Do something in 5 with gas 8-copy between databasesBruce McPherson
This document provides instructions for copying data between databases in 5 minutes using Apps Script. It describes opening a Google Sheet using the database abstraction library, getting all the data, opening a MongoDB database on MongoLab, writing the sheet data to the MongoDB database, and verifying the data was copied correctly. The document then suggests copying the data to a Google Drive file as homework using the driver sheet and driver drive libraries instead of MongoLab.
Do something in 5 with gas 9-copy between databases with oauth2Bruce McPherson
Google Apps Script tutorial to copy between abstracted databases. In this example, one of the databases (Google DataStore) needs OAuth2 authentication. You should be able to complete this tutorial in about 5 mins and a few lines of code.
OAuth2 Goa library for Apps Script tutorial including how to use the Google Developer console dashboard. Handles Google and other providers as well as Service accounts.
Google apps script database abstraction exposed versionBruce McPherson
This document describes a database abstraction library for Google Apps Script that provides a consistent API for NoSQL databases. It allows code to be reused across different database backends by handling queries, authentication, caching, and more transparently. The library exposes the capabilities through a JSON REST API that can be accessed from other programming languages. It also includes a VBA client library that translates VBA calls into requests to the JSON API, allowing VBA code to access databases in the same way as Google Apps Script.
JavaScript client API for Google Apps Script API primerBruce McPherson
An API for JavaScript/jQuery client webapps providing CRUD access to Google Apps Script scriptDB - a free noSQL databases. This adds to a VBA API for Excel already published. Now Excel, Google Apps Script and JavaScript clients can share the same noSQL database and data.
This document describes a database abstraction library for Google Apps Script that allows using different backend databases like Google Sheets, Drive files, and noSQL databases with the same code. It provides caching and handles common queries and operations with only 6 simple methods. Different backends can be accessed by getting a handle to the specific backend using the DataHandler class. Data can also be copied between backends and backends are accessible via a REST API.
MongoDB .local Toronto 2019: Using Change Streams to Keep Up with Your DataMongoDB
Immediate feedback is an essential part of modern application development where developers want to sync across platforms, systems, and users to provide better end-user experiences. Change streams empower developers to easily leverage the power of MongoDB's internal real-time functionality to react to relevant data changes immediately. Change streams also provide the backbone of MongoDB Atlas triggers. This session introduces change streams and walks you through developing with them. We will discuss use cases, integrating with Kafka, and explore how to make good architectural decisions around this new functionality.
MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...MongoDB
Adeo et en particulier Leroy Merlin utilisent massivement MongoDB pour propulser de nombreuses applications et en particulier son site web leroymerlin.fr.
Emmanuel Dieval Ingénieur Software chez ADEO, présentera le nouveau système au coeur de la publication de l'offre Leroy Merlin: OPUS.
OPUS s'appuie particulièrement sur MongoDB pour la construction des pages de famille de produits tout en supportant un important flux de données journalier.
Après un rappel sur les pipelines d'agrégation et une présentation de MongoDB Atlas par Maxime Beugnet, Developer Advocate chez MongoDB, Emmanuel parlera de l'utilisation des pipelines d'agrégation pour la construction des pages de famille de produits, mais aussi de Google Cloud Platform et des avantages à utiliser MongoDB Atlas.
Do something in 5 with gas 4- Get your analytics profiles to a spreadsheetBruce McPherson
Another in the 'do something useful with Google Apps Script' series. This time you'll see how to use the Analytics service and use exponential backoff to mitigate for quota rate limiting.
Do something in 5 minutes with gas 1-use spreadsheet as databaseBruce McPherson
Here's one in a series of tutorials where you can do something useful from scratch in 5 minutes using Google Apps Script. This example shows how to use a Google Spreadsheet as a database
Matt Jarvis - Unravelling Logs: Log Processing with Logstash and Riemann Danny Abukalam
Unravelling Logs discusses log file analysis and centralized log management. Traditional log files are collected from various OpenStack services running on controllers, network nodes, and compute nodes. These logs are ingested and structured using Logstash, indexed with Elasticsearch, and analyzed with Kibana. Log Courier is used to ship logs lightly and securely to Logstash. Riemann processes the logs as events and can trigger alerts and notifications through integrations like PagerDuty and email. Centralized logging provides benefits like troubleshooting, security auditing, and reporting across an infrastructure.
[AI04] Scaling Machine Learning to Big Data Using SparkML and SparkRde:code 2017
Azure's HDInsight provides an easy way to process big data using Spark, and learn from it using Machine Learning. See SparkML in action, and learn how to use R and Python at scale, within Jupyter.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Machine Learning (機械学習)/Microsoft Azure
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Using Cerberus and PySpark to validate semi-structured datasetsBartosz Konieczny
This short presentation shows one of ways to to integrate Cerberus and PySpark. It was initially given at Paris.py meetup (https://www.meetup.com/Paris-py-Python-Django-friends/events/264404036/)
MongoDB: Comparing WiredTiger In-Memory Engine to RedisJason Terpko
This presentation will compare WiredTiger’s In-Memory Engine to Redis. We will review characteristics of each data store, how they are similar, and different. Understanding the similarities and differences will help you decide which data store is best suited for your key-value store needs.
