What is Mapbox and what are its top alternatives?
Mapbox is a popular platform for custom online maps that offers flexible APIs, SDKs, and data visualization tools. Its key features include customizable map styles, geocoding and routing services, and real-time location data. However, Mapbox can be expensive for large-scale use and may have limitations in terms of certain advanced mapping functionalities. 1. Leaflet: Leaflet is a lightweight JavaScript library for interactive maps that works well with both desktop and mobile platforms. Key features include easy integration, custom map designs, and support for various plugins. Pros: Simple to use, customizable, open-source. Cons: Limited built-in functionality compared to Mapbox. 2. OpenLayers: OpenLayers is a high-performance library for creating interactive maps on the web. It supports a wide range of mapping sources and formats, with features like layer switching, styling, and editing capabilities. Pros: Great for complex mapping needs, open-source, powerful. Cons: Steeper learning curve, can be challenging for beginners. 3. Google Maps Platform: Google Maps offers a suite of APIs for mapping, geocoding, routing, and location-based services. Its features include detailed map data, Street View imagery, and integration with other Google services. Pros: Extensive documentation, smooth integration with Google services. Cons: Limited free usage, dependency on Google ecosystem. 4. HERE Technologies: HERE Technologies provides a variety of mapping and location-based services for businesses. Key features include accurate geocoding, routing, and fleet management tools. Pros: Precise location data, reliable routing services. Cons: Costly for large-scale use, complex pricing structure. 5. CARTO: CARTO is a cloud-based platform for mapping, analysis, and data visualization. It offers tools for creating interactive maps, spatial analysis, and sharing geospatial insights. Pros: User-friendly interface, powerful geospatial analysis capabilities. Cons: Pricing can be a barrier for small businesses. 6. MapTiler: MapTiler is a mapping solution that focuses on creating custom maps from various data sources. It supports vector and raster tiles, WebGL rendering, and advanced styling options. Pros: Fast map rendering, versatile styling capabilities. Cons: May require technical expertise for customization. 7. Thunderforest: Thunderforest provides a range of map styles and APIs for creating custom maps. It offers detailed map designs, geocoding services, and tile hosting. Pros: Beautiful map designs, flexible pricing options. Cons: Limited free tier, fewer features compared to Mapbox. 8. TomTom Maps API: TomTom offers mapping and navigation services for businesses, with features like routing, traffic information, and location insights. Pros: Accurate map data, reliable navigation services. Cons: Pricing can be high for extensive usage, limited customization options. 9. esri ArcGIS: esri ArcGIS is a comprehensive mapping platform with tools for spatial analysis, data visualization, and collaboration. It is widely used in the GIS industry for its advanced mapping capabilities. Pros: Robust geospatial analysis tools, extensive data support. Cons: Complex interface, costly for large organizations. 10. Stamen Maps: Stamen Maps offers a collection of unique map styles for different use cases, such as terrain, watercolor, and toner maps. It provides custom map designs and a creative approach to mapping visuals. Pros: Creative map styles, open-source design. Cons: Limited customization options, may not suit all business needs.
Top Alternatives to Mapbox
- Google Maps
Create rich applications and stunning visualisations of your data, leveraging the comprehensiveness, accuracy, and usability of Google Maps and a modern web platform that scales as you grow. ...
- OpenStreetMap
OpenStreetMap is built by a community of mappers that contribute and maintain data about roads, trails, cafés, railway stations, and much more, all over the world. ...
- CARTO
The CARTO platform empowers everyone, from business analysts to data scientists, to turn location data into business outcomes. We accelerate innovation, power new use cases and disrupt business models through Location Intelligence. ...
- Leaflet
Leaflet is an open source JavaScript library for mobile-friendly interactive maps. It is developed by Vladimir Agafonkin of MapBox with a team of dedicated contributors. Weighing just about 30 KB of gzipped JS code, it has all the features most developers ever need for online maps. ...
- ArcGIS
It is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and much more. ...
- OpenLayers
An opensource javascript library to load, display and render maps from multiple sources on web pages. ...
- JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...
- Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...
