This document provides an overview of NoSQL databases. It defines NoSQL and compares it to SQL databases. It discusses the history and concepts behind several popular NoSQL databases like MongoDB, Cassandra, CouchDB, HBase, Amazon SimpleDB. It also provides examples of companies that use these NoSQL databases at large scale, such as Facebook, Twitter, Netflix, Yahoo.
Pearson International has chosen to build a new offering using the Sakai Collaboration and Learning Environment to support instructors and students in many different regions around the world, including Japan, Germany, France, Sweden and more. This demo will look at what is unique to the project and what Sakai offers "out of the box."
Using Sakai Site Archive for Good not EvilCris Holdorph
Sakai Site Archive allows exporting and importing Sakai sites to preserve disk space or maintain performance. The author modified Site Archive to support content exchange between different Sakai systems. Key lessons included assuming entities exist in the target system and handling linking between content. While intended for archiving existing sites, Site Archive can work for content exchange with some changes.
What can you do with this GeoServer thing? This talk covers some of the basic (and not so basic) ways to use GeoServer to publish your geospatial data and make it look great!
GeoServer made its first release in 2001 and has grown into an amazing, capable and diverse program. This also means the “feature list” is spread over years of release announcements, presentations, mailing list archives!
This presentations provides a whirlwind tour of GeoServer and everything it can do today!
This talk is a visual guide to the features of GeoServer. Are you just getting started with GeoServer, or considering it for the first time? Attend this talk and prioritize what you want to look into first. Are you an expert user who has been running GeoServer since Java 1.4? Attend this talk and see what tricks an optimisations you have been missing out on!
This document discusses MySQL's new Document Store and Node.JS API. It introduces the MySQL X DevAPI, which provides a high-level interface for developing applications that leverage both relational and document-style data through a common API. The API is supported across multiple languages by connectors like Node.JS. It allows mixing table and document queries, and uses components like the MySQL Router and X Plugin to interface with the database.
Connector/J Beyond JDBC: the X DevAPI for Java and MySQL as a Document StoreFilipe Silva
The document discusses Connector/J Beyond JDBC and the X DevAPI for Java and MySQL as a Document Store. It provides an agenda that includes an introduction to MySQL as a document store, an overview of the X DevAPI, and how the X DevAPI is implemented in Connector/J. The presentation aims to demonstrate the X DevAPI for developing CRUD-based applications and using MySQL as both a relational database and document store.
Slides semantic web and Drupal 7 NYCCamp 2012scorlosquet
This document summarizes a presentation about using semantic web technologies like RDFa, schema.org, and JSON-LD with Drupal 7. It discusses how Drupal 7 outputs RDFa by default and can be extended through contributed modules to support additional RDF formats, a SPARQL endpoint, schema.org mapping, and JSON-LD. Examples of semantic markup for events and people are provided.
Pearson International has chosen to build a new offering using the Sakai Collaboration and Learning Environment to support instructors and students in many different regions around the world, including Japan, Germany, France, Sweden and more. This demo will look at what is unique to the project and what Sakai offers "out of the box."
Using Sakai Site Archive for Good not EvilCris Holdorph
Sakai Site Archive allows exporting and importing Sakai sites to preserve disk space or maintain performance. The author modified Site Archive to support content exchange between different Sakai systems. Key lessons included assuming entities exist in the target system and handling linking between content. While intended for archiving existing sites, Site Archive can work for content exchange with some changes.
What can you do with this GeoServer thing? This talk covers some of the basic (and not so basic) ways to use GeoServer to publish your geospatial data and make it look great!
GeoServer made its first release in 2001 and has grown into an amazing, capable and diverse program. This also means the “feature list” is spread over years of release announcements, presentations, mailing list archives!
This presentations provides a whirlwind tour of GeoServer and everything it can do today!
This talk is a visual guide to the features of GeoServer. Are you just getting started with GeoServer, or considering it for the first time? Attend this talk and prioritize what you want to look into first. Are you an expert user who has been running GeoServer since Java 1.4? Attend this talk and see what tricks an optimisations you have been missing out on!