The document discusses various formats for accessing REST web services including URI and HTTP methods. It provides code examples for making GET requests to a REST API from C# using HttpClient and jQuery AJAX calls. The examples also demonstrate how to add authentication headers to the requests. Links are included for an e-book on ASP.NET Web API and the author's Twitter profile.
A C# program connects to a SQL database, runs a query to select data from a table where the name is 'Khubaib', reads the results and displays the name, age and image URL in labels and an image on a web form. It opens a connection to the database, executes the query with a data reader, reads each row and extracts the values from the selected columns, then closes the connection.
Benchx: An XQuery benchmarking web application Andy Bunce
A system to record query performance of XQuery statements running on the BaseX http:basex.org XML database. It uses Angular on the client side and RESTXQ on the server.
This document provides an overview and instructions for deploying, upgrading, and troubleshooting a MongoDB sharded cluster. It describes the components of a sharded cluster including shards, config servers, and mongos processes. It provides recommendations for initial deployment including using replica sets for shards and config servers, DNS names instead of IPs, and proper user authorization. The document also outlines best practices for upgrading between minor and major versions, including stopping the balancer, upgrading processes in rolling fashion, and handling incompatible changes when downgrading major versions.
A real-world Relay application in production - Stefano Masini - Codemotion Am...Codemotion
This is the tale of how we, at Balsamiq, rolled out our new web-app, from greenfield to production. A real-world application with real-time collaboration, built using Relay, Redux, React-rendered html emails, node.js, Redis, etc., running on AWS using Convox. Even at small/medium scale, if you're serious about building a good product you will eventually have to build a complex stack. I will share the details of the services we use, how we make them fit together and what we learned in the process.
This document discusses how to connect to different database types from QTP scripts using ADO (ActiveX Data Objects). It provides code samples for connecting to MS Access, SQL Server, Oracle, MySQL, Excel, and Sybase databases. The code samples show how to open an ADO connection and recordset, retrieve field values from the recordset, and populate them into application window controls before closing the connection. Connecting to databases allows test scripts to work with real data during automation testing.
MongoDB Chunks - Distribution, Splitting, and MergingJason Terpko
MongoDB Chunks – Distribution, Splitting, and Merging This presentation will discuss how the distribution of chunks can create scenarios where you may need to manually move, split, or merge chunks in your sharded cluster. Scenarios requiring these actions can exist with both optimal and sub-optimal shard keys. This can be especially true when a new change has been introduced to the workload. It may cause hot-spotting, unbalanced storage, or empty chunks.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
MongoDB .local Toronto 2019: Using Change Streams to Keep Up with Your DataMongoDB
Immediate feedback is an essential part of modern application development where developers want to sync across platforms, systems, and users to provide better end-user experiences. Change streams empower developers to easily leverage the power of MongoDB's internal real-time functionality to react to relevant data changes immediately. Change streams also provide the backbone of MongoDB Atlas triggers. This session introduces change streams and walks you through developing with them. We will discuss use cases, integrating with Kafka, and explore how to make good architectural decisions around this new functionality.
MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...MongoDB
Adeo et en particulier Leroy Merlin utilisent massivement MongoDB pour propulser de nombreuses applications et en particulier son site web leroymerlin.fr.
Emmanuel Dieval Ingénieur Software chez ADEO, présentera le nouveau système au coeur de la publication de l'offre Leroy Merlin: OPUS.
OPUS s'appuie particulièrement sur MongoDB pour la construction des pages de famille de produits tout en supportant un important flux de données journalier.
Après un rappel sur les pipelines d'agrégation et une présentation de MongoDB Atlas par Maxime Beugnet, Developer Advocate chez MongoDB, Emmanuel parlera de l'utilisation des pipelines d'agrégation pour la construction des pages de famille de produits, mais aussi de Google Cloud Platform et des avantages à utiliser MongoDB Atlas.
Do something in 5 with gas 4- Get your analytics profiles to a spreadsheetBruce McPherson
Another in the 'do something useful with Google Apps Script' series. This time you'll see how to use the Analytics service and use exponential backoff to mitigate for quota rate limiting.
Do something in 5 minutes with gas 1-use spreadsheet as databaseBruce McPherson
Here's one in a series of tutorials where you can do something useful from scratch in 5 minutes using Google Apps Script. This example shows how to use a Google Spreadsheet as a database
Matt Jarvis - Unravelling Logs: Log Processing with Logstash and Riemann Danny Abukalam
Unravelling Logs discusses log file analysis and centralized log management. Traditional log files are collected from various OpenStack services running on controllers, network nodes, and compute nodes. These logs are ingested and structured using Logstash, indexed with Elasticsearch, and analyzed with Kibana. Log Courier is used to ship logs lightly and securely to Logstash. Riemann processes the logs as events and can trigger alerts and notifications through integrations like PagerDuty and email. Centralized logging provides benefits like troubleshooting, security auditing, and reporting across an infrastructure.
