Code and patterns from building TimePiece: an Android World Clock application released under the Open Source MIT license. The talk was given at the Philly Android Alliance User Group meeting on 8/24
This document discusses building stateful applications on serverless platforms and provides several patterns for managing state in a serverless environment. The key patterns discussed are: 1) Sending the full context in event payloads to avoid database reads, 2) Using backend-as-a-service providers to manage stateful services like identity, media, and notifications, 3) Running long-running "state planes" to manage state, 4) Building workflows using state machine patterns, and 5) Using fast databases to directly manage state without delegating it. The document emphasizes that real applications always have state and the challenge is effectively delegating state when building on serverless platforms. It also discusses related topics like ensuring idempotency and implementing patterns
This document summarizes a Python script that scrapes airfare data from Kayak.com. It uses the Selenium library to control a Chrome webdriver and interact with webpage elements. The script opens each flight element to dynamically load data before scraping. It collects data like destination, time, airline from each saved flight. The data is organized into a nested list and identifiers are added before writing rows to an Excel file every 4 hours or a MySQL database daily. A Raspberry Pi was used to run the script continuously without keeping a desktop on. A front-end GUI was created with Tkinter but not connected to the backend database yet.
The document discusses building stateful applications using serverless architecture and provides 5 patterns for delegating state when using serverless: 1) Send full context in event payload, 2) Use backend as a service (BaaS) or managed services, 3) Run lightweight long-running proxies, 4) Apply state externally, and 5) Use fast transactional databases. It also provides examples for each pattern and discusses what constitutes a stateless application from a serverless perspective.
Hasura 2.0 is our biggest release since we launched.
This webinar goes over the new capabilities in our v2 release:
- Connect to multiple databases
- Generate REST APIs
- Enhanced Authorisation
- Hasura Cloud & AWS VPC Peering
Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...Chuan-Yen Chiang
This document summarizes an architecture for real-time data processing and analysis using AWS services like Kinesis, Firehose, S3, and Athena. It discusses using Kinesis to collect data, Firehose to load it into S3, and Athena to run SQL queries on the data and define schemas. While this provides flexibility, maintainability and low cost, the author notes it may not be fast enough for true real-time analysis due to limitations of Athena compared to other services. The document concludes with an invitation for questions.
A small agricultural business uses FME to streamline job processing and reporting which saves them 250 hours per year. FME allows them to download shapefiles and metadata from an API. It then transforms and joins the spatial data with information from their accounting system. This updated information is written back to SQL for reporting and the accounting system is also updated. FME also pulls data from a spatial database, joins it with invoice info, and writes a print formatted HTML report with an interactive map using Mapbox Leaflet. This new automated process with FME expedites what was previously a manual, time-consuming workflow.
This document provides an overview of Amazon Web Services (AWS) and why someone would want to learn AWS. It discusses how AWS is the largest and fastest growing public cloud platform, and that many organizations are outsourcing their IT to AWS. It also mentions that AWS certifications are very popular currently. The document then provides more details on AWS services like Lambda, serverless computing, AWS regions and availability zones, and use cases for AWS Lambda. It discusses how to build, configure and test Lambda functions, and includes an example of creating a Lambda function triggered by an S3 event.
The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
This document discusses building stateful applications on serverless platforms and provides several patterns for managing state in a serverless environment. The key patterns discussed are: 1) Sending the full context in event payloads to avoid database reads, 2) Using backend-as-a-service providers to manage stateful services like identity, media, and notifications, 3) Running long-running "state planes" to manage state, 4) Building workflows using state machine patterns, and 5) Using fast databases to directly manage state without delegating it. The document emphasizes that real applications always have state and the challenge is effectively delegating state when building on serverless platforms. It also discusses related topics like ensuring idempotency and implementing patterns
This document summarizes a Python script that scrapes airfare data from Kayak.com. It uses the Selenium library to control a Chrome webdriver and interact with webpage elements. The script opens each flight element to dynamically load data before scraping. It collects data like destination, time, airline from each saved flight. The data is organized into a nested list and identifiers are added before writing rows to an Excel file every 4 hours or a MySQL database daily. A Raspberry Pi was used to run the script continuously without keeping a desktop on. A front-end GUI was created with Tkinter but not connected to the backend database yet.
