of different frameworks used in the pipeline.
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
This document discusses moving machine learning models from prototype to production. It outlines some common problems with the current workflow where moving to production often requires redevelopment from scratch. Some proposed solutions include using notebooks as APIs and developing analytics that are accessed via an API. It also discusses different data science platforms and architectures for building end-to-end machine learning systems, focusing on flexibility, security, testing and scalability for production environments. The document recommends a custom backend integrated with Spark via APIs as the best approach for the current project.
MongoDB for Spatio-Behavioral Data Analysis and VisualizationMongoDB
T-Sciences offers iSpatial - a web-based Spatial Data Infrastructure (SDI) to enable integration of third-party applications with geo-visualization tools. The iHarvest tool further enables the mining and analysis of data aggregated in the iSpatial platform for spatio-temporal behavior modelling. At the back-end of both products is MongoDB, providing fundamental framework capabilities for the spatial indexing and data analysis techniques. Come witness how Thermopylae Sciences and Technology leveraged the aggregation framework, and extended the spatial capabilities of MongoDB to tackle dynamic spatio-behavioral data at scale.
The document discusses JRuby on Google App Engine, including key features of App Engine, quotas and billing, limitations, the current issues with JRuby on App Engine, App Engine gems, the development environment, deployment process, APIs, and milestones in the development of JRuby on App Engine. It also includes a short biography and discussion of learning experiences from building an iPhone app that uses App Engine and JRuby as a backend.
The document discusses how Pivotal uses the Python data science stack in real engagements. It provides an overview of Pivotal's data science toolkit, including PL/Python for running Python code directly in the database and MADlib for parallel in-database machine learning. The document then demonstrates how Pivotal works with large enterprise customers who have large amounts of structured and unstructured data and want to perform interactive data analysis and become more data-driven.
This document summarizes a presentation on using SQL Server Integration Services (SSIS) with HDInsight. It introduces Tillmann Eitelberg and Oliver Engels, who are experts on SSIS and HDInsight. The agenda covers traditional ETL processes, challenges of big data, useful Apache Hadoop components for ETL, clarifying statements about Hadoop and ETL, using Hadoop in the ETL process, how SSIS is more than just an ETL tool, tools for working with HDInsight, getting started with Azure HDInsight, and using SSIS to load and transform data on HDInsight clusters.
Guider: An Integrated Runtime Performance Analyzer on AGLPeace Lee
Guider is an integrated runtime performance analyzer for Linux that collects system resource and task data in real-time. It traces numerous system operations and visualizes complex performance data. Guider provides highly readable reports and debugging features to help optimize performance. It is open source, system-wide, easy to use, accurate, and light on system resources. Guider can monitor and collect system stats, trace threads, functions, and system calls, and control tasks for testing. Future work includes real-time user-level function tracing and a GUI client for remote control and visualization.
The document summarizes Lancaster University Library's experience using Ex Libris Alma Analytics. It notes that basic reporting is fast and data exploration is good. However, there are also problems, such as a limit on the number of rows that can be downloaded, certain data not being available, and daily updates previously taking a long time. The library is working to integrate additional data sources into a dashboard to provide a bigger picture of library data and usage beyond just Alma Analytics.
Apache Pig is a high-level data flow platform for executing MapReduce programs on Hadoop. The language used for Pig is called Pig Latin. Pig scripts get converted into MapReduce jobs that are executed on data stored in HDFS. Pig can handle structured, semi-structured, or unstructured data and store results back in HDFS. Common Pig operations include joining, sorting, filtering, grouping, and using built-in and user-defined functions.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
This document provides an overview of Capital One's plans to introduce Hadoop and discusses several proof of concepts (POCs) that could be developed. It summarizes the history and practices of using Hadoop at other companies like LinkedIn, Netflix, and Yahoo. It then outlines possible POCs for Hadoop distributions, ETL/analytics frameworks, performance testing, and developing a scaling layer. The goal is to contribute open source code and help with Capital One's transition to using Hadoop in production.
Data Pipelines with Python - NWA TechFest 2017Casey Kinsey
This document discusses data pipelines and provides examples of how to design and implement them using Python tools. It defines a data pipeline as a set of dependent operations that move data from an input source to an output target. Common uses of pipelines include data aggregation, cleansing, copying, analytics processing, and AI modeling. Operations within a pipeline can be executed sequentially, concurrently using threads, or in parallel across multiple machines. The document recommends designing operations to be atomic and idempotent. It presents ETL and periodic/event-driven workflows as common pipeline patterns and introduces Python tools like Celery, Luigi, and Airflow that can be used to build scalable data pipelines.
O365Engage17 - How to Automate SharePoint Provisioning with PNP FrameworkNCCOMMS
This document discusses how to automate SharePoint provisioning using the PnP Provisioning Framework. It begins with an introduction to the speaker and then defines the PnP Provisioning Engine as an open source framework for easily doing remote provisioning. It describes the engine's capabilities for automated remote provisioning, site template generation and extraction. The remainder of the document discusses how to use the PnP Provisioning Engine's main features like templates, importing and exporting templates, using resource files, and the available PowerShell cmdlets.