[AI04] Scaling Machine Learning to Big Data Using SparkML and SparkRde:code 2017
Azure's HDInsight provides an easy way to process big data using Spark, and learn from it using Machine Learning. See SparkML in action, and learn how to use R and Python at scale, within Jupyter.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Machine Learning (機械学習)/Microsoft Azure
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Using Cerberus and PySpark to validate semi-structured datasetsBartosz Konieczny
This short presentation shows one of ways to to integrate Cerberus and PySpark. It was initially given at Paris.py meetup (https://www.meetup.com/Paris-py-Python-Django-friends/events/264404036/)
MongoDB: Comparing WiredTiger In-Memory Engine to RedisJason Terpko
This presentation will compare WiredTiger’s In-Memory Engine to Redis. We will review characteristics of each data store, how they are similar, and different. Understanding the similarities and differences will help you decide which data store is best suited for your key-value store needs.
The document discusses various formats for accessing REST web services including URI and HTTP methods. It provides code examples for making GET requests to a REST API from C# using HttpClient and jQuery AJAX calls. The examples also demonstrate how to add authentication headers to the requests. Links are included for an e-book on ASP.NET Web API and the author's Twitter profile.
A C# program connects to a SQL database, runs a query to select data from a table where the name is 'Khubaib', reads the results and displays the name, age and image URL in labels and an image on a web form. It opens a connection to the database, executes the query with a data reader, reads each row and extracts the values from the selected columns, then closes the connection.
Benchx: An XQuery benchmarking web application Andy Bunce
A system to record query performance of XQuery statements running on the BaseX http:basex.org XML database. It uses Angular on the client side and RESTXQ on the server.
This document provides an overview and instructions for deploying, upgrading, and troubleshooting a MongoDB sharded cluster. It describes the components of a sharded cluster including shards, config servers, and mongos processes. It provides recommendations for initial deployment including using replica sets for shards and config servers, DNS names instead of IPs, and proper user authorization. The document also outlines best practices for upgrading between minor and major versions, including stopping the balancer, upgrading processes in rolling fashion, and handling incompatible changes when downgrading major versions.
A real-world Relay application in production - Stefano Masini - Codemotion Am...Codemotion
This is the tale of how we, at Balsamiq, rolled out our new web-app, from greenfield to production. A real-world application with real-time collaboration, built using Relay, Redux, React-rendered html emails, node.js, Redis, etc., running on AWS using Convox. Even at small/medium scale, if you're serious about building a good product you will eventually have to build a complex stack. I will share the details of the services we use, how we make them fit together and what we learned in the process.
This document discusses how to connect to different database types from QTP scripts using ADO (ActiveX Data Objects). It provides code samples for connecting to MS Access, SQL Server, Oracle, MySQL, Excel, and Sybase databases. The code samples show how to open an ADO connection and recordset, retrieve field values from the recordset, and populate them into application window controls before closing the connection. Connecting to databases allows test scripts to work with real data during automation testing.
MongoDB Chunks - Distribution, Splitting, and MergingJason Terpko
MongoDB Chunks – Distribution, Splitting, and Merging This presentation will discuss how the distribution of chunks can create scenarios where you may need to manually move, split, or merge chunks in your sharded cluster. Scenarios requiring these actions can exist with both optimal and sub-optimal shard keys. This can be especially true when a new change has been introduced to the workload. It may cause hot-spotting, unbalanced storage, or empty chunks.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
This document summarizes a student project on using a job shop scheduling model for production scheduling. It outlines the project structure, background on using efficient scheduling to improve system performance and complexity of job shop scheduling. It defines job shop versus flow shop scheduling and describes using a genetic algorithm approach with Evolver software to minimize completion time for a dynamic job shop scheduling problem. Computation results show the best solution was obtained using specific crossover rate and mutation rate parameters in Evolver. The conclusion advises decision makers that minimizing makespan is difficult and provides options for continuing the work.
The document provides information about scheduling techniques for service and manufacturing processes. It discusses front-office and back-office processes, performance measures like flow time and makespan, sequencing jobs using rules like earliest due date and shortest processing time, and priority sequencing rules like critical ratio and slack per remaining operations. Examples are provided to illustrate how to sequence jobs using different rules and compare their performance.
This document discusses scheduling concepts and methods. It provides examples of workforce scheduling using different priority rules to allocate employees over time. It also discusses operations scheduling and different types of manufacturing processes like job shops and flow shops. Priority sequencing rules like earliest due date, shortest processing time and critical ratio are explained and compared using examples.
This document discusses job shop scheduling, which involves scheduling jobs at general purpose work stations. It describes factors like arrival patterns, number of machines, work sequences, and performance criteria. For arrival patterns, it notes static and dynamic types. For work sequences, it discusses fixed and random types. It provides examples of performance criteria like makespan and machine utilization. It also introduces Gantt charts for scheduling displays and discusses scenarios like scheduling n jobs on 1 machine, n jobs on a flow shop with 2 machines, and n jobs on m machines in general. Heuristics for the n jobs on m machines case include shortest processing time, earliest due date, and critical ratio rules.