The document discusses building stateful applications using serverless architecture and provides 5 patterns for delegating state when using serverless: 1) Send full context in event payload, 2) Use backend as a service (BaaS) or managed services, 3) Run lightweight long-running proxies, 4) Apply state externally, and 5) Use fast transactional databases. It also provides examples for each pattern and discusses what constitutes a stateless application from a serverless perspective.
Hasura 2.0 is our biggest release since we launched.
This webinar goes over the new capabilities in our v2 release:
- Connect to multiple databases
- Generate REST APIs
- Enhanced Authorisation
- Hasura Cloud & AWS VPC Peering
Hands on experience in real-time data process with AWS Kinesis, Firehose, S3 ...Chuan-Yen Chiang
This document summarizes an architecture for real-time data processing and analysis using AWS services like Kinesis, Firehose, S3, and Athena. It discusses using Kinesis to collect data, Firehose to load it into S3, and Athena to run SQL queries on the data and define schemas. While this provides flexibility, maintainability and low cost, the author notes it may not be fast enough for true real-time analysis due to limitations of Athena compared to other services. The document concludes with an invitation for questions.
A small agricultural business uses FME to streamline job processing and reporting which saves them 250 hours per year. FME allows them to download shapefiles and metadata from an API. It then transforms and joins the spatial data with information from their accounting system. This updated information is written back to SQL for reporting and the accounting system is also updated. FME also pulls data from a spatial database, joins it with invoice info, and writes a print formatted HTML report with an interactive map using Mapbox Leaflet. This new automated process with FME expedites what was previously a manual, time-consuming workflow.
This document provides an overview of Amazon Web Services (AWS) and why someone would want to learn AWS. It discusses how AWS is the largest and fastest growing public cloud platform, and that many organizations are outsourcing their IT to AWS. It also mentions that AWS certifications are very popular currently. The document then provides more details on AWS services like Lambda, serverless computing, AWS regions and availability zones, and use cases for AWS Lambda. It discusses how to build, configure and test Lambda functions, and includes an example of creating a Lambda function triggered by an S3 event.
The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
The purpose of this study is to develop a system which will assist a user to determine if a location can be entitled as a “Safe” residence or not. The output will be based on an analysis carried out on the local crime history of the city. This involves examining a huge geolocation data and zeroing down to a single area. The area with majority crime incidents will be highlighted as Unsafe. Clicking/hovering on a single record will display name, associated crime and its rank depending on number of crimes occurred. Big Data Hadoop and Hive systems are implemented in Azure for the analysis.
Keywords: Hadoop, Big Data, Hive, Azure
Kibana is a data visualization tool that is part of the ELK stack (Elasticsearch, Logstash, Kibana) and allows users to search, analyze, and visualize data stored in Elasticsearch. The document discusses Kibana's essential features including Discover to query data, Visualize to create visualizations, and Dashboard to combine them. It also covers additional tools like Dev Tools, X-Pack plugins, and Machine Learning capabilities.
The document summarizes a project analyzing connections between GitHub users by constructing a graph based on user collaborations to repositories. Over 1TB of data including users, followers, repositories and events was processed. A graph with users as vertices and collaborations as edges was created and analyzed to find clusters using connected components and influential users using PageRank. Challenges included unstructured data schemas and memory issues when processing the large dataset.
This document describes how to resize images using an AWS Lambda function triggered by S3 events. It involves creating two S3 buckets, one for source images and one for resized images. A Lambda function is configured with an IAM role to access S3 and CloudWatch, and is set as a trigger for objects added to the source bucket. Testing confirms the function resizes images to thumbnails and saves them to the destination bucket on object uploads.