This document discusses MySQL's new Document Store and Node.JS API. It introduces the MySQL X DevAPI, which provides a high-level interface for developing applications that leverage both relational and document-style data through a common API. The API is supported across multiple languages by connectors like Node.JS. It allows mixing table and document queries, and uses components like the MySQL Router and X Plugin to interface with the database.
Connector/J Beyond JDBC: the X DevAPI for Java and MySQL as a Document StoreFilipe Silva
The document discusses Connector/J Beyond JDBC and the X DevAPI for Java and MySQL as a Document Store. It provides an agenda that includes an introduction to MySQL as a document store, an overview of the X DevAPI, and how the X DevAPI is implemented in Connector/J. The presentation aims to demonstrate the X DevAPI for developing CRUD-based applications and using MySQL as both a relational database and document store.
Slides semantic web and Drupal 7 NYCCamp 2012scorlosquet
This document summarizes a presentation about using semantic web technologies like RDFa, schema.org, and JSON-LD with Drupal 7. It discusses how Drupal 7 outputs RDFa by default and can be extended through contributed modules to support additional RDF formats, a SPARQL endpoint, schema.org mapping, and JSON-LD. Examples of semantic markup for events and people are provided.
Using mruby in the nosql database Avocadodbavocadodb
Frank Celler discusses using the Ruby-based scripting language mruby as an embedded language in the AvocadoDB nosql database. He explains that AvocadoDB offers unique features and solves some problems of other nosql databases. The mruby subproject allows using Ruby scripts for stored procedures, as an alternative to JavaScript, and provides access to AvocadoDB's graph functions. He invites interested developers to get involved with the AvocadoDB project.
This document summarizes Everright Chen's presentation on Drupal migration. The presentation covered:
1) Why Drupal is a good content management system for rapid website development, security, and customization.
2) Migrating from non-Drupal systems or older Drupal versions to Drupal, including the upgrade process.
3) How to migrate data between systems using tools like MySQL queries, Drush, and the Migrate module.
4) An overview of CI&T, a global company with a focus on Drupal development and Acquia partnership.
MySQL Cluster is a distributed, highly available, and scalable version of MySQL. It has key components like connection management, SQL parser, and storage engines. The two main types of MySQL Clusters are InnoDB Cluster and Network Database (NDB) Cluster. NDB Cluster uses different node types and data partitioning across nodes to provide scalability and high availability. MySQL Cluster is used for applications that require high performance, scalability and availability.
The document discusses LDAP at Lightning Speed and provides information about LMDB, a new key-value database created by Howard Chu that is optimized for LDAP backends. LMDB uses a single-level store design with memory-mapped files and copy-on-write to provide fully transactional, ACID-compliant access without the need for caching, locking, or write-ahead logging. It has a simple configuration and outperforms Berkeley DB while using a fraction of the code size.
This document provides an overview of big data presented by Zohar Elkayam from Brillix. It defines big data, discusses the challenges of volume, variety, velocity and veracity. It introduces Hadoop as an open source framework for distributed processing of large datasets and describes its key components - HDFS and MapReduce. The document also discusses infrastructure, positions, and use cases for big data as well as success stories from industries applying big data analytics.
The document discusses the NoSQL ecosystem. It provides a brief history of NoSQL databases from the late 1990s to today. It then lists and categorizes the major NoSQL databases. The rest of the document discusses interesting properties of NoSQL databases like data models, query models, transactions, and consistency. It also provides examples of real-world usage at companies like Netflix, Facebook, and Craigslist. Key takeaways are around developer accessibility, reuse of NoSQL components, and using the right tool for the job (polyglot persistence).
Ceph began as a research project in 2005 to create a scalable object storage system. It was incubated at DreamHost from 2007-2012 and spun out as an independent company called Inktank in 2012. Key developments included the RADOS distributed storage cluster, erasure coding, and the Ceph filesystem. The project has grown a large community and is used in many production deployments, focusing on areas like tiering, erasure coding, replication, and integrating with the Linux kernel. Future plans include improving CephFS, expanding the ecosystem through different storage backends, strengthening governance, and targeting new use cases in big data and the enterprise.