Take Better Care of Library Data and Spreadsheets with Google Visualization A...Bohyun Kim
Presentation given at 2013 LITA Forum on Nov 8, 2013. http://www.ala.org/lita/conferences/forum/2013 ; Example files are at http://github.com/bohyunkim/examples
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Alkis Vazacopoulos
Presented in this document is a description of how to develop and deploy industrial applications in a timely fashion using Excel/VBA as the user-interface (UI) and systems-integration (SI) system, IMPL as the industrial modeller and CPLEX as the commercial solver. A small jobshop scheduling example is overviewed to help describe to some extent, the details of this advanced decision-making application where this type of problem can be found in both the manufacturing and process industries.
The purpose of developing and deploying quickly is to acquire feedback from the end-users, to assess the difficulty and tractability of the problem, to ascertain the expected costs and benefits of the application and to address any other issues and requirements regarding the project as a whole as soon as possible. For some projects, proof-of-concepts, prototypes and/or pilots are also useful and these should also be performed ASAP as well using the same approach highlighted here. Ultimately, once a business problem solution has been achieved and full or partial benefits have been captured, then a more robust and sophisticated end-user experience and system architecture can be implemented in the operating system and computer programming environment of choice which will hopefully enhance and maintain the solution over its expected life-cycle.
SQL to NoSQL Best Practices with Amazon DynamoDB - AWS July 2016 Webinar Se...Amazon Web Services
Applications have traditionally stored data in a relational database management system (RDBMS) and have used a Structured Query Language (SQL) to retrieve and update that data. The growth of “internet scale” apps, such as e-commerce, social media, mobile apps, and the rise of big data have increased data throughput demands beyond the range of traditional relational databases. Non-relational (NoSQL) databases enables your application to scale more cost effectively, even for extraordinarily high demand. Amazon DynamoDB is a fully managed NoSQL database service that lets you focus on your app so you don’t have to worry about hardware acquisition or database management and lets you scale down your costs for off-peak periods. In this webinar, we’ll describe common database tasks, then compare and contrast SQL with equivalent DynamoDB operations.
Learning Objectives:
• Why consider the switch from SQL to NoSQL?
• Benefits of Amazon’s NoSQL database service
• Common SQL database operations and their DynamoDB equivalents
by Rohan Dubal, Software Development Engineer, AWS
One of the biggest time sinks and challenges for mobile application developers is developing, accessing, and managing all of the disparate data sources that are involved in delivering delightful, collaborative, and real-time mobile experiences for users while also enabling offline capabilities for when a user is not connected, but still wants to use the app. In this session, you be introduced to the new AWS AppSync service that speed and simplifies these tasks for developers using GraphQL to provide a data abstraction layer and easy query and update statements without having to know the details of the underlying data sources.
Connecting the Dots: Kong for GraphQL EndpointsJulien Bataillé
GraphQL is a popular alternative to REST for front-end applications as it offers flexibility and developer-friendly tooling. In this talk, we will look into the differences between REST and GraphQL, how GraphQL API Management presents a new set of challenges, and finally, how we can address those challenges by leveraging Kong extensibility.
The document discusses MongoDB transactions and concurrency. It provides code examples of how to perform transactions in MongoDB using logical sessions, including inserting a document into a collection and updating related documents in another collection atomically. It also discusses some of the features and timeline for implementing distributed transactions in sharded MongoDB clusters.
GraphQL - when REST API is not enough - lessons learnedMarcinStachniuk
This document discusses lessons learned from implementing GraphQL APIs. It begins by describing some limitations of REST APIs, such as requiring multiple roundtrips to fetch nested data. GraphQL is introduced as an alternative that allows clients to specify exactly what data they need in a single request. The document then covers various GraphQL concepts like queries, mutations, and type systems. It also discusses best practices like using a schema-first approach, pagination support, and the DataLoader library to solve the N+1 problem. Testing GraphQL APIs and integrating with Relay are also briefly outlined. The overall message is that GraphQL is a good alternative to REST when clients have complex data needs.
This document outlines the components of a typical serverless architecture including:
- A frontend hosted in an S3 bucket
- Authentication with Amazon Cognito and Google Identity Provider
- Data stored in DynamoDB accessed via Lambda functions
- Microservices built with Lambda functions triggered by API Gateway
- Infrastructure deployed via Terraform
MongoDB.local Sydney: Evolving your Data Access with MongoDB StitchMongoDB
You have valuable data in MongoDB and while it's important to use that data to empower your users and customers it can be tough to do so in a safe, secure way. In this session, you'll learn how to simply connect your users with the data they need using MongoDB Stitch. We'll cover how to quickly set-up complex access controls using Stitch's Read and Write Rules as well as how to expose that data through Stitch's SDKs, Functions, and Services.
One of the biggest time sinks and challenges for mobile application developers is developing, accessing, and managing all of the disparate data sources that are involved in delivering delightful, collaborative, and real-time mobile experiences for users while also enabling offline capabilities for when a user is not connected, but still wants to use the app. In this session, you be introduced to the new AWS AppSync service that speed and simplifies these tasks for developers using GraphQL to provide a data abstraction layer and easy query and update statements without having to know the details of the underlying data sources.
