What is Laravel Spark and what are its top alternatives?
Top Alternatives to Laravel Spark
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. ...
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. ...
It is a free, online software alternative to Quickbooks. Wave helps manage invoices, credit card payments, accounting & payroll. Best for small businesses & freelancers. ...
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
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. ...
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. ...
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. ...
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
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!
- Fast and Flexible48
- One platform for every big data problem7
- Easy to install and to use6
- Great for distributed SQL like applications6
- Works well for most Datascience usecases3
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Interactive Query0
related Apache Spark posts
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:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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:
(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )
- 1 free instance without CC forever?1
related Wave posts
- Rapid development634
- Open source468
- Great community401
- Easy to learn353
- Beautiful code215
- Great packages191
- Great libraries178
- Comes with auth and crud admin panel65
- Great documentation60
- Great for web58
- Great orm37
- Great for api34
- All included27
- Web Apps22
- Used by top startups18
- Easy setup15
- Convention over configuration12
- The Django community9
- Allows for very rapid development with great libraries9
- Great MVC and templating engine6
- King of backend world6
- Its elegant and practical6
- Batteries included5
- Full stack5
- Fast prototyping5
- Easy Structure , useful inbuilt library5
- Easy to develop end to end AI Models5
- Have not found anything that it can't do5
- Very quick to get something up and running4
- Easy to use4
- Great peformance3
- Just the right level of abstraction3
- Full-Text Search3
- Zero code burden to change databases3
- Python community3
- Many libraries3
- Easy to change database manager2
- Node js1
- Underpowered templating25
- Underpowered ORM19
- Autoreload restarts whole server19
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs7
- Configuration hell6
- Not as clean and nice documentation like Laravel4
- Not typed3
- Bloated admin panel included3
- Overwhelming folder structure2
- InEffective Multithreading1
related Django posts
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.