Etosha - Data Asset Manager : Status and road mapDr. Mirko Kämpf
The document provides an overview and roadmap for the first release of an open data asset manager called Etosha MDS. Key points include:
- Etosha MDS will expose metadata about datasets to enable discovery, exploration, and risk analysis of data assets.
- The first release will focus on collecting and exposing schema, statistics, and semantic annotations about datasets using tools like SPARQL and a graph browser.
- Future releases will integrate datasets across Hadoop clusters using a shared semantic knowledge graph and dataset integration layer following the data as a service paradigm.
Docker Geneva - Your own Kubernetes controller, not only in Go!Nicolas Fränkel
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
Migrating from Monolithic to Serverless (Kostas Katsikas) - GreeceJS #22GreeceJS
The document discusses different architectural approaches for building applications including monolithic, service-oriented, microservices, and serverless architectures. It provides examples of how common application components like APIs, databases, storage, authentication, caching, and more can be implemented in a serverless architecture using AWS services like Lambda, API Gateway, DynamoDB, S3, Cognito, CloudFront, SQS, SNS, and others. The document is presented by Konstantinos Katsikas and concludes by thanking the reader.
From logging to monitoring to reactive insights - C Schneidermfrancis
OSGi Community Event 2017 Presentation by Christian Schneider [Adobe]
In a highly distributed world it is crucial to have the best possible insights into your application.
In the old days we achieved this using plain logging and jmx. This does not scale well to highly distributed processing like in microservices though.
This talk shows how to pimp you OSGi based systems with top notch monitoring and tracing capabilities. Using Apache Karaf Decanter we let karaf instances log into a common elastic search instance with almost zero effort and get insights using kibana. The Apache CXF REST and SOAP integrations allow to log and correlate service requests and replies.
Building on top of this we use the OSGi based reactive components framework and reactor.io streams to provide complex event processing capabilties. A typical use case for this is to describe and monitor service level agreements.
Build Machine Learning Models Quickly & Easily with Amazon SageMaker & Perisc...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build Machine Learning Models Quickly & Easily with Amazon SageMaker & Periscope Data
Building machine learning models is quick and easy. In this session with AWS and Periscope Data and featuring Rover, you’ll learn how Periscope Data enables rapid iteration for data preparation, especially during data cleansing, data transformation, and feature engineering. We’ll demonstrate the integration with Amazon SageMaker in a live demo, where you’ll see how to build, train, and deploy machine learning models. Brandyn Lee, data scientist at Rover, will walk you through how he solves complex machine problem with these tools and the impact that it has on his day-to-day.
Speakers:
Britton Stamper - Sales Engineer, Periscope Data
Brandyn Lee - Data Scientist, Rover
Sharing our work on optimizing PV energy yield leveraging IIoT, serverless framework, Elasticsearch and numerous open source tools with Los Angeles' Elastic User Group
Charla desarrollo de apps con sharepoint y office 365Luis Valencia
This document discusses developing apps in SharePoint 2013. It covers types of apps from a user interface and hosting perspective. It also discusses development tools like Napa, Visual Studio, CSOM vs REST, and the Cloud Application Model. Demos are provided of SharePoint hosted apps, including using the chrome control, full immersive apps, and app parts. Authentication and authorization with OAuth is also summarized.
Bridging the gap from Wikipedia to scholarly sources: a simple discovery solu...Valerie Forrestal
This presentation discusses the creation of a javascript bookmarklet that executes a search of library resources from any web page.
Many user searches begin with searches on the internet, often in Google and Wikipedia. For users to search the library resources, they first need to locate the library website, find the appropriate search tool and then execute their search. To make it easier for students to search the library resources, we created a Javascript bookmarket that eliminates the step of having to go to the library website first before searching.