Mapbox alternatives & related posts
Google Maps
- Free253
- Address input through maps api136
- Sharable Directions81
- Google Earth47
- Unique46
- Custom maps designing3
- Google Attributions and logo4
- Only map allowed alongside google place autocomplete1
related Google Maps posts
For Etom, a side project. We wanted to test an idea for a future and bigger project.
What Etom does is searching places. Right now, it leverages the Google Maps API. For that, we found a React component that makes this integration easy because using Google Maps API is not possible via normal API requests.
You kind of need a map to work as a proxy between the software and Google Maps API.
We hate configuration(coming from Rails world) so also decided to use Create React App because setting up a React app, with all the toys, it's a hard job.
Thanks to all the people behind Create React App it's easier to start any React application.
We also chose a module called Reactstrap which is Bootstrap UI in React components.
An important thing in this side project(and in the bigger project plan) is to measure visitor through out the app. For that we researched and found that Keen was a good choice(very good free tier limits) and also it is very simple to setup and real simple to send data to
Slack and Trello are our defaults tools to comunicate ideas and discuss topics, so, no brainer using them as well for this project.
Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.
- Simple22
- Free17
- Open-Source9
- Open-Data7
- React/ RNative integration1
related OpenStreetMap posts
We need some advice about the map services provider. We are a mobility app that just launched 5 months ago in Tunisia offering P2P carpooling. We are currently using Google Maps API for maps (Places API, Geocoding API, Directions API & Distance Matrix API). Thus, we received expensive bills from Google Cloud following the number of requests we are using. We are looking forward to reduce the number of requests in general because we can't afford these large bills at this stage, knowing that they are going to increase proportionally to the active users of the app. We tried to optimize multiple times but it isn't enough. We are searching for optimization advice or ideas on how we use the APIs, or other map providers (like OpenStreetMap or similar) that offers free or cheaper options than Google Maps, without lacking quality of information (we are in Tunisia and we have to choose options that have enough data about Tunisia). Thanks!
Which will give a better map (better view, markers options, info window) in an Android OS app?
Leaflet with Mapbox or Leaflet with OpenStreetMap?
CARTO
- Crisp UI1
- Great customer service1
- Comprehensive platform1
related CARTO posts
- Light weight32
- Free28
- Evolutive via plugins12
- OpenStreetMap10
- Strong community9
- Choice of map providers7
- Easy API6
- Alternative to Google Maps3
related Leaflet posts
Which will give a better map (better view, markers options, info window) in an Android OS app?
Leaflet with Mapbox or Leaflet with OpenStreetMap?
- Reponsive7
- A lot of widgets4
- Data driven vizualisation4
- Easy tà learn2
- 3D2
- Easy API1
related ArcGIS posts
Google Maps lets "property owners and their authorized representatives" upload indoor maps, but this appears to lack navigation ("wayfinding").
MappedIn is a platform and has SDKs for building indoor mapping experiences (https://www.mappedin.com/) and ESRI ArcGIS also offers some indoor mapping tools (https://www.esri.com/en-us/arcgis/indoor-gis/overview). Finally, there used to be a company called LocusLabs that is now a part of Atrius and they were often integrated into airlines' apps to provide airport maps with wayfinding (https://atrius.com/solutions/personal-experiences/personal-wayfinder/).
I previously worked at Mapbox and while I believe that it's a great platform for building map-based experiences, they don't have any simple solutions for indoor wayfinding. If I were doing this for fun as a side-project and prioritized saving money over saving time, here is what I would do:
Create a graph-based dataset representing the walking paths around your university, where nodes/vertexes represent the intersections of paths, and edges represent paths (literally paths outside, hallways, short path segments that represent entering rooms). You could store this in a hosted graph-based database like Neo4j, Amazon Neptune , or Azure Cosmos DB (with its Gremlin API) and use built-in "shortest path" queries, or deploy a PostgreSQL service with pgRouting.
Add two properties to each edge: one property for the distance between its nodes (libraries like @turf/helpers will have a distance function if you have the latitude & longitude of each node), and another property estimating the walking time (based on the distance). Once you have these values saved in a graph-based format, you should be able to easily query and find the data representation of paths between two points.
