Alternatives to Scala logo

Alternatives to Scala

Kotlin, Python, Clojure, Java, and Golang are the most popular alternatives and competitors to Scala.
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6.9K
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What is Scala and what are its top alternatives?

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
Scala is a tool in the Languages category of a tech stack.
Scala is an open source tool with 13.9K GitHub stars and 3.2K GitHub forks. Here’s a link to Scala's open source repository on GitHub

Top Alternatives to Scala

  • Kotlin
    Kotlin

    Kotlin is a statically typed programming language for the JVM, Android and the browser, 100% interoperable with Java ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • Clojure
    Clojure

    Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system. ...

  • Java
    Java

    Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere! ...

  • Golang
    Golang

    Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...

  • 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. ...

  • Haskell
    Haskell

    It is a general purpose language that can be used in any domain and use case, it is ideally suited for proprietary business logic and data analysis, fast prototyping and enhancing existing software environments with correct code, performance and scalability. ...

  • Groovy
    Groovy

    It is a powerful multi-faceted programming language for the JVM platform. It supports a spectrum of programming styles incorporating features from dynamic languages such as optional and duck typing, but also static compilation and static type checking at levels similar to or greater than Java through its extensible static type checker. It aims to greatly increase developer productivity with many powerful features but also a concise, familiar and easy to learn syntax. ...

Scala alternatives & related posts

Kotlin logo

Kotlin

11.7K
9.6K
618
Statically typed Programming Language targeting JVM and JavaScript
11.7K
9.6K
+ 1
618
PROS OF KOTLIN
  • 69
    Interoperable with Java
  • 53
    Functional Programming support
  • 48
    Null Safety
  • 44
    Official Android support
  • 43
    Backed by JetBrains
  • 36
    Concise
  • 34
    Modern Multiplatform Applications
  • 27
    Expressive Syntax
  • 25
    Coroutines
  • 25
    Target to JVM
  • 23
    Open Source
  • 18
    Practical elegance
  • 18
    Statically Typed
  • 16
    Android support
  • 16
    Type Inference
  • 13
    Readable code
  • 12
    Powerful as Scala, simple as Python, plus coroutines <3
  • 12
    Better Java
  • 10
    Pragmatic
  • 9
    Lambda
  • 8
    Target to JavaScript
  • 8
    Better language for android
  • 8
    Expressive DSLs
  • 6
    Used for Android
  • 6
    Less boilerplate code
  • 5
    Less code
  • 5
    Fast Programming language
  • 4
    Functional Programming Language
  • 4
    Friendly community
  • 4
    Less boiler plate code
  • 3
    Native
  • 2
    Official Google Support
  • 2
    Spring
  • 2
    Latest version of Java
CONS OF KOTLIN
  • 7
    Java interop makes users write Java in Kotlin
  • 4
    Frequent use of {} keys
  • 2
    Hard to make teams adopt the Kotlin style
  • 2
    Nonullpointer Exception
  • 1
    Friendly community
  • 1
    Slow compiler
  • 1
    No boiler plate code

related Kotlin posts

Shivam Bhargava
AVP - Business at VAYUZ Technologies Pvt. Ltd. · | 22 upvotes · 428.7K views

Hi Community! Trust everyone is keeping safe. I am exploring the idea of building a #Neobank (App) with end-to-end banking capabilities. In the process of exploring this space, I have come across multiple Apps (N26, Revolut, Monese, etc) and explored their stacks in detail. The confusion remains to be the Backend Tech to be used?

What would you go with considering all of the languages such as Node.js Java Rails Python are suggested by some person or the other. As a general trend, I have noticed the usage of Node with React on the front or Node with a combination of Kotlin and Swift. Please suggest what would be the right approach!

See more
Jakub Olan
Node.js Software Engineer · | 17 upvotes · 366.7K views

In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.