Azure for SharePoint Developers - Workshop - Part 2: Azure FunctionsBob German
This document discusses Azure Functions and common use cases for using Azure Functions with SharePoint Online. It provides an overview of Azure Functions including common triggers, bindings, and runtimes. It then describes examples of using Functions with SharePoint Online for tasks like elevating permissions, running timer jobs, hiding API keys, extending site designs and scripts, and custom code in Microsoft Flow. Additional details and code samples are provided for Functions integration with SharePoint Online.
This document discusses serverless architectures and provides examples of building serverless applications. It introduces serverless computing and explains why developers want to adopt serverless and DevOps approaches. Examples are given for static and dynamic websites built with serverless technologies like AWS Lambda, API Gateway, and S3 storage. The document also provides a case study of building a serverless application with services like Cognito, DynamoDB, and Lambda. Alternatives to AWS serverless options are mentioned along with takeaways about paying only for resources used.
Build Data Driven Apps with Real-time and Offline CapabilitiesAmazon Web Services
This document discusses AWS AppSync, a managed service for building real-time and offline data apps using GraphQL. It addresses challenges like varying device data needs, offline usage, and building scalable data apps. AppSync provides real-time data collaboration, an offline programming model with automatic syncing, and security features. Example use cases include dashboards, games, and chat apps. GraphQL is introduced as an alternative to traditional REST APIs that allows clients to request specific data fields. Pricing examples are provided to illustrate AppSync's cost structure.
Jeff Scudder, Eric Bidelman
The number of APIs made available for Google products has exploded from a handful to a slew! Get
the big picture on what is possible with the APIs for everything from YouTube, to Spreadsheets, to
Search, to Translate. We'll go over a few tools to help you get started and the things these APIs share
in common. After this session picking up new Google APIs will be a snap.
Simplify Access to Data from Pivotal GemFire Using the GraphQL (G2QL) ExtensionVMware Tanzu
This document discusses Geode GraphQL Extension (G2QL), which allows a Geode cache to be accessed via GraphQL queries and mutations. G2QL introspects a Geode cache configuration and generates a corresponding GraphQL schema. It converts GraphQL queries and mutations to Geode region operations and OQL, eliminating the need to write resolvers. G2QL addresses issues like schema duplication, boilerplate code, and inefficient data access through features like schema generation, automatic conversion of queries to Geode operations, and batching of queries. The document provides an example of G2QL in action and discusses next steps for the project.
Dev Jumpstart: Build Your First App with MongoDBMongoDB
This document provides an overview of MongoDB and how to build a simple blogging application with it. It describes MongoDB as a document database where documents are analogous to JSON objects and collections are analogous to tables in a relational database. It demonstrates how to install and run MongoDB, insert sample data for users and blog posts, query the data, and update documents. The document also lists various MongoDB drivers and provides pointers for next steps in learning about data modeling, replication, performance, and scaling in MongoDB.
MongoDB World 2019: Building a GraphQL API with MongoDB, Prisma, & TypeScriptMongoDB
Originally developed by Facebook, GraphQL is taking over the industry and replaces REST as an API standard. Learn how it works and build your own GraphQL API with Prisma, MongoDB & TypeScript. Prisma auto-generates a MonogDB client to connect your GraphQL resolvers with MongoDB in a type-safe way.
OSGi and Spring Data for simple (Web) Application Development - Christian Bar...mfrancis
This document discusses using OSGi and Spring Data to develop simple web applications. It describes using Bndtools for OSGi application development and the enRoute project for getting started with OSGi. It provides an overview of using JPA and Spring Data with OSGi for the persistence layer. It also covers integrating Handlebars templates, Jersey MVC, and AngularJS for the web layer. Testing strategies using Spock and integration tests are presented. The technologies discussed include OSGi, Equinox, Felix, JPA, Spring Data, Jersey, Handlebars, and AngularJS.
OSGi and Spring Data for simple (Web) Application DevelopmentChristian Baranowski
This document discusses using OSGi and Spring Data to develop simple web applications. It describes using Bndtools for OSGi application development and the enRoute project for getting started with OSGi. It provides an overview of using JPA and Spring Data with OSGi for the persistence layer. It also covers integrating Handlebars templates, Jersey MVC, and helpers for the web layer. Testing strategies using Spock are also summarized. Key technologies discussed include AngularJS, Jetty, OSGi, Spring Data JPA, and Spock.
UiPath Automation Developer Associate Training Series 2025 - Session 1DianaGray10
Welcome to UiPath Automation Developer Associate Training Series 2025 - Session 1.
In this session, we will cover the following topics:
Introduction to RPA & UiPath Studio
Overview of RPA and its applications
Introduction to UiPath Studio
Variables & Data Types
Control Flows
You are requested to finish the following self-paced training for this session:
Variables, Constants and Arguments in Studio 2 modules - 1h 30m - https://academy.uipath.com/courses/variables-constants-and-arguments-in-studio
Control Flow in Studio 2 modules - 2h 15m - https:/academy.uipath.com/courses/control-flow-in-studio
⁉️ For any questions you may have, please use the dedicated Forum thread. You can tag the hosts and mentors directly and they will reply as soon as possible.