Ceph is an open-source distributed storage system that began as a research project in 2005. It has grown significantly since then, with the founding of Inktank in 2012 to support Ceph's development and commercial adoption. Key developments include the native Linux kernel client, erasure coding support, asynchronous replication, and improvements to CephFS. The future of Ceph includes stronger governance, a focus on performance and low-power architectures, integration with big data workloads, and use as an archival storage solution.
The world has changed and having one huge server won’t do the job anymore, when you’re talking about vast amounts of data, growing all the time the ability to Scale Out would be your savior. Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
This lecture will be about the basics of Apache Spark and distributed computing and the development tools needed to have a functional environment.
Ceph is an open-source distributed storage system that provides object, block, and file storage on commodity hardware. It uses a pseudo-random placement algorithm called CRUSH to distribute data across a cluster in a fault-tolerant manner without single points of failure. Ceph has various applications including a RADOS Gateway for S3/Swift compatibility, RADOS Block Device for virtual machine images, and a CephFS for a POSIX-compliant distributed file system.
Ceph: A decade in the making and still going strongPatrick McGarry
Ceph is an open source distributed storage system that has been in development for over a decade. It started as a research project at UC Santa Cruz to build scalable object storage. Over the years, it has grown to include distributed block storage, file storage and an S3-compatible object store. Ceph is now used in many production deployments and has a thriving developer community, though continued work is needed to improve areas like CephFS and add new features around erasure coding, tiering and replication. The future of Ceph involves strengthening governance, expanding the ecosystem, improving performance and gaining more adoption in enterprise storage environments.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
Ceph Day Seoul - Ceph: a decade in the making and still going strong Ceph Community
Ceph began as a research project in 2005 to create a scalable object storage system. It was incubated at various organizations until 2012 when Inktank was formed to support Ceph development and adoption. Inktank focused on releasing stable versions, building infrastructure to test and support Ceph, and growing the developer community. This helped Ceph see widespread adoption in production environments, with Inktank later being acquired by Red Hat in 2014 to further expand Ceph's use.
"NoSQL on the move" by Glynn Bird
Mobile-first app web development is a solved problem, but how can you websites and apps the continue to work with little or internet connectivity? Discover how Offline-first development allows apps to present an "always on" experience for their user
The document discusses operations in the cloud and best practices. It describes how companies like Zynga and others were able to scale games and applications using AWS services like EC2, S3, EBS, ELB, and RDS. It discusses high availability, making applications stateless, monitoring, and open source alternatives. Best practices include infrastructure as code, automated provisioning, eliminating single points of failure, caching, and following the sun for development.
The document discusses operations in the cloud and best practices. It describes how companies like Zynga and others were able to scale games and applications using AWS services like EC2, S3, EBS, ELB, and RDS. It discusses high availability, making applications stateless, monitoring, and open source alternatives. Best practices include infrastructure as code, automated provisioning, eliminating single points of failure, caching, and following the sun for development.
This document provides an introduction to big data and NoSQL databases. It begins with an introduction of the presenter. It then discusses how the era of big data came to be due to limitations of traditional relational databases and scaling approaches. The document introduces different NoSQL data models including document, key-value, graph and column-oriented databases. It provides examples of NoSQL databases that use each data model. The document discusses how NoSQL databases are better suited than relational databases for big data problems and provides a real-world example of Twitter's use of FlockDB. It concludes by discussing approaches for working with big data using MapReduce and provides examples of using MongoDB and Azure for big data.
This document provides an introduction and agenda for a presentation on MongoDB 2.4 and Spring Data. The presentation will include a quick introduction to NoSQL and MongoDB, an overview of Spring Data's MongoDB support including configuration, templates, repositories and queries, and details on metadata mapping, aggregation functions, GridFS file storage and indexes in MongoDB.
