This document provides an overview of functional programming concepts in Scala. It discusses the history and advantages of functional programming. It then covers the basics of Scala including its support for object oriented and functional programming. Key functional programming aspects of Scala like immutable data, higher order functions, and implicit parameters are explained with examples.
Scala Intro training @ Lohika, Odessa, UA.
This is a basic Scala Programming Language overview intended to evangelize the language among any-language programmers.
In this Meetup Victor Perepelitsky - R&D Technical Leader at LivePerson leading the 'Real Time Event Processing Platform' team , will talk about Java 8', 'Stream API', 'Lambda', and 'Method reference'.
Victor will clarify what functional programming is and how can you use java 8 in order to create better software.
Victor will also cover some pain points that Java 8 did not solve regarding functionality and see how you can work around it.
Presentation provides introduction and detailed explanation of the Java 8 Lambda and Streams. Lambda covers with Method references, default methods and Streams covers with stream operations,types of streams, collectors. Also streams are elaborated with parallel streams and benchmarking comparison of sequential and parallel streams.
Additional slides are covered with Optional, Splitators, certain projects based on lambda and streams
Scala Intro training @ Lohika, Odessa, UA.
This is a basic Scala Programming Language overview intended to evangelize the language among any-language programmers.
In this Meetup Victor Perepelitsky - R&D Technical Leader at LivePerson leading the 'Real Time Event Processing Platform' team , will talk about Java 8', 'Stream API', 'Lambda', and 'Method reference'.
Victor will clarify what functional programming is and how can you use java 8 in order to create better software.
Victor will also cover some pain points that Java 8 did not solve regarding functionality and see how you can work around it.
Presentation provides introduction and detailed explanation of the Java 8 Lambda and Streams. Lambda covers with Method references, default methods and Streams covers with stream operations,types of streams, collectors. Also streams are elaborated with parallel streams and benchmarking comparison of sequential and parallel streams.
Additional slides are covered with Optional, Splitators, certain projects based on lambda and streams
An overview of functional programming. You can do functional programming in any language, it’s just that some languages push you toward this style. There are many styles of programming, depending on the dimension you look at and the features available in the language: Imperative vs Functional, Compiled vs Scripting, Static vs Dynamic, Implicit vs Explicit type conversion, Managed vs Unmanaged (memory). We'll talk about these concepts.
Github: https://github.com/ijcd
Twitter: @ijcd
San Francisco, California
This was a short introduction to Scala programming language.
me and my colleague lectured these slides in Programming Language Design and Implementation course in K.N. Toosi University of Technology.
Introduction to Java Programming, Basic Structure, variables Data type, input...Mr. Akaash
This is First Lecture of java Programming which cover all basic points (ie. History and feature of java, Introduction to java, about variables data type and compilation....
What is Python Lambda Function? Python Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/RQRCWDK9UkA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Lambda' is to educate you about the Lambda functions of Python and help you understand how to use them in various scenarios. Below are the topics covered in this PPT:
What are Python Lambda functions?
Why are they used?
How to write anonymous functions?
Lambda functions within user-defined functions
Using Anonymous functions within
- filter()
- map()
- reduce()
Solving algebric expressions using Lambda
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Python Advanced – Building on the foundationKevlin Henney
This is a two-day course in Python programming aimed at professional programmers. The course material provided here is intended to be used by teachers of the language, but individual learners might find some of this useful as well.
The course assume the students already know Python, to the level taught in the Python Foundation course: http://www.slideshare.net/Kevlin/python-foundation-a-programmers-introduction-to-python-concepts-style)
The course is released under Creative Commons Attribution 4.0. Its primary location (along with the original PowerPoint) is at https://github.com/JonJagger/two-day-courses/tree/master/pa
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.
Java is Object Oriented Programming. Java 8 is the latest version of the Java which is used by many companies for the development in many areas. Mobile, Web, Standalone applications.
