In most cases talking about big data follows an "a posteriori" view where an organization overwhelmed by huge amounts of log files and numerous data sources scattered among its departments decides to put some order to the mess and get some value out of the "big data", usually building a Hadoop cluster. In this presentation I take the opposite direction and try to demonstrate how to proactively design and build product architectures that manage to remain simple and lean while at the same time anticipate the big data complexities and solve them easily and elegantly from day one.
Este documento describe los sistemas de recomendación y sus aplicaciones. Explica que las recomendaciones representan un porcentaje significativo del tráfico en sitios como Netflix, Google News y Amazon. Describe diferentes tipos de sistemas de recomendación como los basados en contenido, filtros colaborativos y modelos. Finalmente, discute herramientas como Mahout que permiten implementar sistemas de recomendación a gran escala.
CDO Slides: Real World Data Strategy Success StoriesDATAVERSITY
A common question from upper management is “Does this really work? Can you show me where there has been success?” Well, the answer is “Yes, this works.” Join John and Kelle for a review of Data Strategy success stories. We will review success stories for data governance, data quality, and other types of data.
Some successes we will examine are:
- Standing up data governance in difficult cultures
- EIM programs that created value for the organization
- Several small case studies of organizations that have had success in DQ, Analytics, and MDM
This document summarizes the journey of SalesStash, a startup developing tools to help sales teams. It describes:
1) Their initial focus on automating slide creation which they learned did not address the biggest pain points.
2) After customer interviews, they pivoted to a content management tool that matches sales decks with third-party content using machine learning.
3) The document outlines their plans to disrupt the market research industry in three phases, growing revenue from $1M to $10M by focusing on the biggest pain points of sales teams.
Healthy adults ages 50-65 with over $500k in assets and an existing will/trust are the target customer segment. The product automatically maps a user's financial and digital assets to help organize their affairs and ensure ease of handling for loved ones after death. Revenue comes from a one-time fee for static access or annual subscription for ongoing access. The goal is to gain customers through professional referrals, ads, and bloggers while retaining them through service upgrades and incentives.
HomeSlice aims to make real estate more accessible through fractional ownership. It provides an end-to-end home buying solution that removes barriers like finding reliable co-owners, structuring agreements, and liability in case of default. After validating customer interest and barriers, HomeSlice focused on facilitating co-owner agreements and mitigating default risk. By acting as a guarantor and working with institutional investors, HomeSlice allows single mortgages while keeping the process simple for lenders and borrowers.
The document outlines the evolution of an idea to help engineers write better patents through increased transparency and communication, however, customer interviews revealed that communication was not a major issue and the focus shifted to helping internal IP committees evaluate patent opportunities within an engineer's documentation. Further customer discovery helped refine the value proposition and target customers as internal IP committees at large companies that have a need to thoroughly evaluate a high volume of patent opportunities but lack the capacity to do so.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Beyond Social – Tailor Sharepoint 2013 social features according to your need...Adis Jugo
The document discusses SharePoint 2013 social features and how they can be extended and customized. It provides an overview of the social architecture in SharePoint 2013 including how feeds are stored. It then covers the different APIs available for working with social features, including the client-side APIs like CSOM and REST, and the server-side object model. It demonstrates how these APIs can be used and their limitations. It emphasizes the need to implement governance features on the server-side to control social features according to organizational needs and compliance regulations.
Este documento describe los sistemas de recomendación y sus aplicaciones. Explica que las recomendaciones representan un porcentaje significativo del tráfico en sitios como Netflix, Google News y Amazon. Describe diferentes tipos de sistemas de recomendación como los basados en contenido, filtros colaborativos y modelos. Finalmente, discute herramientas como Mahout que permiten implementar sistemas de recomendación a gran escala.
CDO Slides: Real World Data Strategy Success StoriesDATAVERSITY
A common question from upper management is “Does this really work? Can you show me where there has been success?” Well, the answer is “Yes, this works.” Join John and Kelle for a review of Data Strategy success stories. We will review success stories for data governance, data quality, and other types of data.
Some successes we will examine are:
- Standing up data governance in difficult cultures
- EIM programs that created value for the organization
- Several small case studies of organizations that have had success in DQ, Analytics, and MDM
This document summarizes the journey of SalesStash, a startup developing tools to help sales teams. It describes:
1) Their initial focus on automating slide creation which they learned did not address the biggest pain points.