Node Js, AngularJs and Express Js TutorialPHP Support
This document provides an overview of the Node.js, Express.js, AngularJS, and MongoDB technologies and how they can be used together. It discusses what each technology is, its features and uses. Node.js is a JavaScript runtime built on Chrome's V8 engine for building fast network applications. Express.js is a web framework built on Node.js that simplifies building web apps. AngularJS is a JavaScript framework for building dynamic web apps. MongoDB is a popular open-source NoSQL database that stores data in JSON-like documents.
Using mruby in the nosql database Avocadodbavocadodb
Frank Celler discusses using the Ruby-based scripting language mruby as an embedded language in the AvocadoDB nosql database. He explains that AvocadoDB offers unique features and solves some problems of other nosql databases. The mruby subproject allows using Ruby scripts for stored procedures, as an alternative to JavaScript, and provides access to AvocadoDB's graph functions. He invites interested developers to get involved with the AvocadoDB project.
This document summarizes Everright Chen's presentation on Drupal migration. The presentation covered:
1) Why Drupal is a good content management system for rapid website development, security, and customization.
2) Migrating from non-Drupal systems or older Drupal versions to Drupal, including the upgrade process.
3) How to migrate data between systems using tools like MySQL queries, Drush, and the Migrate module.
4) An overview of CI&T, a global company with a focus on Drupal development and Acquia partnership.
MySQL Cluster is a distributed, highly available, and scalable version of MySQL. It has key components like connection management, SQL parser, and storage engines. The two main types of MySQL Clusters are InnoDB Cluster and Network Database (NDB) Cluster. NDB Cluster uses different node types and data partitioning across nodes to provide scalability and high availability. MySQL Cluster is used for applications that require high performance, scalability and availability.
The document discusses LDAP at Lightning Speed and provides information about LMDB, a new key-value database created by Howard Chu that is optimized for LDAP backends. LMDB uses a single-level store design with memory-mapped files and copy-on-write to provide fully transactional, ACID-compliant access without the need for caching, locking, or write-ahead logging. It has a simple configuration and outperforms Berkeley DB while using a fraction of the code size.
This document provides an overview of big data presented by Zohar Elkayam from Brillix. It defines big data, discusses the challenges of volume, variety, velocity and veracity. It introduces Hadoop as an open source framework for distributed processing of large datasets and describes its key components - HDFS and MapReduce. The document also discusses infrastructure, positions, and use cases for big data as well as success stories from industries applying big data analytics.
The document discusses the NoSQL ecosystem. It provides a brief history of NoSQL databases from the late 1990s to today. It then lists and categorizes the major NoSQL databases. The rest of the document discusses interesting properties of NoSQL databases like data models, query models, transactions, and consistency. It also provides examples of real-world usage at companies like Netflix, Facebook, and Craigslist. Key takeaways are around developer accessibility, reuse of NoSQL components, and using the right tool for the job (polyglot persistence).
Ceph began as a research project in 2005 to create a scalable object storage system. It was incubated at DreamHost from 2007-2012 and spun out as an independent company called Inktank in 2012. Key developments included the RADOS distributed storage cluster, erasure coding, and the Ceph filesystem. The project has grown a large community and is used in many production deployments, focusing on areas like tiering, erasure coding, replication, and integrating with the Linux kernel. Future plans include improving CephFS, expanding the ecosystem through different storage backends, strengthening governance, and targeting new use cases in big data and the enterprise.
Ceph is an open-source distributed storage system that began as a research project in 2005. It has grown significantly since then, with the founding of Inktank in 2012 to support Ceph's development and commercial adoption. Key developments include the native Linux kernel client, erasure coding support, asynchronous replication, and improvements to CephFS. The future of Ceph includes stronger governance, a focus on performance and low-power architectures, integration with big data workloads, and use as an archival storage solution.
The world has changed and having one huge server won’t do the job anymore, when you’re talking about vast amounts of data, growing all the time the ability to Scale Out would be your savior. Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
This lecture will be about the basics of Apache Spark and distributed computing and the development tools needed to have a functional environment.
Ceph is an open-source distributed storage system that provides object, block, and file storage on commodity hardware. It uses a pseudo-random placement algorithm called CRUSH to distribute data across a cluster in a fault-tolerant manner without single points of failure. Ceph has various applications including a RADOS Gateway for S3/Swift compatibility, RADOS Block Device for virtual machine images, and a CephFS for a POSIX-compliant distributed file system.
Ceph: A decade in the making and still going strongPatrick McGarry
Ceph is an open source distributed storage system that has been in development for over a decade. It started as a research project at UC Santa Cruz to build scalable object storage. Over the years, it has grown to include distributed block storage, file storage and an S3-compatible object store. Ceph is now used in many production deployments and has a thriving developer community, though continued work is needed to improve areas like CephFS and add new features around erasure coding, tiering and replication. The future of Ceph involves strengthening governance, expanding the ecosystem, improving performance and gaining more adoption in enterprise storage environments.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
Ceph Day Seoul - Ceph: a decade in the making and still going strong Ceph Community
Ceph began as a research project in 2005 to create a scalable object storage system. It was incubated at various organizations until 2012 when Inktank was formed to support Ceph development and adoption. Inktank focused on releasing stable versions, building infrastructure to test and support Ceph, and growing the developer community. This helped Ceph see widespread adoption in production environments, with Inktank later being acquired by Red Hat in 2014 to further expand Ceph's use.
"NoSQL on the move" by Glynn Bird
Mobile-first app web development is a solved problem, but how can you websites and apps the continue to work with little or internet connectivity? Discover how Offline-first development allows apps to present an "always on" experience for their user
The document discusses operations in the cloud and best practices. It describes how companies like Zynga and others were able to scale games and applications using AWS services like EC2, S3, EBS, ELB, and RDS. It discusses high availability, making applications stateless, monitoring, and open source alternatives. Best practices include infrastructure as code, automated provisioning, eliminating single points of failure, caching, and following the sun for development.
The document discusses operations in the cloud and best practices. It describes how companies like Zynga and others were able to scale games and applications using AWS services like EC2, S3, EBS, ELB, and RDS. It discusses high availability, making applications stateless, monitoring, and open source alternatives. Best practices include infrastructure as code, automated provisioning, eliminating single points of failure, caching, and following the sun for development.
This document provides an introduction to big data and NoSQL databases. It begins with an introduction of the presenter. It then discusses how the era of big data came to be due to limitations of traditional relational databases and scaling approaches. The document introduces different NoSQL data models including document, key-value, graph and column-oriented databases. It provides examples of NoSQL databases that use each data model. The document discusses how NoSQL databases are better suited than relational databases for big data problems and provides a real-world example of Twitter's use of FlockDB. It concludes by discussing approaches for working with big data using MapReduce and provides examples of using MongoDB and Azure for big data.
This document provides an introduction and agenda for a presentation on MongoDB 2.4 and Spring Data. The presentation will include a quick introduction to NoSQL and MongoDB, an overview of Spring Data's MongoDB support including configuration, templates, repositories and queries, and details on metadata mapping, aggregation functions, GridFS file storage and indexes in MongoDB.
Node Js, AngularJs and Express Js TutorialPHP Support
This document provides an overview of the Node.js, Express.js, AngularJS, and MongoDB technologies and how they can be used together. It discusses what each technology is, its features and uses. Node.js is a JavaScript runtime built on Chrome's V8 engine for building fast network applications. Express.js is a web framework built on Node.js that simplifies building web apps. AngularJS is a JavaScript framework for building dynamic web apps. MongoDB is a popular open-source NoSQL database that stores data in JSON-like documents.
This document compares the two major open source databases: MySQL and PostgreSQL. It provides a brief history of each database's development. MySQL prioritized ease-of-use and performance early on, while PostgreSQL focused on features, security, and standards compliance. More recently, both databases have expanded their feature sets. The document discusses the most common uses, features, and performance of each database. It concludes that for simple queries on 2-core machines, MySQL may perform better, while PostgreSQL tends to perform better for complex queries that can leverage multiple CPU cores.