Data Labs supports LINE services by performing high-level data analysis and machine learning model development using their Hadoop data lake. The machine learning lifecycle involves many steps beyond just model training, including data collection, preprocessing, deployment, and monitoring. LINE's platform provides the necessary infrastructure to efficiently perform each step of the lifecycle, allowing for rapid continuous development and experimentation through tools like HDFS, Kubernetes, Jupyter notebooks, and CI/CD pipelines.
Data analytics in the cloud with Jupyter notebooks.Graham Dumpleton
Jupyter Notebooks provide an interactive computational environment, in which you can combine Python code, rich text, mathematics, plots and rich media. It provides a convenient way for data analysts to explore, capture and share their research.
Numerous options exist for working with Jupyter Notebooks, including running a Jupyter Notebook instance locally or by using a Jupyter Notebook hosting service.
This talk will provide a quick tour of some of the more well known options available for running Jupyter Notebooks. It will then look at custom options for hosting Jupyter Notebooks yourself using public or private cloud infrastructure.
An in-depth look at how you can run Jupyter Notebooks in OpenShift will be presented. This will cover how you can directly deploy a Jupyter Notebook server image, as well as how you can use Source-to-Image (S2I) to create a custom application for your requirements by combining an existing Jupyter Notebook server image with your own notebooks, additional code and research data.
Specific use cases around Jupyter Notebooks which will be explored will include individual use, team use within an organisation, and class room environments for teaching. Other issues which will be covered include importing of notebooks and data into an environment, storing data using persistent volumes and other forms of centralised storage.
As an example of the possibilities of using Jupyter Notebooks with a cloud, it will be shown how you can easily use OpenShift to set up a distributed parallel computing cluster using ‘ipyparallel’ and use it in conjunction with a Jupyter Notebook.
The document discusses the Arabidopsis Information Portal (AIP), a new open resource for sharing and analyzing Arabidopsis data. The AIP aims to develop a community-driven web portal with analysis tools and user data spaces. It will integrate diverse datasets through federation and maintain the Col-0 genome annotation. The AIP architecture uses InterMine, JBrowse and other tools, and provides APIs and an app store for developing interactive analysis applications. A developer workshop is scheduled for November 2014 to involve the community.
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing
This document discusses using locality sensitive hashing (LSH) to enable large-scale similarity searches of massive datasets. LSH works by hashing similar objects into the same "buckets", allowing efficient discovery of similar items by only comparing objects within a small number of buckets. The document outlines how LSH could be used to find similar users on Facebook based on shared interests, and describes OpenLSH, an open-source Python framework for implementing LSH on Google App Engine using a MapReduce architecture.
The document discusses Google App Engine, a platform as a service (PaaS) that allows developers to focus on development rather than operations. It presents Google App Engine as having a "virtual raised floor" with the IDE above the floor and website/deployment system below. It provides an overview of Google App Engine's history and supported languages/data stores. It also summarizes code samples for a guestbook application and MapReduce workflow on Google App Engine.
Mining of massive datasets using locality sensitive hashing (LSH)J Singh
This document discusses using locality sensitive hashing (LSH) to solve large-scale search problems by clustering similar data points together. It presents an example of using LSH to find Facebook friends with similar interests. The key steps are: (1) representing each user as a vector of interests and computing minhashes, (2) clustering users into buckets based on minhash similarity, and (3) comparing a candidate to others in their bucket to find nearest neighbors. The performance of LSH involves tuning parameters like the number of minhashes and bands to balance false positives and negatives. Implementing LSH on MapReduce can make it scalable to large datasets.
Data Analytic Technology Platforms: Options and TradeoffsJ Singh
This document discusses options for data analytic technology platforms to address big data problems. It begins by distinguishing between problems that truly involve big data versus just large data problems. Examples of big data problems include recommendations, financial analysis, internet security monitoring, social media network analysis, genomics, and sensor data. The key characteristics of big data problems are that the data sets are too large to download, data is generated rapidly requiring near real-time analysis, and the problems involve diverse data types. The document then outlines the governing principle for choosing a platform as processing needing to be close to the data due to data size. Examples of platforms used for different applications are discussed to illustrate this principle. The decision making process for choosing a platform is described as
Receiving data from a source that produces 5-10 GBytes per hour, and presenting analysis results as the data streams in has some interesting challenges.
We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time.
(Slides from my presentation at MongoDB Boston)
The document discusses NoSQL databases and MapReduce. It provides historical context on how databases were not adequate for the large amounts of data being accumulated from the web. It describes Brewer's Conjecture and CAP Theorem, which contributed to the rise of NoSQL databases. It then defines what NoSQL databases are, provides examples of different types, and discusses some large-scale implementations like Amazon SimpleDB, Google Datastore, and Hadoop MapReduce.