See more
Python logo

Python

194.7K
163.8K
6.7K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
194.7K
163.8K
+ 1
6.7K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 947
    Readable code
  • 834
    Beautiful code
  • 780
    Rapid development
  • 682
    Large community
  • 426
    Open source
  • 385
    Elegant
  • 278
    Great community
  • 268
    Object oriented
  • 214
    Dynamic typing
  • 75
    Great standard library
  • 56
    Very fast
  • 51
    Functional programming
  • 43
    Scientific computing
  • 43
    Easy to learn
  • 33
    Great documentation
  • 26
    Matlab alternative
  • 25
    Productivity
  • 25
    Easy to read
  • 21
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 15
    Free
  • 14
    Powerfull language
  • 14
    Machine learning support
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 9
    Explicit is better than implicit
  • 8
    Unlimited power
  • 8
    Ease of development
  • 8
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Great for tooling
  • 5
    Fast coding and good for competitions
  • 5
    I love snakes
  • 5
    Python has great libraries for data processing
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    High Documented language
  • 5
    Flat is better than nested
  • 5
    Although practicality beats purity
  • 4
    Rapid Prototyping
  • 4
    Readability counts
  • 3
    Great for analytics
  • 3
    Web scraping
  • 3
    Now is better than never
  • 3
    Plotting
  • 3
    Lists, tuples, dictionaries
  • 3
    Socially engaged community
  • 3
    Complex is better than complicated
  • 3
    Multiple Inheritence
  • 3
    Beautiful is better than ugly
  • 3
    CG industry needs
  • 2
    No cruft
  • 2
    Many types of collections
  • 2
    Easy to learn and use
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    List comprehensions
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Easy to setup and run smooth
  • 2
    Import this
  • 1
    Powerful language for AI
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Can understand easily who are new to programming
  • 1
    Flexible and easy
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Good for hacking
  • 1
    Should START with this but not STICK with This
  • 1
    Pip install everything
  • 1
    It is Very easy , simple and will you be love programmi
  • 0
    Powerful
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 5
    Fake object-oriented programming
  • 5
    The "lisp style" whitespaces
  • 4
    Official documentation is unclear.
  • 4
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 4
    The benevolent-dictator-for-life quit
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.5M views

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

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 1.7M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

See more
Clojure logo

Clojure

1.4K
1.3K
1.1K
A dynamic programming language that targets the Java Virtual Machine
1.4K
1.3K
+ 1
1.1K
PROS OF CLOJURE
  • 118
    It is a lisp
  • 101
    Persistent data structures
  • 100
    Concise syntax
  • 89
    jvm-based language
  • 88
    Concurrency
  • 82
    Interactive repl
  • 76
    Code is data
  • 61
    Open source
  • 60
    Lazy data structures
  • 55
    Macros
  • 48
    Functional
  • 22
    Simplistic
  • 21
    Immutable by default
  • 19
    Excellent collections
  • 18
    Fast-growing community
  • 14
    Simple (not easy!)
  • 14
    Multiple host languages
  • 14
    Practical Lisp
  • 9
    Because it's really fun to use
  • 9
    Community
  • 9
    Addictive
  • 8
    It creates Reusable code
  • 8
    Web friendly
  • 8
    Rapid development
  • 7
    Minimalist
  • 5
    Programmable programming language
  • 5
    Java interop
  • 4
    Regained interest in programming
  • 3
    EDN
  • 3
    Compiles to JavaScript
  • 2
    Share a lot of code with clojurescript/use on frontend
CONS OF CLOJURE
  • 9
    Cryptic stacktraces
  • 4
    Need to wrap basically every java lib
  • 4
    Toxic community
  • 3
    Good code heavily relies on local conventions
  • 2
    Slow application startup
  • 2
    Tonns of abandonware
  • 1
    Usable only with REPL
  • 1
    Hiring issues
  • 1
    Bad documented libs
  • 1
    Macros are overused by devs
  • 1
    Tricky profiling
  • 1
    IDE with high learning curve
  • 1
    Configuration bolierplate
  • 1
    Conservative community
  • 0
    Have no good and fast fmt

related Clojure posts

Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).

The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.

While we continue to be happy with Clojure for our internal services, we felt that its relatively narrow adoption could impede Singer's growth. We chose Python both because it is well suited to the task, and it seems to have reached critical mass among data engineers. All that being said, the Singer spec is language agnostic, and integrations and libraries have been developed in JavaScript, Go, and Clojure.