The bookmarket provides an important bridge between common search behaviors (especially among undergraduates), and the "deep web" content located in library-funded, proprietary databases, thus easing their transition into scholarly research. The bookmarklet can be dragged-and-dropped into any browser, after which a search can then be initiated from any webpage the user visits. When the bookmarklet is clicked, the search terms default to the title of the page, and a prompt is displayed that allows the user to change the terms.
Building a scalable search architecture in share point 2013Terrence Nguyen
This document discusses building a scalable search architecture in SharePoint 2013. It begins with an overview of the speaker and agenda. It then addresses common misunderstandings around search architecture before explaining the logical components of search - crawl, content processing, analytics processing, index, administration, and query processing. It provides examples of how to design the architecture based on assessment of content size and user load. Finally, it offers guidance on implementing and verifying the search architecture using PowerShell.
DataFinder: A Python Application for Scientific Data ManagementAndreas Schreiber
DataFinder is a Python application developed by the German Aerospace Center (DLR) for efficient management of large scientific and technical data sets. It provides a structured way to organize data through customizable data models and flexible use of distributed storage resources. DataFinder uses a client-server model with a WebDAV server to store metadata and data. It allows integration of data management into scientific workflows through a Python API and scripting.
The document discusses Apache CouchDB, a NoSQL database management system. It begins with an overview of NoSQL databases and their characteristics like being non-relational, distributed, and horizontally scalable. It then provides details on CouchDB, describing it as a document-oriented database using JSON documents and JavaScript for queries. The document outlines CouchDB's features like schema-free design, ACID compliance, replication, RESTful API, and MapReduce functions. It concludes with examples of CouchDB use cases and steps to set up a sample project using a CouchDB instance with sample employee data and views/shows to query the data.
The document discusses Microsoft's ALM Search service architecture and design. It describes plans for the search indexing and query pipelines, including using Elastic Search for indexing and querying across artifacts. It addresses security, performance, deployment topology, and futures like semantic search and integration with on-premise systems. Key points include indexing millions of files in hours, scaling out the indexing pipeline, and supporting cross-account and public repository search.
Android architecture components with cloud firestorePankaj Rai
Session on what are architecture components and how to use them. Apart from Room persistence library how to use cloud firestore to store and retrieve data which supports offline capability by default.
Also mentioned about the new announcement made by firebase team during the firebase summit 2019 and how to use firebase extension and app distribution.
Author: Stefan Papp, Data Architect at “The unbelievable Machine Company“. An overview of Big Data Processing engines with a focus on Apache Spark and Apache Flink, given at a Vienna Data Science Group meeting on 26 January 2017. Following questions are addressed:
• What are big data processing paradigms and how do Spark 1.x/Spark 2.x and Apache Flink solve them?
• When to use batch and when stream processing?
• What is a Lambda-Architecture and a Kappa Architecture?
• What are the best practices for your project?
Copy of the slides from the Advanced Web Development Workshop presented by Ed Bachta, Charlie Moad and Robert Stein of the Indianapolis Museum of Art during the Museums and the Web 2008 conference in Montreal
The purpose of this study is to develop a system which will assist a user to determine if a location can be entitled as a “Safe” residence or not. The output will be based on an analysis carried out on the local crime history of the city. This involves examining a huge geolocation data and zeroing down to a single area. The area with majority crime incidents will be highlighted as Unsafe. Clicking/hovering on a single record will display name, associated crime and its rank depending on number of crimes occurred. Big Data Hadoop and Hive systems are implemented in Azure for the analysis.
Keywords: Hadoop, Big Data, Hive, Azure
Kibana is a data visualization tool that is part of the ELK stack (Elasticsearch, Logstash, Kibana) and allows users to search, analyze, and visualize data stored in Elasticsearch. The document discusses Kibana's essential features including Discover to query data, Visualize to create visualizations, and Dashboard to combine them. It also covers additional tools like Dev Tools, X-Pack plugins, and Machine Learning capabilities.