Zilliz's presentation in AWS x Apache Doris meetup on 24th Feb 2025 in Singapore.
In this presentation, I shared a live demo on how you can outsource thinking and reasoning to Amazon Nova for generating a research report on any topic.
Code repo for live demo can be found here: https://github.com/zilliztech/deep-searcher
I've also shared about the hard tradeoffs you need to make when choosing vector indexes and lastly my top 5 favourite features of Zilliz Cloud.
Supercharge Your Career with UiPath CertificationsDianaGray10
Join us on February 25th as we discuss how you can supercharge your career with the updated 2025 UiPath Certifications.
Diana Gray, UiPath Senior Community Marketing Manager, Americas, will walk us through:
-- Workforce Trends
-- Value of UiPath Certifications
-- Certification Program
-- Steps to Earning a Certificate
Caching for Performance Masterclass: Caching StrategiesScyllaDB
Exploring the tradeoffs of common caching strategies – and a look at the architectural differences.
- Which strategies exist
- When to apply different strategies
- ScyllaDB cache design
Caching for Performance Masterclass: The In-Memory DatastoreScyllaDB
Understanding where in-memory data stores help most and where teams get into trouble.
- Where in the stack to cache
- Memcached as a tool
- Modern cache primitives
Kickstart Your QA: An Introduction to Automated Regression Testing ToolsShubham Joshi
For teams eager to elevate their quality assurance practices, this guide offers an introductory look at automated regression testing tools. In the realm of software development, regression tests are vital to ensuring that recent code changes don’t inadvertently break existing functionality. This article demystifies how automated regression testing tools function and why they’re indispensable for maintaining software quality. It covers the basics of setting up automated tests, integrating these tests into your CI/CD pipelines, and choosing the right tool to match your project’s needs. With clear explanations and actionable insights, you’ll discover how these tools help reduce manual testing burdens and catch bugs early. The guide also highlights common challenges beginners face and offers practical solutions to overcome them. Whether you’re a QA professional or a developer stepping into the world of test automation, this resource will help you kickstart your journey toward more reliable, efficient, and scalable testing practices.
DealBook of Ukraine: 2025 edition | AVentures CapitalYevgen Sysoyev
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2024 and the first deals of 2025.
This is session #3 of the 5-session online study series with Google Cloud, where we take you onto the journey learning generative AI. You’ll explore the dynamic landscape of Generative AI, gaining both theoretical insights and practical know-how of Google Cloud GenAI tools such as Gemini, Vertex AI, AI agents and Imagen 3.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG (coming 2025)
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Big Data Analytics Quick Research Guide by Arthur Morgan (PREVIEW)Arthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG (coming 2025)
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
This is a comprehensive guide explaining how blockchain technology works, its key features, and real-world applications in industries like finance, supply chain, and retail. Learn about different blockchain networks (public, private, and consortium) and the challenges businesses face in adopting blockchain. Discover how blockchain consulting can help businesses implement secure, transparent, and efficient solutions, reducing risks and optimizing operations. This guide is ideal for businesses exploring blockchain adoption and seeking expert guidance.
Revolutionizing Field Service: How LLMs Are Powering Smarter Knowledge Access...Earley Information Science
Revolutionizing Field Service with LLM-Powered Knowledge Management
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In this webinar, Seth Earley and industry experts Sanjay Mehta, and Heather Eisenbraun explore how LLMs and Retrieval-Augmented Generation (RAG) are transforming field service operations. Discover how AI-powered knowledge management is improving efficiency, reducing downtime, and elevating service quality.
LLMs for Instant Knowledge Retrieval – How AI-driven search dramatically cuts troubleshooting time.
Structured Data & AI – Why high-quality, organized knowledge is essential for LLM success.
Real-World Implementation – Lessons from deploying LLM-powered knowledge tools in field service.
Business Impact – How AI reduces service delays, optimizes workflows, and enhances technician productivity.
Empower your field service teams with AI-driven knowledge access. Watch the webinar to see how LLMs are revolutionizing service efficiency.
Agentic AI: The 2025 Next-Gen Automation GuideThoughtminds
Introduction to Agentic AI: Explains how it differs from traditional automation and its ability to make independent decisions.
Comparison with Generative AI: A structured comparison between Generative AI (content creation) and Agentic AI (autonomous action-taking).
Technical Breakdown: Covers core components such as LLMs, reinforcement learning, and cloud infrastructure that power Agentic AI.
Real-World Use Cases (2025 & Beyond): Examines how Agentic AI is transforming industries like insurance, healthcare, retail, finance, and cybersecurity.
Business Impact & ROI: Discusses case studies from Unilever, FedEx, and more, showcasing cost savings and operational efficiency improvements.
Challenges & Risks: Highlights bias, security threats, regulatory compliance, and workforce reskilling as critical challenges in AI adoption.
5-Step Implementation Strategy: A practical roadmap to help organizations integrate Agentic AI seamlessly.
Future Predictions (2025-2030): Forecasts on AI-driven workforce evolution, industry disruptions, and the rise of Quantum AI.