Big data refers to large, complex datasets that are difficult to process using traditional methods. This document discusses three examples of real-world big data challenges and their solutions. The challenges included storage, analysis, and processing capabilities given hardware and time constraints. Solutions involved switching databases, using Hadoop/MapReduce, and representing complex data structures to enable analysis of terabytes of ad serving data. Flexibility and understanding domain needs were key to feasible versus theoretical solutions.
Clustering Made Easier: Using Terracotta with Hibernate and/or EHCacheCris Holdorph
The document discusses using various technologies like Ehcache, Hibernate, and Terracotta for distributed caching and clustering. It provides code samples and configuration for using Ehcache as a second-level cache for Hibernate, and using Terracotta to cluster the Ehcache for distributed caching across multiple servers.
Pre-conference seminar from the March 2010, Jasig (www.jasig.org) conference in San Diego, CA.
Additional presentation materials are available at the following page - http://www.ja-sig.org/wiki/display/JCON/JSR+286+Seminar+March+2010
Adding Performance Testing to a Software Development ProjectCris Holdorph
Performance testing aims to eliminate bottlenecks and establish baselines by testing software under load. It involves setting up a test environment, defining goals, using tools to stress the system and collect data, and analyzing the results to optimize performance. While it cannot perfectly simulate real usage, performance testing is still valuable for regression testing and anticipating hardware needs before deployment.
This presentation provides an overview and future directions of the Learning Information Services (LIS) specification and Sakora integration tool in Sakai. LIS is used primarily for LMS/SIS integration and defines how student data like courses, sections, grades and outcomes can be exchanged. Sakora allows Sakai to integrate with external student systems by implementing the LIS spec. Going forward, Sakai aims to improve automated site provisioning based on student data, better support for sections and enrollment status in tools, and define group/role standards.
This document discusses cluster-enabling the Sakai learning management system with Terracotta. It provides an overview of why Terracotta was chosen, the current status of the integration, how the design and development was approached, how to run a Terracotta-enabled Sakai, how to cluster-enable a Sakai tool, a demo, and resources for learning more. Key points covered include making session data and classes cluster-aware, only partially sharing data between nodes, unit testing changes, and cluster-enabling specific tools as proof of concepts.
This document introduces Terracotta, an open source distributed caching and session clustering technology. It discusses the main components of Terracotta including the Terracotta server, enabled applications, and administration console. It also covers configuring Terracotta using the tc-config.xml file to define roots, locks, instrumented classes, and resolving transient fields. The document discusses integrating Terracotta using modules and provides a hello world example. It shares lessons learned using Terracotta and different uses for the technology. Resources for learning more are provided.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
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.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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.
10. Map Reduce
●
Patented software framework introduced by Google
in 2004 to support distributed computing on large
data sets on clusters of computers.
●
Naming originally inspired by map and reduce
functions of functional programming (but their
purpose is not the same as it was there)
●
Map
– The master node takes the input, partitions it up into
smaller sub-problems, and distributes those to worker nodes
●
Reduce
– The master node then takes the answers to all the sub-
problems and combines them in some way to get the output
10
11. What does NoSQL Stand For?
●
NoSQL
●
No SQL
●
Not SQL
●
Not Only SQL
●
Not the RDBMS
●
Wikipedia:
– Carlo Strozzi used the term "NoSQL" in 1998 to
name his lightweight, open-source relational
database that did not expose an SQL interface.