The document summarizes a project analyzing connections between GitHub users by constructing a graph based on user collaborations to repositories. Over 1TB of data including users, followers, repositories and events was processed. A graph with users as vertices and collaborations as edges was created and analyzed to find clusters using connected components and influential users using PageRank. Challenges included unstructured data schemas and memory issues when processing the large dataset.
This document describes how to resize images using an AWS Lambda function triggered by S3 events. It involves creating two S3 buckets, one for source images and one for resized images. A Lambda function is configured with an IAM role to access S3 and CloudWatch, and is set as a trigger for objects added to the source bucket. Testing confirms the function resizes images to thumbnails and saves them to the destination bucket on object uploads.
Etosha - Data Asset Manager : Status and road mapDr. Mirko Kämpf
The document provides an overview and roadmap for the first release of an open data asset manager called Etosha MDS. Key points include:
- Etosha MDS will expose metadata about datasets to enable discovery, exploration, and risk analysis of data assets.
- The first release will focus on collecting and exposing schema, statistics, and semantic annotations about datasets using tools like SPARQL and a graph browser.
- Future releases will integrate datasets across Hadoop clusters using a shared semantic knowledge graph and dataset integration layer following the data as a service paradigm.
Docker Geneva - Your own Kubernetes controller, not only in Go!Nicolas Fränkel
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
Migrating from Monolithic to Serverless (Kostas Katsikas) - GreeceJS #22GreeceJS
The document discusses different architectural approaches for building applications including monolithic, service-oriented, microservices, and serverless architectures. It provides examples of how common application components like APIs, databases, storage, authentication, caching, and more can be implemented in a serverless architecture using AWS services like Lambda, API Gateway, DynamoDB, S3, Cognito, CloudFront, SQS, SNS, and others. The document is presented by Konstantinos Katsikas and concludes by thanking the reader.
From logging to monitoring to reactive insights - C Schneidermfrancis
OSGi Community Event 2017 Presentation by Christian Schneider [Adobe]
In a highly distributed world it is crucial to have the best possible insights into your application.
In the old days we achieved this using plain logging and jmx. This does not scale well to highly distributed processing like in microservices though.
This talk shows how to pimp you OSGi based systems with top notch monitoring and tracing capabilities. Using Apache Karaf Decanter we let karaf instances log into a common elastic search instance with almost zero effort and get insights using kibana. The Apache CXF REST and SOAP integrations allow to log and correlate service requests and replies.
Building on top of this we use the OSGi based reactive components framework and reactor.io streams to provide complex event processing capabilties. A typical use case for this is to describe and monitor service level agreements.
Build Machine Learning Models Quickly & Easily with Amazon SageMaker & Perisc...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build Machine Learning Models Quickly & Easily with Amazon SageMaker & Periscope Data
Building machine learning models is quick and easy. In this session with AWS and Periscope Data and featuring Rover, you’ll learn how Periscope Data enables rapid iteration for data preparation, especially during data cleansing, data transformation, and feature engineering. We’ll demonstrate the integration with Amazon SageMaker in a live demo, where you’ll see how to build, train, and deploy machine learning models. Brandyn Lee, data scientist at Rover, will walk you through how he solves complex machine problem with these tools and the impact that it has on his day-to-day.
Speakers:
Britton Stamper - Sales Engineer, Periscope Data
Brandyn Lee - Data Scientist, Rover
Sharing our work on optimizing PV energy yield leveraging IIoT, serverless framework, Elasticsearch and numerous open source tools with Los Angeles' Elastic User Group
Charla desarrollo de apps con sharepoint y office 365Luis Valencia
This document discusses developing apps in SharePoint 2013. It covers types of apps from a user interface and hosting perspective. It also discusses development tools like Napa, Visual Studio, CSOM vs REST, and the Cloud Application Model. Demos are provided of SharePoint hosted apps, including using the chrome control, full immersive apps, and app parts. Authentication and authorization with OAuth is also summarized.