At this point, you'd have the routing problem solved and it would come down to building a UI. Mapbox arguably leads the industry in developer tools for custom map experiences. You could convert your nodes/edges to GeoJSON, then either upload to Mapbox and create a Tileset to visualize the paths, or add the GeoJSON to the map on the fly.
*You might be able to use open source routing tools like OSRM (https://github.com/Project-OSRM/osrm-backend/issues/6257) or Graphhopper (instead of a custom graph database implementation), but it would likely be more involved to maintain these services.
- Flexibility15
- Maturity11
- Open Source8
- Incredibly comprehensive, excellent support7
- Extensible4
- Strong community4
- Choice of map providers4
- Low Level API3
- OpenStreetMap1
related OpenLayers posts
JavaScript
- Can be used on frontend/backend1.7K
- It's everywhere1.5K
- Lots of great frameworks1.2K
- Fast896
- Light weight745
- Flexible425
- You can't get a device today that doesn't run js392
- Non-blocking i/o286
- Ubiquitousness236
- Expressive191
- Extended functionality to web pages55
- Relatively easy language49
- Executed on the client side46
- Relatively fast to the end user30
- Pure Javascript25
- Functional programming21
- Async15
- Full-stack13
- Setup is easy12
- Its everywhere12
- JavaScript is the New PHP11
- Because I love functions11
- Like it or not, JS is part of the web standard10
- Can be used in backend, frontend and DB9
- Expansive community9
- Future Language of The Web9
- Easy9
- No need to use PHP8
- For the good parts8
- Can be used both as frontend and backend as well8
- Everyone use it8
- Most Popular Language in the World8
- Easy to hire developers8
- Love-hate relationship7
- Powerful7
- Photoshop has 3 JS runtimes built in7
- Evolution of C7
- Popularized Class-Less Architecture & Lambdas7
- Agile, packages simple to use7
- Supports lambdas and closures7
- 1.6K Can be used on frontend/backend6
- It's fun6
- Hard not to use6
- Nice6
- Client side JS uses the visitors CPU to save Server Res6
- Versitile6
- It let's me use Babel & Typescript6
- Easy to make something6
- Its fun and fast6
- Can be used on frontend/backend/Mobile/create PRO Ui6
- Function expressions are useful for callbacks5
- What to add5
- Client processing5
- Everywhere5
- Scope manipulation5
- Stockholm Syndrome5
- Promise relationship5
- Clojurescript5
- Because it is so simple and lightweight4
- Only Programming language on browser4
- Hard to learn1
- Test1
- Test21
- Easy to understand1
- Not the best1
- Easy to learn1
- Subskill #41
- Hard 彤0
- A constant moving target, too much churn22
- Horribly inconsistent20
- Javascript is the New PHP15
- No ability to monitor memory utilitization9
- Shows Zero output in case of ANY error8
- Thinks strange results are better than errors7
- Can be ugly6
- No GitHub3
- Slow2
related JavaScript posts
Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.
But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.
But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.
Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
- Distributed version control system1.4K
- Efficient branching and merging1.1K
- Fast959
- Open source845
- Better than svn726
- Great command-line application368
- Simple306
- Free291
- Easy to use232
- Does not require server222
- Distributed27
- Small & Fast22
- Feature based workflow18
- Staging Area15
- Most wide-spread VSC13
- Role-based codelines11
- Disposable Experimentation11
- Frictionless Context Switching7
- Data Assurance6
- Efficient5
- Just awesome4
- Github integration3
- Easy branching and merging3
- Compatible2
- Flexible2
- Possible to lose history and commits2
- Rebase supported natively; reflog; access to plumbing1
- Light1
- Team Integration1
- Fast, scalable, distributed revision control system1
- Easy1
- Flexible, easy, Safe, and fast1
- CLI is great, but the GUI tools are awesome1
- It's what you do1
- Phinx0
- Hard to learn16
- Inconsistent command line interface11
- Easy to lose uncommitted work9
- Worst documentation ever possibly made7
- Awful merge handling5
- Unexistent preventive security flows3
- Rebase hell3
- When --force is disabled, cannot rebase2
- Ironically even die-hard supporters screw up badly2
- Doesn't scale for big data1
related Git 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.
Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).
It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up
or vagrant reload
we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.
I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up
, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.
We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.
If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.
The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).
Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.