Alternatives to Laravel Spark logo

Alternatives to Laravel Spark

Laravel Nova, Apache Spark, Wave, Django, and Checkout.com are the most popular alternatives and competitors to Laravel Spark.
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What is Laravel Spark and what are its top alternatives?

Spark is a Laravel package that provides scaffolding for all of the stuff you don't want to code. Subscription billing? We got that. Invoices? No problem.
Laravel Spark is a tool in the Payments Tools category of a tech stack.

Top Alternatives to Laravel Spark

  • Laravel Nova
    Laravel Nova

    It is a beautifully designed administration panel for Laravel. Carefully crafted by the creators of Laravel to make you the most productive developer. It provides a full CRUD interface for your Eloquent models. Every type of Eloquent relationship is fully supported. ...

  • Apache Spark
    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • Wave
    Wave

    It is a free, online software alternative to Quickbooks. Wave helps manage invoices, credit card payments, accounting & payroll. Best for small businesses & freelancers. ...

  • Django
    Django

    Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...

  • Checkout.com
    Checkout.com

    Adapt, innovate and thrive with more value from every transaction flowing through your business. You deserve more than plain old payments. Our super-connected technology makes them seamless and so much more. Get faster, more reliable transactions. Higher conversions. Unbeatable insight and flexibility. So you can delight your customers and unlock new revenue streams. ...

  • Midtrans
    Midtrans

    It has helped thousands of businesses in Indonesia with a secure and convenient online payment system. It is compatible with various APIs and plug-ins for an easy integration process. ...

  • Servicebot
    Servicebot

    Servicebot is the Stripe Billing UI toolkit to scale your subscription business. Provide your customers with beautiful Pricing Pages, Signup Forms, and Subscription Management Portals. ...

  • BankFeeds.io
    BankFeeds.io

    It is a connector between your Stripe and Xero accounts. It runs in the background, automatically importing your Stripe sales, refunds and fees into Xero. ...

Laravel Spark alternatives & related posts

Laravel Nova logo

Laravel Nova

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Beautifully-designed administration panel for Laravel
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PROS OF LARAVEL NOVA
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      related Laravel Nova posts

      Hello, I'm currently writing an e-commerce website with Laravel and Laravel Nova (as an admin panel). I want to start deploying the app and created a DigitalOcean account. After some searches about the deployment process, I saw that the setup via DigitalOcean (using Droplets) isn't very easy for beginners. Now I'm not sure how to deploy my app. I am in between Laravel Forge and DigitalOcean (?Apps Platform or Droplets?). I've read that Heroku and Laravel Vapor are a bit expensive. That's why I didn't consider them yet. I'd be happy to read your opinions on that topic!

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      Apache Spark logo

      Apache Spark

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      Fast and general engine for large-scale data processing
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      PROS OF APACHE SPARK
      • 59
        Open-source
      • 48
        Fast and Flexible
      • 8
        One platform for every big data problem
      • 7
        Great for distributed SQL like applications
      • 6
        Easy to install and to use
      • 3
        Works well for most Datascience usecases
      • 2
        Interactive Query
      • 2
        In memory Computation
      • 2
        Machine learning libratimery, Streaming in real
      CONS OF APACHE SPARK
      • 3
        Speed

      related Apache Spark posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.1M views

      Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

      Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

      https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

      (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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      Wave logo

      Wave

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      Free invoicing & accounting software with credit card processing & payroll services
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      PROS OF WAVE
      • 1
        1 free instance without CC forever?
      CONS OF WAVE
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        related Wave posts

        Django logo

        Django

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        The Web framework for perfectionists with deadlines
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        PROS OF DJANGO
        • 643
          Rapid development
        • 476
          Open source
        • 409
          Great community
        • 359
          Easy to learn
        • 268
          Mvc
        • 219
          Beautiful code
        • 212
          Elegant
        • 196
          Free
        • 195
          Great packages
        • 182
          Great libraries
        • 71
          Restful
        • 69
          Powerful
        • 68
          Comes with auth and crud admin panel
        • 65
          Great documentation
        • 62
          Great for web
        • 48
          Python
        • 38
          Great orm
        • 36
          Great for api
        • 27
          All included
        • 22
          Fast
        • 22
          Web Apps
        • 19
          Used by top startups
        • 18
          Clean
        • 17
          Easy setup
        • 16
          Sexy
        • 13
          Convention over configuration
        • 13
          ORM
        • 10
          Allows for very rapid development with great libraries
        • 10
          The Django community
        • 7
          Great MVC and templating engine
        • 7
          Its elegant and practical
        • 7
          King of backend world
        • 6
          Batteries included
        • 6
          Full stack
        • 6
          Mvt
        • 6
          Cross-Platform
        • 6
          Have not found anything that it can't do
        • 6
          Fast prototyping
        • 5
          Easy to develop end to end AI Models
        • 5
          Very quick to get something up and running
        • 5
          Easy Structure , useful inbuilt library
        • 4
          Modular
        • 4
          Many libraries
        • 4
          Great peformance
        • 4
          Easy to use
        • 4
          Python community
        • 4
          Easy
        • 3
          Just the right level of abstraction
        • 3
          Scaffold
        • 3
          Full-Text Search
        • 3
          Map
        • 3
          Zero code burden to change databases
        • 2
          Easy to change database manager
        • 1
          Node js
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          Rails
        • 0
          Fastapi
        • 0
          Aaaa
        • 0
          Asdasd
        CONS OF DJANGO
        • 25
          Underpowered templating
        • 21
          Autoreload restarts whole server
        • 20
          Underpowered ORM
        • 15
          URL dispatcher ignores HTTP method
        • 10
          Internal subcomponents coupling
        • 7
          Not nodejs
        • 7
          Configuration hell
        • 7
          Admin
        • 5
          Not as clean and nice documentation like Laravel
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          Bloated admin panel included
        • 3
          Not typed
        • 3
          Python
        • 2
          Overwhelming folder structure
        • 2
          InEffective Multithreading

        related Django posts

        Dmitry Mukhin

        Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.

        Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.

        For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d go with Ember.js.

        However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.

        All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.

        See more

        Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?

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        Checkout.com logo

        Checkout.com

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        Unleash innovation with connected payments
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            Midtrans logo

            Midtrans

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            Empowering commerce through technology
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                Servicebot logo

                Servicebot

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                The easiest and fastest way to launch with Stripe
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                    BankFeeds.io logo

                    BankFeeds.io

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                    Connector between your Stripe and Xero accounts
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