2) After customer interviews, they pivoted to a content management tool that matches sales decks with third-party content using machine learning.
3) The document outlines their plans to disrupt the market research industry in three phases, growing revenue from $1M to $10M by focusing on the biggest pain points of sales teams.
Healthy adults ages 50-65 with over $500k in assets and an existing will/trust are the target customer segment. The product automatically maps a user's financial and digital assets to help organize their affairs and ensure ease of handling for loved ones after death. Revenue comes from a one-time fee for static access or annual subscription for ongoing access. The goal is to gain customers through professional referrals, ads, and bloggers while retaining them through service upgrades and incentives.
HomeSlice aims to make real estate more accessible through fractional ownership. It provides an end-to-end home buying solution that removes barriers like finding reliable co-owners, structuring agreements, and liability in case of default. After validating customer interest and barriers, HomeSlice focused on facilitating co-owner agreements and mitigating default risk. By acting as a guarantor and working with institutional investors, HomeSlice allows single mortgages while keeping the process simple for lenders and borrowers.
The document outlines the evolution of an idea to help engineers write better patents through increased transparency and communication, however, customer interviews revealed that communication was not a major issue and the focus shifted to helping internal IP committees evaluate patent opportunities within an engineer's documentation. Further customer discovery helped refine the value proposition and target customers as internal IP committees at large companies that have a need to thoroughly evaluate a high volume of patent opportunities but lack the capacity to do so.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Beyond Social – Tailor Sharepoint 2013 social features according to your need...Adis Jugo
The document discusses SharePoint 2013 social features and how they can be extended and customized. It provides an overview of the social architecture in SharePoint 2013 including how feeds are stored. It then covers the different APIs available for working with social features, including the client-side APIs like CSOM and REST, and the server-side object model. It demonstrates how these APIs can be used and their limitations. It emphasizes the need to implement governance features on the server-side to control social features according to organizational needs and compliance regulations.
Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.
Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.
What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:
- Product mindset and how balanced teams can reduce internal friction
- Creating data as a product to align with cloud-native application architectures, like microservices and serverless
- Getting started bringing lean principles into your data organization
- Balancing data usability with data protection, governance, and security
Presenter : Dormain Drewitz, Pivotal & Mike Koleno, Solstice
Sharepoint Online and Windows Azure together: Autohosted AppsAdis Jugo
Adis Jugo, a Microsoft MVP and technology advisor at PlanB, gave a presentation on autohosted apps with SharePoint Online and Windows Azure. He discussed the history of SharePoint in the cloud and different solution types. Jugo also covered SharePoint apps and how they can access SharePoint and external data through OAuth authentication without running on SharePoint servers. He explained the benefits of autohosted apps being isolated, multitenant, and fully cloud-based. Jugo concluded by taking questions and noting that apps can be upgraded independently in the cloud.
Are you working with an existing codebase? Do you want to use the cool libraries, tools or methodology you saw at this conference? This talk will show you how to combine legacy code with bleeding edge technology.
Often the use of new technology and protocols is limited by fear of change in existing applications. Yet, as developers, we want to remain relevant and have fun with all the disruptive changes in our industry.
This talk will drill-down from a high-level architectural view to the actual implementation of modern, strangling services in a legacy monolith, using DDD to define bounded contexts, in a safe and controlled manner.
Enable SQL/JDBC Access to Apache Geode/GemFire Using Apache CalciteVMware Tanzu
SpringOne Platform 2017
Christian Tzolov, Pivotal
"When working with BigData & IoT systems we often feel the need for an established, Common Query Language.
To fill this gap some NoSql vendors are building SQL access to their systems. Building SQL engine from scratch is a daunting job and frameworks like Apache Calcite can help you with the heavy lifting. It allows you to integrate SQL parser, Cost-Based Optimizer, and JDBC with your NoSql system. Calcite has been used to empower many BigData platforms such as Hive, Spark, Flink, Drill, HBase/Phoenix to name some.
In this session I will walk you through the process of building a SQL access layer for Apache Geode (GemFire). I will share my experience, pitfalls and technical consideration like balancing between the SQL/RDBMS semantics and the design choices and limitations of In-Memory-Data-Grid systems like Geode.