11
12. History
●
Some techniques have existed for over 25
years
●
Teradata selling product for more then 20
years
●
RDBMS dates back to 1970
12
13. CAP Theorem
●
A conjecture made by Eric Brewer at the
Symposium on Principles of Distributed
Computing (2000)
●
States only possible to achieve 2 of 3
– Consistency (all nodes see the same data at the
same time)
– Availability (node failures do not prevent survivors
from continuing to operate)
– Partition Tolerance (the system continues to
operate despite arbitrary message loss)
13
14. CAP
●
Consistent and Available
– ACID systems, MySQL cluster, Oracle Coherence,
Drizzle
●
Consistent and Partition Tolerance
– SCLA (strongly consistent, loosely available)
– HBase, Bigtable
●
Available and Partition Tolerant
– BASE systems (CouchDB, SimpleDB, MongoDB
●
Cassandra (sits between SCLA/BASE
systems)
14
16. Hadoop
●
Open-source software for reliable, scalable,
distributed computing (Hadoop website)
– Hadoop Common
– HDFS
– MapReduce
●
Created Initially in early 2006 to support
search engine project Nutch
●
Inspired by the Google File System and
MapReduce papers (Oct 2003)
16
17. Hadoop Related Projects
●
Hbase
– A scalable, distributed database that supports
structured data storage for large tables
●
Hive
– A data warehouse infrastructure that provides
data summarization and ad hoc querying
●
Pig
– A high-level data-flow language and execution
framework for parallel computation
●
Cassandra
– uses Hadoop for MapReduce 17
18. Who Uses Hadoop
●
EBay (532 nodes, Search optimization)
●
Facebook (1100x8 node cluster, 300x8 node cluster, more on
this later)
●
GumGum (Ken Weiner, 20+ node cluster on Amazon EC2)
●
Hulu (log storage analysis)
●
Last.fm (44x2 nodes log analysis, 20x2 nodes profile analysis)
●
LinkedIn (120x2x4 nodes, 520x2x4 nodes, "People you may
know")
●
Twitter (more on this later)
●
Yahoo! (100,000 cpus running Hadoop, more on this later)
18
19. CouchDB
●
Apache open source document oriented database
written in Erlang (concurrent programming lang)
●
Designed to scale horizontally
●
Stores documents (one or more field value pairs
expressed as JSON)
●
ACID Semantics
●
Map/Reduce Views and Indexes (written in server
side javascript)
●
Bi-direction replication (with conflict resolution)
●
REST API
19
21. CouchDB Sample Document
"Subject": "I like Plankton"
"Author": "Rusty"
"PostedDate": "5/23/2006"
"Tags": ["plankton", "baseball", "decisions"]
"Body": "I decided today that I don't like baseball. I
like plankton."
http://couchdb.apache.org/docs/intro.html
21
22. Who uses CouchDB?
●
Ubuntu One – cloud storage service
– http://ubuntuone.com/
●
"I Play WoW" facebook app
– http://blog.socklabs.com/2008/12/24/iplaywow_monthly_actives.html
●
Wego - travel site
– http://www.wego.com/
22
23. Cassandra
●
Fault Tolerant (replication, failed nodes can
be replaced with no downtime)
●
Decentralized (ever node in cluster is
identical, no bottlenicks)
●
Supports either Synchronous or
Asynchronous update replication
●
Supports more then simple key/value pair
●
Elastic (read/write throughput increase
linearly as machines are added)
●
Durable (suitable for applictions that can't
23
afford to lose data)
24. Cassandra
●
Initially developed by Facebook for Inbox
Search (until replaced by HBase)
●
Key-value store where values can be multiple
values
●
Some inspiration from Amazon's Dynamo
(another key-value store)
24
26. MongoDB
●
Name is derived from "humongous"
●
Document oriented database written in C++
●
Manages collections of JSON-like documents
●
Binaries available for windows, linux, OS X,
Solaris
●
Supports dates, regular expressions code,
binary data (all BSON types)
●
Cursors for query results
●
Any field can be queried at any time
26
27. MongoDB
●
Queries can include user-defined JavaScript
functions
●
Master/Slave (only master supports writes,
slaves can be read from)
●
Scales horizontally using sharding
●
Support for Map/Reduce
27
28. Who uses MongoDB?
●
New York Times
●
Shutterfly
●
Foursquare
●
SourceForge
●
Intuit
28
29. Google Big Table
●
Built on GFS (Google File System)