Mastering ChatGPT & LLMs for Practical Applications: Tips, Tricks, and Use CasesSanjay Willie
Our latest session with Astiostech covered how to unlock the full potential of ChatGPT and LLMs for real-world use!
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🔹 Advanced ChatGPT Features: Managing system prompts, conversation memory, and file uploads.
🔹 Optimizing AI Outputs: Refining responses, handling large texts, and knowing when fine-tuning is needed.
🔹 Competitive Insights: Exploring how ChatGPT compares with other AI tools.
🔹 Business & Content Use Cases: From summarization to SEO, sales, and audience targeting.
💡 The session provided hands-on strategies to make AI a powerful tool for content creation, decision-making, and business growth.
🚀 Are you using AI effectively in your workflow? Let’s discuss how it can improve efficiency and creativity!
#AI #ChatGPT #PromptEngineering #ArtificialIntelligence #LLM #Productivity #Astiostech
William Maclyn Murphy McRae, a logistics expert with 9+ years of experience, is known for optimizing supply chain operations and consistently exceeding industry standards. His strategic approach, combined with hands-on execution, has streamlined distribution processes, reduced lead times, and consistently delivered exceptional results.
Data-Driven Public Safety: Reliable Data When Every Second CountsSafe Software
When every second counts, you need access to data you can trust. In this webinar, we’ll explore how FME empowers public safety services to streamline their operations and safeguard communities. This session will showcase workflow examples that public safety teams leverage every day.
We’ll cover real-world use cases and demo workflows, including:
Automating Police Traffic Stop Compliance: Learn how the City of Fremont meets traffic stop data standards by automating QA/QC processes, generating error reports – saving over 2,800 hours annually on manual tasks.
Anonymizing Crime Data: Discover how cities protect citizen privacy while enabling transparent and trustworthy open data sharing.
Next Gen 9-1-1 Integration: Explore how Santa Clara County supports the transition to digital emergency response systems for faster, more accurate dispatching, including automated schema mapping for address standardization.
Extreme Heat Alerts: See how FME supports disaster risk management by automating the delivery of extreme heat alerts for proactive emergency response.
Our goal is to provide practical workflows and actionable steps you can implement right away. Plus, we’ll provide quick steps to find more information about our public safety subscription for Police, Fire Departments, EMS, HAZMAT teams, and more.
Whether you’re in a call center, on the ground, or managing operations, this webinar is crafted to help you leverage data to make informed, timely decisions that matter most.
Transcript: AI in publishing: Your questions answered - Tech Forum 2025BookNet Canada
George Walkley, a publishing veteran and leading authority on AI applications, joins us for a follow-up to his presentation "Applying AI to publishing: A balanced and ethical approach". George gives a brief overview of developments since that presentation and answers attendees' pressing questions about AI’s impact and potential applications in the book industry.
Link to recording and presentation slides: https://bnctechforum.ca/sessions/ai-in-publishing-your-questions-answered/
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Drew Madelung is a Cloud Solutions Architect and a Microsoft MVP for Office Apps and Services. He helps organizations realize what is possible with Microsoft 365 & Azure, onboard them in a secure and compliant way, and drive sustained adoption for those solutions. He is experienced in a range of technologies but specializes in the collaboration and teamwork workspaces such as Microsoft Teams, SharePoint, and OneDrive. He has helped deploy Microsoft 365 to multiple global companies while rolling out modern information protection and information governance technologies. He has been doing Microsoft consulting for 10+ years with a strength in security & compliance solutions.
2. what is this API for?
•
•
•
•
Allow maintenance and query Google Apps
Script ScriptDB directly from VBA
Share data in real time between Google
Docs and Office
a free noSQL database for Excel
Migration or coexistence path for GAS and
VBA
4. authentication with oAuth2
One off
credential
storage
user/google
scope
Registry
● Get oAuth2 credentials from Google Cloud Console
● Run the below to encrypt them on your PC, one time
only.
getGoogled("drive", , "xxxxx.apps.googleusercontent.com", "xxxxx").tearDown
● Credentials are shared between all VBA APIs
needing oAuth2 on Excel Liberation
● Thereafter, oauth2 dance is automatically handled in
all API requests
5. optional access control
User and
Access
type
credentials
your entry/
your scope
Registry
● You can also optionally add a couple more keys,
which are passed with every request in its header
● Equivalent to the RESTAPIkey and ApplicationID in
parse.com
● Your Gas Webapp handler can inspect these to
signal the GAS API the type of access allowed
● Only needs to be stored one time
scriptdbCom.init("anything",”some entry id” ,”some scope id”,
"your app id", _
"your restapikey", , , True, _
"Gas web app url endpoint").tearDown
6. comparison with parse.com API
VBA and GAS Main Apps are virtually the same code irrespective of the
database
cParseCom
VBA API
cParseCom
GAS API
cScriptDBCom
VBA API
parse.com restAPI handler
GAS scriptDB
webApp and API
library
parse.com cloud based noSQL
database
GAS scriptDB
cloud based noSQL
database
7. optimization and batching
•
•
•
The gas library api will batch all requests it can.
You should also batch requests from VBA API. It will
automatically handle batching limits.
It’s easy. Just enclose various operations with .batch()
with getScriptDb("someSilo")
.batch(True)
.someoperations(...)