Hopefully this will enable you to add SQL capabilities to your preferred NoSQL data system."
Ravi Sundriyal is seeking an internship position to gain valuable experience and apply his technical skills. He has over 4 years of experience as a Senior Software Engineer for Computer Sciences Corporation in India, where he worked on the Manulife Japan project. In this role, he served as a product developer, quality assurance tester, project lead, and business analyst. He is currently pursuing a Master's degree in Computer Software Engineering at San Jose State University. His technical skills include languages like Java, Python, COBOL, and frameworks like AngularJS and NodeJS. He has worked on several academic projects involving web and mobile applications.
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
This document discusses how data science models have transitioned to the cloud to take advantage of greater computing resources. It notes that data science models are resource-intensive and traditionally required powerful local machines. The cloud allows data scientists to run models on cloud infrastructure for lower costs than high-end laptops and with access to many GPUs. Several major cloud platforms - Azure, AWS, and Google Cloud - are discussed and compared in terms of their machine learning offerings. The document also introduces Microsoft's Team Data Science Process, which aims to help data science teams collaborate more effectively on projects in the cloud.
Lambda architecture for real time big dataTrieu Nguyen
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
This document discusses secrets of enterprise data mining. It begins by defining data mining as the automated or semi-automated process of discovering patterns in data. It then discusses how data mining can be applied in various industries like telecommunications, oil and gas, and Volkswagen Group. Finally, it discusses how Microsoft offers solutions for enterprise data mining through SQL Server Analysis Services and Microsoft Azure Machine Learning.
Coming right from the Recommender Systems conference in San Francisco, I present some latest developments in the field of large scale recommendation engines and machine learning.
Webinar: SpagoBI & Big Data, a smart approach to turn data into knowledge SpagoWorld
The presentation supported the webinar focused on the smart approach adopted by SpagoBI suite to manage Big Data, delivered on October 8th, 2013 within SpagoWorld Webinar Center. http://www.spagoworld.org/
In this presentation, Kaz Ohta, Kiyoto Tamura, and Ankush Rustagi from Treasure Data describe the company's Cloud Data Warehouse service.
"The Treasure Data Cloud Data Warehouse service enables companies to get big data analytics running in days not months without specialist IT resources and for a tenth the cost of other alternatives. Traditional data warehousing solutions - even modern alternatives such as Hadoop - are too expensive, complex and take too long for many companies to implement, so the idea of quickly launching a data warehouse service that uses the power and economics of the Cloud for companies of any size, opens up a huge potential market."
Learn more at: http://treasure-data.com * Watch the presentation video: http://inside-bigdata.com/?p=3531
Treasure Data is a cloud-based big data analytics company based in Silicon Valley with about 20 employees. The document discusses Treasure Data's services and architecture, which includes collecting data from various sources using Fluentd, storing the data in a columnar format on AWS S3, and performing analytics using Hadoop and SQL queries. Treasure Data aims to simplify big data adoption through its fully-managed platform and quick setup process. Example customers discussed were able to see results within 2 weeks of signing up.
- The document discusses a presentation given by Jongwook Woo on introducing Spark and its uses for big data analysis. It includes information on Woo's background and experience with big data, an overview of Spark and its components like RDDs and task scheduling, and examples of using Spark for different types of data analysis and use cases.
Sarath Kumar Prabhakaran is a graduate student at Illinois Institute of Technology studying computer science. He has experience as a teaching assistant and intern at Oracle India Private Ltd where he developed automation test scripts and found application bugs. His technical skills include programming languages like Java, C++ and Python as well as technologies like HTML5, CSS3, PHP, and databases like Oracle, MySQL and MongoDB. He has created several Android and web applications as academic projects.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Enable SQL/JDBC Access to Apache Geode/GemFire Using Apache CalciteChristian Tzolov
https://springoneplatform.io/sessions/enable-sql-jdbc-access-to-apache-geode-gemfire-using-apache-calcite
When working with BigData & IoT systems we often feel the need for an established, Common Query Language.
To fill this gap some NoSql vendors are building SQL access to their systems. Building SQL engine from scratch is a daunting job and frameworks like Apache Calcite can help you with the heavy lifting. It allows you to integrate SQL parser, Cost-Based Optimizer, and JDBC with your NoSql system. Calcite has been used to empower many BigData platforms such as Hive, Spark, Flink, Drill, HBase/Phoenix to name some.