Bridging the gap from Wikipedia to scholarly sources: a simple discovery solu...Valerie Forrestal
This presentation discusses the creation of a javascript bookmarklet that executes a search of library resources from any web page.
Many user searches begin with searches on the internet, often in Google and Wikipedia. For users to search the library resources, they first need to locate the library website, find the appropriate search tool and then execute their search. To make it easier for students to search the library resources, we created a Javascript bookmarket that eliminates the step of having to go to the library website first before searching.
The bookmarket provides an important bridge between common search behaviors (especially among undergraduates), and the "deep web" content located in library-funded, proprietary databases, thus easing their transition into scholarly research. The bookmarklet can be dragged-and-dropped into any browser, after which a search can then be initiated from any webpage the user visits. When the bookmarklet is clicked, the search terms default to the title of the page, and a prompt is displayed that allows the user to change the terms.
Building a scalable search architecture in share point 2013Terrence Nguyen
This document discusses building a scalable search architecture in SharePoint 2013. It begins with an overview of the speaker and agenda. It then addresses common misunderstandings around search architecture before explaining the logical components of search - crawl, content processing, analytics processing, index, administration, and query processing. It provides examples of how to design the architecture based on assessment of content size and user load. Finally, it offers guidance on implementing and verifying the search architecture using PowerShell.
DataFinder: A Python Application for Scientific Data ManagementAndreas Schreiber
DataFinder is a Python application developed by the German Aerospace Center (DLR) for efficient management of large scientific and technical data sets. It provides a structured way to organize data through customizable data models and flexible use of distributed storage resources. DataFinder uses a client-server model with a WebDAV server to store metadata and data. It allows integration of data management into scientific workflows through a Python API and scripting.
The document discusses Apache CouchDB, a NoSQL database management system. It begins with an overview of NoSQL databases and their characteristics like being non-relational, distributed, and horizontally scalable. It then provides details on CouchDB, describing it as a document-oriented database using JSON documents and JavaScript for queries. The document outlines CouchDB's features like schema-free design, ACID compliance, replication, RESTful API, and MapReduce functions. It concludes with examples of CouchDB use cases and steps to set up a sample project using a CouchDB instance with sample employee data and views/shows to query the data.
The document discusses Microsoft's ALM Search service architecture and design. It describes plans for the search indexing and query pipelines, including using Elastic Search for indexing and querying across artifacts. It addresses security, performance, deployment topology, and futures like semantic search and integration with on-premise systems. Key points include indexing millions of files in hours, scaling out the indexing pipeline, and supporting cross-account and public repository search.
Android architecture components with cloud firestorePankaj Rai
Session on what are architecture components and how to use them. Apart from Room persistence library how to use cloud firestore to store and retrieve data which supports offline capability by default.
Also mentioned about the new announcement made by firebase team during the firebase summit 2019 and how to use firebase extension and app distribution.
Author: Stefan Papp, Data Architect at “The unbelievable Machine Company“. An overview of Big Data Processing engines with a focus on Apache Spark and Apache Flink, given at a Vienna Data Science Group meeting on 26 January 2017. Following questions are addressed:
• What are big data processing paradigms and how do Spark 1.x/Spark 2.x and Apache Flink solve them?
• When to use batch and when stream processing?
• What is a Lambda-Architecture and a Kappa Architecture?
• What are the best practices for your project?
Copy of the slides from the Advanced Web Development Workshop presented by Ed Bachta, Charlie Moad and Robert Stein of the Indianapolis Museum of Art during the Museums and the Web 2008 conference in Montreal
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.OrgCloudera, Inc.