.someotheroperations(...)
.batch(false)
better
when
batched
8. example - create an object
Data is stored in silos within one or more ScriptDb
code
getScriptDb("somesilo") _
.createObject(JSONParse("{'customerid':1}"))
response (.jObject.stringify)
{
"status":"good",
"results":[]
}
better
when
batched
9. example - do a query
Queries are by example, and can include limit/skip
code
getScriptDb("somesilo") _
.getObjectsByQuery(JSONParse("{'customerid':1}"), JSONParse("{'limit':10}"))
response (.jObject.stringify)
{"status":"good","count":1,"results":[ {
"siloId":"somesilo",
"customerid":1,
"objectId":"S320799307189"
}]
}
10. example - update objects
All matching objects are updated to the given value. New
fields are created, existing fields are replaced
code
getScriptDb("somesilo") _
.updateObjects(JSONParse("{'customerid':1}"), JSONParse("{'name':'john'}"))
response (.jObject.stringify)
{ "status":"good","results":[]}
better
when
batched
11. example - count matching objects
Count operations can have optional queries
code
getScriptDb("somesilo") _
.count(JSONParse("{'customerid':1}")))
response
1
12. example - get object by id
Each object is assigned a unique Id returned by queries
code
getScriptDb("someSilo")
.getObjectById ("S320802188977")
response (.jObject.stringify)
{"status":"good","count":1,"results":[ {
"siloId":"somesilo",
"customerid":1,
"name":"john"
}]
}
13. example - delete objects
All objects matching the query are deleted
code
getScriptDb("somesilo").deleteObjects(JSONParse("{'customerid':1}"))
response (.jObject.stringify)
{"status":"good","count":0,"results":[ ]}
better
when
batched
14. example - load a spreadsheet to
scriptDb
Example add-on for this is included in workbook
code
populateFromSheet "VBAParseData"
Reading sheet and creating scriptDB objects
With dset.populateData(wholeSheet(sheetName), , , , , , True).jObject(, True, True)
For Each job In .children
Debug.Assert scriptdbCom.createObject(job).isOk
Next job
.tearDown
End With
better
when
batched
15. limits and skipping
Queries are subject to limits, so you need to work multiple
passes for big queries using skip/limit
Do
With sdb.getObjectsByQuery(Nothing, jobskip).jObject.child("results")
If .children.count = 0 Or Not sdb.isOk Then Exit Do
jobskip.child("skip").value = jobskip.child("skip").value + .children.count
For Each job In .children
jobStore.add , job.toString("objectId")
Next job
End With
Loop
{"results":["S320923864193","S320923308803", …. ]}
this is
automatically
handled for update
and delete
16. dates and times
These are handled the same was as
parse.com
"date":{
"__type":"Date",
"iso":"2013-08-22T00:00:00.000Z"
}
with supplied conversion function used like this
Debug.Print "date converted", getAnIsoDate(jor.child("date"))
17. silos and parse classes
•
•
•
•
A Silo is like a parse Class. For 2D data it
can be considered like a table.
Multiple classes can be held in the same
scriptDB.
scriptDB siloing is handled automatically
A seperate cScriptDbCom instance should
be instatiated for each class being worked
18. multiple scriptDB
•
•
•
The scriptDB dispatcher handled which actual scriptDB
to write to.
Multiple DBs can be used by referencing multiple
libraries in the scriptDB dispatcher.
Library names will be signalled to the dispatcher if they
are in the setup for this entry
scriptdbCom.init("anything", "some entry name", , "your app id", "your restapikey", , _
"some library",, "your web app Url")
19. restricting operations
You may want to allow a different kind of access to certain
users.
provide a different URL or use the app keys to signal
some restricted access , and include something like this
in the doGet() and doPost() functions
•
•
var whatsAllowed = ['query','count','getbyid'];
setting that configuration up under a different entry
scriptdbCom.init("anything", "some restricted entry", , "your app id", "your restapikey", , _
, "your restricted web app Url")
20. performance versus parse.com
•
•
Performance of parse.com as a rest API
handler seems to be much better than
scriptDB for most operations.
A full analysis will be published on Excel
Liberation and associated blog at some
future date
21. accessing from other apps
•
•
•
The scriptDB GAS handler API can be
accessed using any rest query mechanism.
The VBA API just manages the conversation
with the scriptDB GAS handler
Examples of other languages will be on
Excel Liberation at a later date.
22. public facing scriptdb
•
•
•
Normally access is controlled by oAuth2 - especially if
you are allowing write access.
oAuth2 is very much invisible in this API, as described
in this writeup, but you still need to get google
credentials
You can dispense with oAuth2 altogether for public
facing scriptdb by signalling false in the entry setup
scriptdbCom.init("anything", "readonly", , "your app id", "your restapikey", , "some library", False, _
"https://script.google.com/macros/s/AKfycbx7_gPpc38Map4QqHOQrzx_kvIX00nfYGO9OLq8_cMD486Va6M/exec")
23. further detail
All this code is downloadable or copyable from
Google Apps Script Libraries.
For more information see Excel Liberation