The document summarizes topics discussed in a database management systems lecture, including concurrency control techniques like intention locks, index locking, optimistic concurrency control using validation, and timestamp ordering algorithms. It also discusses multi-version concurrency control and challenges with commit in distributed databases using two phase commit and the Paxos algorithm. The lecture covers lock-based and optimistic approaches to concurrency control and managing concurrent transactions in a database system.
This document discusses database recovery techniques including undo logging, redo logging, and undo/redo logging.
Undo logging involves writing enough information to the log to allow rolling back uncommitted transactions after a failure. Redo logging writes log records to allow reapplying committed transactions not yet written to disk.
Undo/redo logging combines these approaches by writing both old and new values to the log, allowing flexible flushing of data pages before or after commit. It uses a two-pass recovery procedure of undoing uncommitted transactions followed by redoing committed ones.
Checkpoints are used to limit the portion of the log that needs to be processed during recovery by bracketing active transactions. Various checkpointing techniques like quies
The document discusses query optimization in database management systems. It covers converting SQL queries to logical and physical query plans, improving logical plans through algebraic transformations, and choosing the optimal physical query plan by considering the order of operations and join trees. The goal is to select the most efficient physical plan by estimating the size of relations and intermediate results.
The document discusses query execution in database management systems. It begins with an example query on a City, Country database and represents it in relational algebra. It then discusses different query execution strategies like table scan, nested loop join, sort merge join, and hash join. The strategies are compared based on their memory and disk I/O requirements. The document emphasizes that query execution plans can be optimized for parallelism and pipelining to improve performance.
CS 542 Putting it all together -- Storage ManagementJ Singh
The document provides an overview and plan for a lecture on database management systems. Key points include:
- By the second break, the lecture will cover storage hierarchies, secondary storage management, and system catalogs.
- After the second break, the topics will include data modeling and storage hierarchies.
- Storage hierarchies involve multiple storage levels from main memory to disk and beyond. The cost and performance of each level differs.
- Techniques like caching aim to keep frequently used data in faster storage levels like memory.
This document provides an overview of topics to be covered in a database management systems course, including parallel and distributed databases, NoSQL databases, and MapReduce. It discusses parallel databases and different architectures for distributed databases. It introduces several NoSQL databases like Amazon SimpleDB, Google BigTable, and HBase and describes their data models and implementations. It also provides details about MapReduce, including its programming model, implementation, optimizations, and statistics on its usage at Google. The next class meetings will include a mid-term exam, student presentations on assigned topics, and a proposal for each student's final project.
The document summarizes key topics in database integrity and performance, including:
- Primary and foreign key constraints to prevent duplicate and dangling tuples
- Attribute and tuple constraints to enforce data integrity
- Views to provide virtual subsets and joins of database relations
- Indexes to enable fast search through tables
The document discusses these concepts over multiple pages and provides examples to illustrate primary keys, foreign keys, constraints, views and indexing. It concludes by offering feedback on students' report proposals, emphasizing depth over breadth and a focus on design over implementation.
CS 542 Controlling Database Integrity and PerformanceJ Singh
This document summarizes a lecture on database integrity and performance. It discusses various techniques for ensuring database integrity, including primary key constraints to prevent duplicate tuples, foreign key constraints to prevent dangling references, and attribute constraints to prevent inconsistent attribute values. It also covers views, which allow querying virtual tables, and indexes to improve query performance by enabling faster searching. The document proposes discussing index structures and report topics at the next meeting.
This document discusses SQL queries and Datalog rules. It begins with examples of simple SQL queries on a BROWSER_TABLE relation. More complex queries are demonstrated using joins, subqueries, aggregation, and set operations. Transaction processing and ensuring isolation levels are covered. The document then introduces Datalog, a logical query language, and how its rules can extend SQL with recursion to express queries not possible in SQL alone. Key concepts in Datalog like the distinction between extensional and intensional databases, computing rules bottom-up and top-down, and ensuring safe rules are explained. Finally, examples are given of expressing Datalog rules and recursive queries using the SQL WITH clause.
This document provides information about a CS 542 Database Management Systems course. It introduces the instructor, discusses course content including SQL, relational algebra, database architecture and models. It also outlines course policies, computing options and plans for future topics like the relational model of data, data definition language, and data manipulation algebra.
Cloud Computing from an Entrpreneur's ViewpointJ Singh
Cloud computing allows users to access computing resources like servers and storage over the internet. It provides on-demand self-service, ubiquitous network access, resource pooling and rapid elasticity. Companies can start small without large capital expenditures and scale easily. Major players in cloud computing include Amazon, Google, Microsoft and IBM. Amazon EC2 allows users to launch virtual machines while S3 provides storage services. Google App Engine uses a virtual operating system and datastore for applications. Cloud computing enables massive parallelism for data-intensive tasks.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.