●
Can be used with Google App Engine
●
Maps two aribtrary strings and a timestamp
●
Designed to scale into the petabyte range
●
Designed to scale across hundreds or
thousands of machines
●
Portions of a table (tablets) can be
compressed
●
HBase was modeled after BigTable
29
30. Who uses Big Table?
●
Google Reader
●
Google Maps
●
Google Book Search
●
Google Earth
●
Blogger.com
●
Google Code
●
Orkut
●
YouTube
●
Gmail 30
31. Amazon SimpleDB
●
Written in Erlang
●
Used with Amazon EC2 and Amazon S3
●
Easy access to lookup and query functions
●
Without support for the less used complex database
functions
●
Do not need to pre-define data formats that will be stored
●
Scalable (with size limitations)
– 10gb per domain, up to 250 domains
●
Fast/Reliable
●
Supports eventually consistent read and consistent read
●
Potentially Inexpensive
31
33. SimpleDB Data Model
●
Customer Account (amazon web services account)
●
Domains (similar to tables, or spreadsheet tabs)
●
Items (similar to rows)
●
Attributes (similar to columns)
●
Values (similar to cells)
– Unlike a spreadsheet, however, multiple values can be
associated with a cell
●
One domain can contain different types of data
(some attributes not filled in)
33
36. memcached
●
General purpose distributed memory caching system
●
Often used to cache in RAM that might otherwise be
obtained from an external data source
●
LRU (when cache is full)
●
Can be distributed across multiple machines
36
38. Terracotta
●
JVM in-memory distributed cache / store
●
The object store can be persistent
●
Distribution between nodes is handled through
Terracotta server
●
Supports multiple Terracotta servers
●
Nodes only receive data they need/reference
38
39. Who uses Terracotta?
●
Sakai (thanks to John Wiley & Sons)
●
PartyGaming (PartyPoker.com)
●
Adobe
●
Pearson
39
41. Yahoo!
●
Hadoop
– http://developer.yahoo.com/blogs/hadoop
– More than 100,000 CPUs in >36,000 computers
running Hadoop
– Our biggest cluster: 4000 nodes (2*4cpu boxes w
4*1TB disk & 16GB RAM)
– Used to support research for Ad Systems and Web
Search
– Also used to do scaling tests to support
development of Hadoop on larger clusters
– >60% of Hadoop Jobs within Yahoo are Pig jobs
41
42. Twitter
●
How Twitter Uses NoSQL
– http://goo.gl/Bwxoe
●
Scribe
– Syslog stopped scaling
●
Hadoop
– Needs to store more data per day than it can reliably write to a
single hard drive
●
Pig
– Used for interacting with Hadoop
●
Hbase
– People Search
●
FlockDB
– Social Graph Analysis 42
43. Netflix
●
NoSQL at Netflix
– http://goo.gl/SDcsZ
●
SimpleDB
– Highly durable, with writes automatically replicated across
availability zones within a region
– Love it when others do heavy lifting for us
●
Hadoop/HBase
– Convenient, high-performance column-oriented distributed
database solution
– HBase makes it really easy to grow your cluster and re-distribute
load across nodes at runtime
●
Cassandra
– Adding more servers, without the need to re-shard
43
44. Facebook
●
http://goo.gl/J9EVW
●
350 million users sending over 15 billion person-to-person messages
per month
●
Chat service supports over 300 million users who send over 120 billion
messages per month
●
Two patterns emerged
– A short set of temporal data that tends to be volatile
– An ever-growing set of data that rarely gets accessed
●
Evaluate clusters of MySQL, Apache Cassandra, Apache HBase, and a
couple of other systems
– MySQL proved to not handle the long tail of data well (as
indexes/data grows large performance suffers
– Cassandra's eventual consistency model to be a difficult pattern to
reconcile for our new Messages infrastructure.
44
45. “There is a learning curve and an
operational overhead. Still, the scalability,
availability and performance advantages of
the NoSQL persistence model are evident
and are paying for themselves already, and
will be central to our long-term cloud
strategy.”
Yury Izrailevsky, Netflix
45
46. Questions & Answers
Cris J. Holdorph
Software Architect
Unicon, Inc.
Twitter: @holdorph
holdorph@unicon.net
www.unicon.net 46