In this session I will walk you through the process of building a SQL access layer for Apache Geode (GemFire). I will share my experience, pitfalls and technical consideration like balancing between the SQL/RDBMS semantics and the design choices and limitations of In-Memory-Data-Grid systems like Geode.
Hopefully this will enable you to add SQL capabilities to your preferred NoSQL data system.
Pivotal Greenplum is a massively parallel processing (MPP) database for analytics. It provides high performance for data warehousing and big data analytics workloads. Key features include its ability to load and query data in parallel across multiple CPUs and disks, support for SQL and analytical functions and libraries like MADlib, and deployment on public clouds or on-premises. Pivotal Greenplum can be used for both structured and unstructured data and integrates with other Pivotal products like GemFire, Data Flow, and the Pivotal Data Suite for analytics workflows.
Deep Learning for Recommender Systems with Nick pentreathDatabricks
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.
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.
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Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.
Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.
What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:
- Product mindset and how balanced teams can reduce internal friction
- Creating data as a product to align with cloud-native application architectures, like microservices and serverless
- Getting started bringing lean principles into your data organization
- Balancing data usability with data protection, governance, and security
Presenter : Dormain Drewitz, Pivotal & Mike Koleno, Solstice
Sharepoint Online and Windows Azure together: Autohosted AppsAdis Jugo
Adis Jugo, a Microsoft MVP and technology advisor at PlanB, gave a presentation on autohosted apps with SharePoint Online and Windows Azure. He discussed the history of SharePoint in the cloud and different solution types. Jugo also covered SharePoint apps and how they can access SharePoint and external data through OAuth authentication without running on SharePoint servers. He explained the benefits of autohosted apps being isolated, multitenant, and fully cloud-based. Jugo concluded by taking questions and noting that apps can be upgraded independently in the cloud.
Are you working with an existing codebase? Do you want to use the cool libraries, tools or methodology you saw at this conference? This talk will show you how to combine legacy code with bleeding edge technology.
Often the use of new technology and protocols is limited by fear of change in existing applications. Yet, as developers, we want to remain relevant and have fun with all the disruptive changes in our industry.
This talk will drill-down from a high-level architectural view to the actual implementation of modern, strangling services in a legacy monolith, using DDD to define bounded contexts, in a safe and controlled manner.
Enable SQL/JDBC Access to Apache Geode/GemFire Using Apache CalciteVMware Tanzu
SpringOne Platform 2017
Christian Tzolov, Pivotal
"When working with BigData & IoT systems we often feel the need for an established, Common Query Language.
To fill this gap some NoSql vendors are building SQL access to their systems. Building SQL engine from scratch is a daunting job and frameworks like Apache Calcite can help you with the heavy lifting. It allows you to integrate SQL parser, Cost-Based Optimizer, and JDBC with your NoSql system. Calcite has been used to empower many BigData platforms such as Hive, Spark, Flink, Drill, HBase/Phoenix to name some.
In this session I will walk you through the process of building a SQL access layer for Apache Geode (GemFire). I will share my experience, pitfalls and technical consideration like balancing between the SQL/RDBMS semantics and the design choices and limitations of In-Memory-Data-Grid systems like Geode.
Hopefully this will enable you to add SQL capabilities to your preferred NoSQL data system."
Ravi Sundriyal is seeking an internship position to gain valuable experience and apply his technical skills. He has over 4 years of experience as a Senior Software Engineer for Computer Sciences Corporation in India, where he worked on the Manulife Japan project. In this role, he served as a product developer, quality assurance tester, project lead, and business analyst. He is currently pursuing a Master's degree in Computer Software Engineering at San Jose State University. His technical skills include languages like Java, Python, COBOL, and frameworks like AngularJS and NodeJS. He has worked on several academic projects involving web and mobile applications.
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
This document discusses how data science models have transitioned to the cloud to take advantage of greater computing resources. It notes that data science models are resource-intensive and traditionally required powerful local machines. The cloud allows data scientists to run models on cloud infrastructure for lower costs than high-end laptops and with access to many GPUs. Several major cloud platforms - Azure, AWS, and Google Cloud - are discussed and compared in terms of their machine learning offerings. The document also introduces Microsoft's Team Data Science Process, which aims to help data science teams collaborate more effectively on projects in the cloud.