The document describes TrendingTopics.org, a website that detects trending topics using Wikipedia page view data analyzed with Hadoop on Amazon EC2. It loads over 1TB of Wikipedia page view logs into Hadoop on EC2, uses Hive and Python to perform daily timelines and trend detection, and hosts the results on a Ruby on Rails front end also running on EC2. All the code is open source and hosted on Github.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
DataFinder is software developed by the German Aerospace Center (DLR) to help scientists and engineers efficiently manage and organize their large and growing scientific data sets. It provides a structured way to organize data through customizable data models and metadata, and can integrate various storage resources. DataFinder was created in Python due to its ease of use and maintainability. It uses a client-server model with a WebDAV server to manage metadata and data structures, and can access different storage backends. Customizations through Python scripts allow users to automate tasks and integrate it into their workflows.
This document provides an overview and agenda for a presentation on integrating Drupal and SharePoint. The presentation includes a primer on SharePoint components and terminology, use cases for Drupal integration, concepts for the SharePoint module, a demo, current status and further work. The SharePoint module allows consuming and publishing SharePoint content from Drupal using REST, web services or the client object model.
The document describes an Android application architecture that was developed. It includes consuming a REST API with Retrofit, parsing response data with Jackson, communicating between components using an event bus (Otto was chosen), and ensuring events are received on the main thread. The architecture abstracts away network calls, handles different event types through inheritance, and provides a unified interface through a BusManager facade.
DataFinder concepts and example: General (20100503)Data Finder
DataFinder is a lightweight client-server solution for centralized data management. It was created by the German Aerospace Center (DLR) to address the problems of absent data organization structures and no centralized policy for data management. DataFinder provides graphical user interfaces and uses a logical data store concept to organize data across distributed storage locations according to a configurable data model. It can be customized through Python scripts to integrate with different environments and automate tasks like data migration.
Customizing the SharePoint 2013 user interface with JavaScript - Chris OBrienChris O'Brien
Covers several approaches for user interface customization in SP2013 - using JSLink to customize a list and/or view, creating custom Display Templates for the Content Search web part, and different approaches for customizing the search hover panel.
The document discusses various topics related to SharePoint including architecture, configuring and managing sites, content types, lists, libraries, master pages, provisioning, web parts, workflows, Excel services, Business Data Catalog, and enhancements in SharePoint 2010. Key components include farms, web applications, and site collections which make up the SharePoint hierarchy. It also covers developing workflows, using the business data connectivity service, and integrating Excel and other business intelligence features.
With SharePoint 2013 just around the corner a plethora of new features for developers will also become available. Starting with enhancements in Visual Studio 2012 for SharePoint developer, iterating through the new SharePoint REST and OData, WCF Data Services framework, Client Side/JavaScript Object Model (CSOM/JSOM), new WCF service for BCS or Remote Event Receivers, ending with building applications for the new tore, we will walk you through you what you need to get your current skillset updated for the SharePoint 2013.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
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.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
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
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.
2. What is TimePiece World Time lookup without API calls Visually interesting (fun with animation) Open Source (under the MIT license)
3. Technical breakdown Local SQLite database with City / Java TimeZone Id mappings JODA DateTime library Search Custom List Renderers The usual app stuff: User Preferences Menus Intro screens Context menus
5. Steps Add SQLite to your assets folder Copy the database to your app’s data folder Open the database (extend SQLiteOpenHelper) Create a ContentProvider from this Database
7. Create a searchable config xml file Create an Activity to handle the search Add the Search config and activity to the activity that you want to trigger search on Implementing Search
14. Building Custom Lists Create a List View or a ListActivity Create an Adapter for populating the List Populate the List Adapter Refresh the Adapter every 60 seconds
21. Remember: Animations only effect the rendering buffers so objects don’t actually change in position itself If you don’t set setFillAfter, the animated object will return to its location/transformation as before animation started Leverage LayoutAnimations as well as regular animations
22. Releasing to the Market Use Android Asset Studio to create your icons Broken Market search: Joining words is probably a bad idea When adding images to your app’s description, make sure the first one is most representative (for third party market indexers like Chomp)