Alternatives to Zoho Catalyst logo

Alternatives to Zoho Catalyst

AWS Lambda, Serverless, Cloud Functions for Firebase, Azure Functions, and Google Cloud Functions are the most popular alternatives and competitors to Zoho Catalyst.
0
2
+ 1
0

What is Zoho Catalyst and what are its top alternatives?

It is a serverless development platform that helps developers build applications, APIs, microservices, and more with the help of serverless functions, databases, filestores, and other useful components and APIs.
Zoho Catalyst is a tool in the Serverless / Task Processing category of a tech stack.

Top Alternatives to Zoho Catalyst

Zoho Catalyst alternatives & related posts

AWS Lambda logo

AWS Lambda

10.5K
7.4K
404
Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or...
10.5K
7.4K
+ 1
404

related AWS Lambda posts

Jeyabalaji Subramanian
Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly · | 20 upvotes · 1.4M views

Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

Enough biz talk, onto tech. The challenges were:

  • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
  • Update API and back end services to handle and enforce plan limits.
  • Update the UI to kindly state plan limits are in effect on some part of the UI.
  • Update the pricing page to reflect all changes.
  • Keep the actual processing backend, storage and API's as untouched as possible.

In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

  1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
  2. The Vue.js frontend reads these from the vuex store on login.
  3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
  4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

Hope this helps anyone building out their SaaS and is in a similar situation.

See more
Serverless logo

Serverless

707
720
20
The most widely-adopted toolkit for building serverless applications
707
720
+ 1
20

related Serverless posts

Nitzan Shapira
Nitzan Shapira

At Epsagon, we use hundreds of AWS Lambda functions, most of them are written in Python, and the Serverless Framework to pack and deploy them. One of the issues we've encountered is the difficulty to package external libraries into the Lambda environment using the Serverless Framework. This limitation is probably by design since the external code your Lambda needs can be usually included with a package manager.

In order to overcome this issue, we've developed a tool, which we also published as open-source (see link below), which automatically packs these libraries using a simple npm package and a YAML configuration file. Support for Node.js, Go, and Java will be available soon.

The GitHub respoitory: https://github.com/epsagon/serverless-package-external

See more
Praveen Mooli
Praveen Mooli
Engineering Manager at Taylor and Francis · | 13 upvotes · 1.2M views

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless

See more
Cloud Functions for Firebase logo

Cloud Functions for Firebase

333
275
3
Run your mobile backend code without managing servers
333
275
+ 1
3
PROS OF CLOUD FUNCTIONS FOR FIREBASE
CONS OF CLOUD FUNCTIONS FOR FIREBASE
    No cons available

    related Cloud Functions for Firebase posts

    Eugene Cheah
    Eugene Cheah

    For inboxkitten.com, an opensource disposable email service;

    We migrated our serverless workload from Cloud Functions for Firebase to CloudFlare workers, taking advantage of the lower cost and faster-performing edge computing of Cloudflare network. Made possible due to our extremely low CPU and RAM overhead of our serverless functions.

    If I were to summarize the limitation of Cloudflare (as oppose to firebase/gcp functions), it would be ...

    1. <5ms CPU time limit
    2. Incompatible with express.js
    3. one script limitation per domain

    Limitations our workload is able to conform with (YMMV)

    For hosting of static files, we migrated from Firebase to CommonsHost

    More details on the trade-off in between both serverless providers is in the article

    See more
    Aliadoc Team
    Aliadoc Team

    In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

    For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

    For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

    We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

    Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

    See more

    related Azure Functions posts

    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 16 upvotes · 326.8K views

    CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

    CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

    AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

    It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

    The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

    In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

    Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

    See more
    Michal Nowak
    Michal Nowak

    In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

    We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

    PROS
    • No servers to manage (obviously!)
    • Limited fixed costs – you pay only for used time
    • Automated scaling and balancing
    • Automatic failover (or, at this level of abstraction, no failover problem at all)
    • Security easier to provide and audit
    • Low overhead at the start (with the certain level of knowledge)
    • Short time to market
    • Easy handover - deployment coupled with code
    • Perfect choice for lean startups with fast-paced iterations
    • Augmentation for the classic cloud, server(full) approach
    CONS
    • Not much know-how and best practices available about structuring the code and projects on the market
    • Not suitable for complex business logic due to the risk of producing highly coupled code
    • Cost difficult to estimate (helpful tools: serverlesscalc.com)
    • Difficulty in migration to other platforms (Vendor lock⚠️)
    • Little engineers with experience in serverless on the job market
    • Steep learning curve for engineers without any cloud experience

    More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

    See more
    Google Cloud Functions logo

    Google Cloud Functions

    291
    285
    14
    A serverless environment to build and connect cloud services
    291
    285
    + 1
    14

    related Google Cloud Functions posts

    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 16 upvotes · 326.8K views

    CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

    CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

    AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

    It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

    The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

    In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

    Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

    See more
    Tim Nolet
    Tim Nolet
    Founder, Engineer & Dishwasher at Checkly · | 6 upvotes · 94.5K views

    AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

    In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

    • We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
    • All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
    • The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
    • We run separate dev and test versions of each function in each region.

    Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

    • Why Lambda is an almost perfect match for SaaS. Especially when you're small.
    • Why I don't use a "big" framework around it.
    • Why distributed background jobs triggered by queues are Lambda's raison d'être.
    • Why monitoring & logging is still an issue.

    https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

    See more
    Apex logo

    Apex

    77
    73
    0
    Serverless Architecture with AWS Lambda
    77
    73
    + 1
    0
    PROS OF APEX
      No pros available
      CONS OF APEX
        No cons available

        related Apex posts

        Google Cloud Run logo

        Google Cloud Run

        72
        102
        52
        Run stateless HTTP containers on a fully managed environment or in your own GKE cluster
        72
        102
        + 1
        52

        related Google Cloud Run posts

        I use Google Cloud Run because it's like bring your own docker image to Google Cloud Functions.

        I use it for building Dash Apps

        It creates a nice url for web apps, and I see it being the evolution of serverless if GCP can scale this up.

        My Real-Time Python App Example

        See more

        What are the best options to host a Spring Boot application that acts as a receiver and publisher from Google Cloud Pub/Sub. I am using Google App Engine to do that, but there is Google Cloud Dataflow and Google Cloud Run that can be used. Which is the best option that can be used for this purpose and also that can handle the failover scenarios as well. Thanks!

        See more
        Zappa logo

        Zappa

        48
        58
        0
        Deploy all Python WSGI applications on AWS Lambda + API Gateway.
        48
        58
        + 1
        0
        PROS OF ZAPPA
          No pros available
          CONS OF ZAPPA
            No cons available

            related Zappa posts

            Jeyabalaji Subramanian
            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            Jeyabalaji Subramanian
            Jeyabalaji Subramanian

            At FundsCorner, we are on a mission to enable fast accessible credit to India’s Kirana Stores. We are an early stage startup with an ultra small Engineering team. All the tech decisions we have made until now are based on our core philosophy: "Build usable products fast".

            Based on the above fundamentals, we chose Python as our base language for all our APIs and micro-services. It is ultra easy to start with, yet provides great libraries even for the most complex of use cases. Our entire backend stack runs on Python and we cannot be more happy with it! If you are looking to deploy your API as server-less, Python provides one of the least cold start times.

            We build our APIs with Flask. For backend database, our natural choice was MongoDB. It frees up our time from complex database specifications - we instead use our time in doing sensible data modelling & once we finalize the data model, we integrate it into Flask using Swagger UI. Mongo supports complex queries to cull out difficult data through aggregation framework & we have even built an internal framework called "Poetry", for aggregation queries.

            Our web apps are built on Vue.js , Vuetify and vuex. Initially we debated a lot around choosing Vue.js or React , but finally settled with Vue.js, mainly because of the ease of use, fast development cycles & awesome set of libraries and utilities backing Vue.

            You simply cannot go wrong with Vue.js . Great documentation, the library is ultra compact & is blazing fast. Choosing Vue.js was one of the critical decisions made, which enabled us to launch our web app in under a month (which otherwise would have taken 3 months easily). For those folks who are looking for big names, Adobe, and Alibaba and Gitlab are using Vue.

            By choosing Vuetify, we saved thousands of person hours in designing the CSS files. Vuetify contains all key material components for designing a smooth User experience & it just works! It's an awesome framework. All of us at FundsCorner are now lifelong fanboys of Vue.js and Vuetify.

            On the infrastructure side, all our API services and backend services are deployed as server less micro-services through Zappa. Zappa makes your life super easy by packaging everything that is required to deploy your code as AWS Lambda. We are now addicted to the single - click deploys / updates through Zappa. Try it out & you will convert!

            Also, if you are using Zappa, you can greatly simplify your CI / CD pipelines. Do try it! It's just awesome! and... you will be astonished by the savings you have made on AWS bills at end of the month.

            Our CI / CD pipelines are built using GitLab CI. The documentation is very good & it enables you to go from from concept to production in minimal time frame.

            We use Sentry for all crash reporting and resolution. Pro tip, they do have handlers for AWS Lambda , which made our integration super easy.

            All our micro-services including APIs are event-driven. Our background micro-services are message oriented & we use Amazon SQS as our message pipe. We have our own in-house workflow manager to orchestrate across micro - services.

            We host our static websites on Netlify. One of the cool things about Netlify is the automated CI / CD on git push. You just do a git push to deploy! Again, it is super simple to use and it just works. We were dogmatic about going server less even on static web sites & you can go server less on Netlify in a few minutes. It's just a few clicks away.

            We use Google Compute Engine, especially Google Vision for our AI experiments.

            For Ops automation, we use Slack. Slack provides a super-rich API (through Slack App) through which you can weave magical automation on boring ops tasks.

            See more