See more

I adopted Clojure and ClojureScript because:

  • it's 1 language, multiple platforms.
  • Simple syntax.
  • Designed to avoid unwanted side effects and bugs.
  • Immutable data-structures.
  • Compact code, very expressive.
  • Source code is data.
  • It has super-flexible macro.
  • Has metadata.
  • Interoperability with JavaScript, Java and C#.
See more
Java logo

Java

110.7K
85.7K
3.7K
A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
110.7K
85.7K
+ 1
3.7K
PROS OF JAVA
  • 592
    Great libraries
  • 444
    Widely used
  • 400
    Excellent tooling
  • 389
    Huge amount of documentation available
  • 332
    Large pool of developers available
  • 204
    Open source
  • 201
    Excellent performance
  • 155
    Great development
  • 149
    Vast array of 3rd party libraries
  • 148
    Used for android
  • 60
    Compiled Language
  • 50
    Used for Web
  • 46
    Managed memory
  • 45
    High Performance
  • 44
    Native threads
  • 43
    Statically typed
  • 35
    Easy to read
  • 33
    Great Community
  • 29
    Reliable platform
  • 24
    Sturdy garbage collection
  • 24
    JVM compatibility
  • 21
    Cross Platform Enterprise Integration
  • 20
    Good amount of APIs
  • 20
    Universal platform
  • 18
    Great Support
  • 14
    Great ecosystem
  • 11
    Backward compatible
  • 11
    Lots of boilerplate
  • 10
    Everywhere
  • 9
    Excellent SDK - JDK
  • 7
    Static typing
  • 7
    It's Java
  • 6
    Mature language thus stable systems
  • 6
    Better than Ruby
  • 6
    Long term language
  • 6
    Cross-platform
  • 6
    Portability
  • 5
    Clojure
  • 5
    Vast Collections Library
  • 5
    Used for Android development
  • 4
    Most developers favorite
  • 4
    Old tech
  • 3
    Javadoc
  • 3
    Stable platform, which many new languages depend on
  • 3
    History
  • 3
    Testable
  • 3
    Best martial for design
  • 3
    Great Structure
  • 2
    Faster than python
  • 2
    Type Safe
CONS OF JAVA
  • 32
    Verbosity
  • 27
    NullpointerException
  • 16
    Nightmare to Write
  • 16
    Overcomplexity is praised in community culture
  • 12
    Boiler plate code
  • 8
    Classpath hell prior to Java 9
  • 6
    No REPL
  • 4
    No property
  • 3
    Code are too long
  • 2
    Non-intuitive generic implementation
  • 2
    There is not optional parameter
  • 2
    Floating-point errors
  • 1
    Terrbible compared to Python/Batch Perormence
  • 1
    Java's too statically, stronglly, and strictly typed
  • 1
    Returning Wildcard Types

related Java posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.5M views

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

See more
Kamil Kowalski
Lead Architect at Fresha · | 28 upvotes · 1.7M views

When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.

See more
Golang logo

Golang

16.1K
12.9K
3.2K
An open source programming language that makes it easy to build simple, reliable, and efficient software
16.1K
12.9K
+ 1
3.2K
PROS OF GOLANG
  • 534
    High-performance
  • 389
    Simple, minimal syntax
  • 356
    Fun to write
  • 297
    Easy concurrency support via goroutines
  • 269
    Fast compilation times
  • 191
    Goroutines
  • 178
    Statically linked binaries that are simple to deploy
  • 149
    Simple compile build/run procedures
  • 135
    Backed by google
  • 132
    Great community
  • 51
    Garbage collection built-in
  • 43
    Built-in Testing
  • 42
    Excellent tools - gofmt, godoc etc
  • 38
    Elegant and concise like Python, fast like C
  • 35
    Awesome to Develop
  • 25
    Used for Docker
  • 24
    Flexible interface system
  • 22
    Deploy as executable
  • 22
    Great concurrency pattern
  • 19
    Open-source Integration
  • 16
    Fun to write and so many feature out of the box
  • 15
    Easy to read
  • 15
    Go is God
  • 14
    Its Simple and Heavy duty
  • 13
    Powerful and simple
  • 13
    Easy to deploy
  • 12
    Best language for concurrency
  • 11
    Concurrency
  • 10
    Safe GOTOs
  • 10
    Rich standard library
  • 9
    Clean code, high performance
  • 9
    Easy setup
  • 8
    High performance
  • 8
    Hassle free deployment
  • 8
    Simplicity, Concurrency, Performance
  • 7
    Used by Giants of the industry
  • 7
    Single binary avoids library dependency issues
  • 6
    Cross compiling
  • 6
    Simple, powerful, and great performance
  • 5
    Gofmt
  • 5
    Garbage Collection
  • 5
    Very sophisticated syntax
  • 5
    Excellent tooling
  • 5
    WYSIWYG
  • 4
    Keep it simple and stupid
  • 4
    Widely used
  • 4
    Kubernetes written on Go
  • 2
    No generics
  • 1
    Operator goto
CONS OF GOLANG
  • 41
    You waste time in plumbing code catching errors
  • 25
    Verbose
  • 23
    Packages and their path dependencies are braindead
  • 15
    Dependency management when working on multiple projects
  • 15
    Google's documentations aren't beginer friendly
  • 10
    Automatic garbage collection overheads
  • 8
    Uncommon syntax
  • 6
    Type system is lacking (no generics, etc)
  • 3
    Collection framework is lacking (list, set, map)
  • 2
    Best programming language

related Golang posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.5M views

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

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 1.7M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

See more
Apache Spark logo

Apache Spark

2.8K
3.2K
137
Fast and general engine for large-scale data processing
2.8K
3.2K
+ 1
137
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 · 2.6M 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 · 1.2M 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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Haskell

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An advanced purely-functional programming language
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PROS OF HASKELL
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    Purely-functional programming
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    Statically typed
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    Type-safe
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    Open source
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    Great community
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    Built-in concurrency
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    Composable
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    Built-in parallelism
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    Referentially transparent
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    Generics
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    Intellectual satisfaction
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    Type inference
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    If it compiles, it's correct
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    Flexible
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    Monads
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    Proposition testing with QuickCheck
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    Great type system
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    Purely-functional Programming
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    One of the most powerful languages *(see blub paradox)*
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    Highly expressive, type-safe, fast development time
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    Reliable
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    Kind system
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    Pattern matching and completeness checking
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    Better type-safe than sorry
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    Type classes
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    Great maintainability of the code
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    Fun
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    Best in class thinking tool
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    Orthogonality
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    Predictable
CONS OF HASKELL
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    Error messages can be very confusing
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    Too much distraction in language extensions
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    Libraries have poor documentation
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    No best practices
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    No good ABI
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    Sometimes performance is unpredictable
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    Poor packaging for apps written in it for Linux distros
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    Slow compilation

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Vadim Bakaev
Shared insights
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Why I am using Haskell in my free time?

I have 3 reasons for it. I am looking for:

Fun.

Improve functional programming skill.

Improve problem-solving skill.

Laziness and mathematical abstractions behind Haskell makes it a wonderful language.

It is Pure functional, it helps me to write better Scala code.

Highly expressive language gives elegant ways to solve coding puzzle.

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Groovy

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A multi-faceted language for the Java platform
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PROS OF GROOVY
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    Java platform
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    Much more productive than java
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    Concise and readable
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    Very little code needed for complex tasks
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    Dynamic language
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    Nice dynamic syntax for the jvm
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    Very fast
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    Can work with JSON as an object
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    Easy to setup
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    Supports closures (lambdas)
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    Literal Collections
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    Developer Friendly
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    Optional static typing
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    Syntactic sugar
CONS OF GROOVY
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    Groovy Code can be slower than Java Code
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Alex A

Some may wonder why did we choose Grails ? Really good question :) We spent quite some time to evaluate what framework to go with and the battle was between Play Scala and Grails ( Groovy ). We have enough experience with both and, to be honest, I absolutely in love with Scala; however, the tipping point for us was the potential speed of development. Grails allows much faster development pace than Play , and as of right now this is the most important parameter. We might convert later though. Also, worth mentioning, by default Grails comes with Gradle as a build tool, so why change?

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Presently, a web-based ERP is developed in Groovy on Grails. Now the ERP is getting revamped with more functionalities. Is it advisable to continue with the same software and framework or try something new especially Node.js over ExpressJS?

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