Lambda architecture for real time big dataTrieu Nguyen
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
This document discusses secrets of enterprise data mining. It begins by defining data mining as the automated or semi-automated process of discovering patterns in data. It then discusses how data mining can be applied in various industries like telecommunications, oil and gas, and Volkswagen Group. Finally, it discusses how Microsoft offers solutions for enterprise data mining through SQL Server Analysis Services and Microsoft Azure Machine Learning.
Coming right from the Recommender Systems conference in San Francisco, I present some latest developments in the field of large scale recommendation engines and machine learning.
Webinar: SpagoBI & Big Data, a smart approach to turn data into knowledge SpagoWorld
The presentation supported the webinar focused on the smart approach adopted by SpagoBI suite to manage Big Data, delivered on October 8th, 2013 within SpagoWorld Webinar Center. http://www.spagoworld.org/
In this presentation, Kaz Ohta, Kiyoto Tamura, and Ankush Rustagi from Treasure Data describe the company's Cloud Data Warehouse service.
"The Treasure Data Cloud Data Warehouse service enables companies to get big data analytics running in days not months without specialist IT resources and for a tenth the cost of other alternatives. Traditional data warehousing solutions - even modern alternatives such as Hadoop - are too expensive, complex and take too long for many companies to implement, so the idea of quickly launching a data warehouse service that uses the power and economics of the Cloud for companies of any size, opens up a huge potential market."
Learn more at: http://treasure-data.com * Watch the presentation video: http://inside-bigdata.com/?p=3531
Treasure Data is a cloud-based big data analytics company based in Silicon Valley with about 20 employees. The document discusses Treasure Data's services and architecture, which includes collecting data from various sources using Fluentd, storing the data in a columnar format on AWS S3, and performing analytics using Hadoop and SQL queries. Treasure Data aims to simplify big data adoption through its fully-managed platform and quick setup process. Example customers discussed were able to see results within 2 weeks of signing up.
- The document discusses a presentation given by Jongwook Woo on introducing Spark and its uses for big data analysis. It includes information on Woo's background and experience with big data, an overview of Spark and its components like RDDs and task scheduling, and examples of using Spark for different types of data analysis and use cases.
Sarath Kumar Prabhakaran is a graduate student at Illinois Institute of Technology studying computer science. He has experience as a teaching assistant and intern at Oracle India Private Ltd where he developed automation test scripts and found application bugs. His technical skills include programming languages like Java, C++ and Python as well as technologies like HTML5, CSS3, PHP, and databases like Oracle, MySQL and MongoDB. He has created several Android and web applications as academic projects.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Enable SQL/JDBC Access to Apache Geode/GemFire Using Apache CalciteChristian Tzolov
https://springoneplatform.io/sessions/enable-sql-jdbc-access-to-apache-geode-gemfire-using-apache-calcite
When working with BigData & IoT systems we often feel the need for an established, Common Query Language.
To fill this gap some NoSql vendors are building SQL access to their systems. Building SQL engine from scratch is a daunting job and frameworks like Apache Calcite can help you with the heavy lifting. It allows you to integrate SQL parser, Cost-Based Optimizer, and JDBC with your NoSql system. Calcite has been used to empower many BigData platforms such as Hive, Spark, Flink, Drill, HBase/Phoenix to name some.
In this session I will walk you through the process of building a SQL access layer for Apache Geode (GemFire). I will share my experience, pitfalls and technical consideration like balancing between the SQL/RDBMS semantics and the design choices and limitations of In-Memory-Data-Grid systems like Geode.
Hopefully this will enable you to add SQL capabilities to your preferred NoSQL data system.
Pivotal Greenplum is a massively parallel processing (MPP) database for analytics. It provides high performance for data warehousing and big data analytics workloads. Key features include its ability to load and query data in parallel across multiple CPUs and disks, support for SQL and analytical functions and libraries like MADlib, and deployment on public clouds or on-premises. Pivotal Greenplum can be used for both structured and unstructured data and integrates with other Pivotal products like GemFire, Data Flow, and the Pivotal Data Suite for analytics workflows.
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In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.
Similar to Delivering a 'Big Data Ready' minimum viable product (20)
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.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
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.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
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.
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.
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
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.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
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 .