What is Microsoft PowerApps and what are its top alternatives?
Top Alternatives to Microsoft PowerApps
- Google App Maker
App Maker lets you develop powerful apps with relative ease. Create a model to manage your data, build a UI in the visual editor, use Apps Script to write some scripts, and you're on your way. ...
- Flow
Flow is an online collaboration platform that makes it easy for people to create, organize, discuss, and accomplish tasks with anyone, anytime, anywhere. By merging a sleek, intuitive interface with powerful functionality, we're out to revolutionize the way the world's productive teams get things done. ...
- Microsoft Access
It is an easy-to-use tool for creating business applications, from templates or from scratch. With its rich and intuitive design tools, it can help you create appealing and highly functional applications in a minimal amount of time. ...
- OutSystems
OutSystems is a low-code platform to visually develop your application, integrate with existing systems and add your own code when needed. ...
- Power BI
It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...
- FileMaker
It is a Platform to create innovative custom apps for your workplace.
- Mendix
It is a low-code software platform. It provides tools to build, test, deploy and iterate applications. ...
- NGINX
nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018. ...
Microsoft PowerApps alternatives & related posts
related Google App Maker posts
- Great for collaboration6
- Easy to use6
- Free3
related Flow posts
related Microsoft Access posts
- Price1
- Maturidade0
- Perfomamnce0
related OutSystems posts
- Cross-filtering18
- Database visualisation2
- Powerful Calculation Engine2
- Access from anywhere2
- Intuitive and complete internal ETL2
- Azure Based Service1
related Power BI posts
Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.
Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?
I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.
- Rapid development2
- REST API2
- API1
- Permissions1
- All included1
- Easy to learn1
- Expensive1
related FileMaker posts
related Mendix posts
NGINX
- High-performance http server1.4K
- Performance894
- Easy to configure730
- Open source607
- Load balancer530
- Free289
- Scalability288
- Web server226
- Simplicity175
- Easy setup136
- Content caching30
- Web Accelerator21
- Capability15
- Fast14
- High-latency12
- Predictability12
- Reverse Proxy8
- Supports http/27
- The best of them7
- Great Community5
- Lots of Modules5
- Enterprise version5
- High perfomance proxy server4
- Embedded Lua scripting3
- Streaming media delivery3
- Streaming media3
- Reversy Proxy3
- Blash2
- GRPC-Web2
- Lightweight2
- Fast and easy to set up2
- Slim2
- saltstack2
- Virtual hosting1
- Narrow focus. Easy to configure. Fast1
- Along with Redis Cache its the Most superior1
- Ingress controller1
- Advanced features require subscription10
related NGINX posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
We chose AWS because, at the time, it was really the only cloud provider to choose from.
We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.
We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).
While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.
#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy