Alternatives to Scala logo

Alternatives to Scala

Kotlin, Python, Clojure, Java, and Go are the most popular alternatives and competitors to Scala.
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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 13.1K GitHub stars and 3K 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! ...

  • Go

    Go

    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

    Groovy builds upon the strengths of Java but has additional power features inspired by languages like Python, Ruby and Smalltalk. It makes modern programming features available to Java developers with almost-zero learning curve. ...

Scala alternatives & related posts

Kotlin logo

Kotlin

5.7K
4.7K
473
Statically typed Programming Language targeting JVM and JavaScript
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4.7K
+ 1
473
PROS OF KOTLIN
  • 59
    Interoperable with Java
  • 46
    Functional Programming support
  • 40
    Null Safety
  • 39
    Backed by JetBrains
  • 37
    Official Android support
  • 27
    Modern Multiplatform Applications
  • 26
    Concise
  • 23
    Expressive Syntax
  • 20
    Coroutines
  • 20
    Target to JVM
  • 19
    Open Source
  • 14
    Practical elegance
  • 13
    Statically Typed
  • 12
    Type Inference
  • 11
    Android support
  • 8
    Pragmatic
  • 8
    Better Java
  • 7
    Powerful as Scala, simple as Python, plus coroutines <3
  • 7
    Readable code
  • 6
    Lambda
  • 6
    Better language for android
  • 6
    Expressive DSLs
  • 5
    Target to JavaScript
  • 4
    Less boilerplate code
  • 3
    Fast Programming language
  • 3
    Used for Android
  • 2
    Functional Programming Language
  • 1
    Less code
  • 1
    Latest version of Java
CONS OF KOTLIN
  • 4
    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
    No boiler plate code

related Kotlin posts

Shivam Bhargava
AVP - Business at VAYUZ Technologies Pvt. Ltd. · | 22 upvotes · 164.5K 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 · 168K 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

117K
91.2K
6.4K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
117K
91.2K
+ 1
6.4K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 920
    Readable code
  • 814
    Beautiful code
  • 763
    Rapid development
  • 668
    Large community
  • 414
    Open source
  • 375
    Elegant
  • 264
    Great community
  • 257
    Object oriented
  • 206
    Dynamic typing
  • 68
    Great standard library
  • 51
    Very fast
  • 47
    Functional programming
  • 33
    Scientific computing
  • 31
    Easy to learn
  • 29
    Great documentation
  • 25
    Matlab alternative
  • 22
    Productivity
  • 21
    Easy to read
  • 19
    Simple is better than complex
  • 17
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 14
    Free
  • 14
    Powerful
  • 13
    Fast and simple
  • 13
    Powerfull language
  • 12
    Scripting
  • 9
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Ease of development
  • 8
    Unlimited power
  • 7
    Import antigravity
  • 7
    Clear and easy and powerfull
  • 6
    Print "life is short, use python"
  • 6
    It's lean and fun to code
  • 5
    Great for tooling
  • 5
    Fast coding and good for competitions
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    I love snakes
  • 5
    Although practicality beats purity
  • 5
    Flat is better than nested
  • 4
    Readability counts
  • 3
    Multiple Inheritence
  • 3
    Complex is better than complicated
  • 3
    Lists, tuples, dictionaries
  • 3
    Plotting
  • 3
    Rapid Prototyping
  • 3
    Great for analytics
  • 3
    Socially engaged community
  • 3
    Beautiful is better than ugly
  • 3
    CG industry needs
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 2
    List comprehensions
  • 2
    Generators
  • 2
    Special cases aren't special enough to break the rules
  • 2
    Now is better than never
  • 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
    Simple and easy to learn
  • 2
    Import this
  • 1
    Powerful language for AI
  • 1
    Because of Netflix
  • 1
    Pip install everything
  • 1
    Web scraping
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Easy to setup and run smooth
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Only one way to do it
  • 1
    A-to-Z
  • 1
    Many types of collections
  • 1
    Flexible and easy
  • 0
    Pro
  • 0
    Powerful
CONS OF PYTHON
  • 49
    Still divided between python 2 and python 3
  • 26
    Poor syntax for anonymous functions
  • 26
    Performance impact
  • 18
    GIL
  • 18
    Package management is a mess
  • 13
    Too imperative-oriented
  • 12
    Hard to understand
  • 10
    Dynamic typing
  • 8
    Very slow
  • 8
    Not everything is expression
  • 7
    Indentations matter a lot
  • 7
    Explicit self parameter in methods
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Hard to obfuscate
  • 5
    The "lisp style" whitespaces
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Threading
  • 4
    Fake object-oriented programming
  • 4
    Incredibly slow
  • 3
    Not suitable for autocomplete
  • 3
    Official documentation is unclear.
  • 2
    Circular import
  • 1
    Training wheels (forced indentation)
  • 1
    Meta classes

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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
Director of Developer Marketing at Stream · | 35 upvotes · 1.2M 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

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A dynamic programming language that targets the Java Virtual Machine
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PROS OF CLOJURE
  • 111
    It is a lisp
  • 97
    Persistent data structures
  • 96
    Concise syntax
  • 85
    jvm-based language
  • 85
    Concurrency
  • 78
    Interactive repl
  • 74
    Code is data
  • 60
    Open source
  • 56
    Lazy data structures
  • 54
    Macros
  • 45
    Functional
  • 21
    Simplistic
  • 19
    Immutable by default
  • 18
    Excellent collections
  • 17
    Fast-growing community
  • 14
    Multiple host languages
  • 12
    Simple (not easy!)
  • 12
    Practical Lisp
  • 9
    Addictive
  • 9
    Community
  • 8
    It creates Reusable code
  • 8
    Because it's really fun to use
  • 7
    Web friendly
  • 7
    Minimalist
  • 7
    Rapid development
  • 5
    Java interop
  • 5
    Programmable programming language
  • 4
    Regained interest in programming
  • 3
    Compiles to JavaScript
  • 2
    Share a lot of code with clojurescript/use on frontend
  • 2
    EDN
CONS OF CLOJURE
  • 9
    Cryptic stacktraces
  • 4
    Need to wrap basically every java lib
  • 3
    Toxic community
  • 3
    Good code heavily relies on local conventions
  • 2
    Tonns of abandonware
  • 2
    Slow application startup
  • 1
    Tricky profiling
  • 1
    Macros are overused by devs
  • 1
    Have no good and fast fmt
  • 1
    Bad documented libs
  • 1
    Usable only with REPL
  • 1
    LISP!!!!!!!!!!
  • 1
    Configuration bolierplate
  • 1
    Conservative community
  • 1
    IDE with high learning curve

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

74.3K
53K
3.5K
A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
74.3K
53K
+ 1
3.5K
PROS OF JAVA
  • 572
    Great libraries
  • 433
    Widely used
  • 396
    Excellent tooling
  • 378
    Huge amount of documentation available
  • 328
    Large pool of developers available
  • 197
    Open source
  • 192
    Excellent performance
  • 150
    Great development
  • 143
    Used for android
  • 142
    Vast array of 3rd party libraries
  • 54
    Compiled Language
  • 46
    Used for Web
  • 42
    Managed memory
  • 42
    Native threads
  • 40
    High Performance
  • 35
    Statically typed
  • 31
    Easy to read
  • 29
    Great Community
  • 25
    Reliable platform
  • 23
    JVM compatibility
  • 22
    Sturdy garbage collection
  • 19
    Cross Platform Enterprise Integration
  • 18
    Universal platform
  • 16
    Great Support
  • 16
    Good amount of APIs
  • 11
    Lots of boilerplate
  • 10
    Great ecosystem
  • 10
    Backward compatible
  • 9
    Everywhere
  • 7
    Excellent SDK - JDK
  • 6
    Mature language thus stable systems
  • 5
    Better than Ruby
  • 5
    Portability
  • 5
    Cross-platform
  • 5
    Static typing
  • 5
    Clojure
  • 5
    It's Java
  • 4
    Old tech
  • 4
    Vast Collections Library
  • 3
    Most developers favorite
  • 3
    Stable platform, which many new languages depend on
  • 3
    Long term language
  • 3
    Great Structure
  • 3
    Best martial for design
  • 3
    Used for Android development
  • 2
    Testable
  • 1
    Javadoc
CONS OF JAVA
  • 29
    Verbosity
  • 23
    NullpointerException
  • 15
    Overcomplexity is praised in community culture
  • 13
    Nightmare to Write
  • 10
    Boiler plate code
  • 8
    Classpath hell prior to Java 9
  • 6
    No REPL
  • 4
    No property
  • 2
    Code are too long
  • 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
    Non-intuitive generic implementation
  • 1
    Returning Wildcard Types

related Java posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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

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Kamil Kowalski
Lead Architect at Fresha · | 27 upvotes · 811.8K 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
Go logo

Go

11.4K
9.6K
3K
An open source programming language that makes it easy to build simple, reliable, and efficient software
11.4K
9.6K
+ 1
3K
PROS OF GO
  • 511
    High-performance
  • 375
    Simple, minimal syntax
  • 343
    Fun to write
  • 289
    Easy concurrency support via goroutines
  • 261
    Fast compilation times
  • 183
    Goroutines
  • 173
    Statically linked binaries that are simple to deploy
  • 144
    Simple compile build/run procedures
  • 129
    Backed by google
  • 125
    Great community
  • 46
    Garbage collection built-in
  • 40
    Built-in Testing
  • 36
    Excellent tools - gofmt, godoc etc
  • 33
    Elegant and concise like Python, fast like C
  • 28
    Awesome to Develop
  • 22
    Flexible interface system
  • 21
    Used for Docker
  • 21
    Great concurrency pattern
  • 18
    Deploy as executable
  • 17
    Open-source Integration
  • 14
    Fun to write and so many feature out of the box
  • 11
    Its Simple and Heavy duty
  • 11
    Easy to read
  • 10
    Powerful and simple
  • 9
    Go is God
  • 9
    Safe GOTOs
  • 9
    Easy to deploy
  • 7
    Hassle free deployment
  • 7
    Rich standard library
  • 7
    Concurrency
  • 7
    Best language for concurrency
  • 7
    Easy setup
  • 6
    Used by Giants of the industry
  • 6
    Simplicity, Concurrency, Performance
  • 6
    Clean code, high performance
  • 6
    High performance
  • 6
    Single binary avoids library dependency issues
  • 5
    Simple, powerful, and great performance
  • 5
    Cross compiling
  • 4
    Garbage Collection
  • 4
    Excellent tooling
  • 4
    Very sophisticated syntax
  • 4
    Gofmt
  • 4
    WYSIWYG
  • 3
    Kubernetes written on Go
  • 2
    Keep it simple and stupid
  • 1
    Widely used
  • 0
    No generics
  • 0
    Operator goto
CONS OF GO
  • 38
    You waste time in plumbing code catching errors
  • 23
    Verbose
  • 22
    Packages and their path dependencies are braindead
  • 15
    Dependency management when working on multiple projects
  • 12
    Google's documentations aren't beginer friendly
  • 10
    Automatic garbage collection overheads
  • 7
    Uncommon syntax
  • 6
    Type system is lacking (no generics, etc)
  • 2
    Collection framework is lacking (list, set, map)

related Go posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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
Director of Developer Marketing at Stream · | 35 upvotes · 1.2M 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

2.1K
2.3K
131
Fast and general engine for large-scale data processing
2.1K
2.3K
+ 1
131
PROS OF APACHE SPARK
  • 58
    Open-source
  • 47
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query
CONS OF APACHE SPARK
  • 2
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 20 upvotes · 1.7M 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 · 873.8K 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 )

See more
Haskell logo

Haskell

848
875
471
An advanced purely-functional programming language
848
875
+ 1
471
PROS OF HASKELL
  • 82
    Purely-functional programming
  • 63
    Statically typed
  • 56
    Type-safe
  • 38
    Open source
  • 37
    Great community
  • 29
    Composable
  • 28
    Built-in concurrency
  • 27
    Built-in parallelism
  • 21
    Referentially transparent
  • 18
    Generics
  • 13
    Intellectual satisfaction
  • 13
    Type inference
  • 10
    If it compiles, it's correct
  • 7
    Flexible
  • 6
    Monads
  • 4
    Great type system
  • 3
    Proposition testing with QuickCheck
  • 2
    Best in class thinking tool
  • 2
    Great maintainability of the code
  • 2
    Fun
  • 2
    One of the most powerful languages *(see blub paradox)*
  • 2
    Highly expressive, type-safe, fast development time
  • 1
    Type classes
  • 1
    Better type-safe than sorry
  • 1
    Pattern matching and completeness checking
  • 1
    Kind system
  • 1
    Purely-functional Programming
  • 1
    Reliable
  • 0
    Orthogonality
  • 0
    Predictable
CONS OF HASKELL
  • 6
    Too much distraction in language extensions
  • 5
    Error messages can be very confusing
  • 3
    No best practices
  • 3
    No good ABI
  • 3
    Libraries have poor documentation
  • 2
    Sometimes performance is unpredictable
  • 2
    Poor packaging for apps written in it for Linux distros
  • 1
    Slow compilation

related Haskell posts

Vadim Bakaev
Shared insights
on
HaskellHaskellScalaScala

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 logo

Groovy

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A dynamic language for the Java platform
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PROS OF GROOVY
  • 43
    Java platform
  • 32
    Much more productive than java
  • 28
    Concise and readable
  • 27
    Very little code needed for complex tasks
  • 21
    Dynamic language
  • 12
    Nice dynamic syntax for the jvm
  • 9
    Very fast
  • 6
    Easy to setup
  • 6
    Can work with JSON as an object
  • 5
    Literal Collections
  • 4
    Supports closures (lambdas)
  • 2
    Syntactic sugar
  • 2
    Developer Friendly
  • 1
    Interoperable with Java
  • 1
    Optional static typing
CONS OF GROOVY
  • 3
    Groovy Code can be slower than Java Code
  • 1
    Objects cause stateful/heap mess

related Groovy posts

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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Midhun Harikumar
Senior Associate at Cognizant Technology Solutions · | 1 upvote · 53.2K views
Shared insights
on
SQLiteSQLiteGradleGradleGroovyGroovy

This app uses SQLite to store internal data and is superfast, especially good to use with Android JetPack framework like Room. Gradle is good for managing the dependencies and Groovy script enables some advanced configuration.

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