An overview of functional programming. You can do functional programming in any language, it’s just that some languages push you toward this style. There are many styles of programming, depending on the dimension you look at and the features available in the language: Imperative vs Functional, Compiled vs Scripting, Static vs Dynamic, Implicit vs Explicit type conversion, Managed vs Unmanaged (memory). We'll talk about these concepts.
Github: https://github.com/ijcd
Twitter: @ijcd
San Francisco, California
This was a short introduction to Scala programming language.
me and my colleague lectured these slides in Programming Language Design and Implementation course in K.N. Toosi University of Technology.
Introduction to Java Programming, Basic Structure, variables Data type, input...Mr. Akaash
This is First Lecture of java Programming which cover all basic points (ie. History and feature of java, Introduction to java, about variables data type and compilation....
What is Python Lambda Function? Python Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/RQRCWDK9UkA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Lambda' is to educate you about the Lambda functions of Python and help you understand how to use them in various scenarios. Below are the topics covered in this PPT:
What are Python Lambda functions?
Why are they used?
How to write anonymous functions?
Lambda functions within user-defined functions
Using Anonymous functions within
- filter()
- map()
- reduce()
Solving algebric expressions using Lambda
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Python Advanced – Building on the foundationKevlin Henney
This is a two-day course in Python programming aimed at professional programmers. The course material provided here is intended to be used by teachers of the language, but individual learners might find some of this useful as well.
The course assume the students already know Python, to the level taught in the Python Foundation course: http://www.slideshare.net/Kevlin/python-foundation-a-programmers-introduction-to-python-concepts-style)
The course is released under Creative Commons Attribution 4.0. Its primary location (along with the original PowerPoint) is at https://github.com/JonJagger/two-day-courses/tree/master/pa
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.
Java is Object Oriented Programming. Java 8 is the latest version of the Java which is used by many companies for the development in many areas. Mobile, Web, Standalone applications.
Anatomy of Data Frame API : A deep dive into Spark Data Frame APIdatamantra
In this presentation, we discuss about internals of spark data frame API. All the code discussed in this presentation available at https://github.com/phatak-dev/anatomy_of_spark_dataframe_api
Anatomy of Data Source API : A deep dive into Spark Data source APIdatamantra
In this presentation, we discuss how to build a datasource from the scratch using spark data source API. All the code discussed in this presentation available at https://github.com/phatak-dev/anatomy_of_spark_datasource_api
This slide deck is used as an introduction to the internals of Apache Spark, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Check out these exercises: http://de.slideshare.net/nicolayludwig/3-cpp-procedural-programmingexercises
- Procedural Programming
- Predefined and User defined Functions
- Declaration and Definition of Functions
- Procedural and recursive Function Calling
- Namespaces and separated Function Definitions
- A Glimpse of Separated Compilation and Translation Units
Over recent years, JavaScript has increasingly gained popularity, partly due to libraries that are developed to make JavaScript apps/effects easier to create for those who may not have fully grasped the core language yet.
While in the past it was a common argument that JavaScript was a basic language and was very ‘slap dash’ with no real foundation; this is no longer the case, especially with the introduction of high scale web applications and ‘adaptations’ such as JSON (JavaScript Object Notation).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
5. Programming Paradigms
● Programming Paradigm is a approach to programming
● It will be based on set of principles or theory
● Different ways of thinking.
● Different types
○ Imperative
○ Functional
8. Expression vs Statement
Expression : evaluated to produce a value without changing state.
Statement : executed to update state.
Example:
Expression -> val isEven = if(x%2 == 0) true else false
11. History of Functional Programming
● Mathematical abstraction Lambda Calculus was the foundation
for functional Programming
● Lambda calculus was developed in 1930s to be used for
functional definition, functional application and recursion.
● It states that any computation can be achieved by sending the
input to some black box, which produces its result.
● This black box is lambda functions, and in FP it’s functions.
● LISP was the first functional programming language. It was
defined by McCarthy in 1958
12. Imperative vs Functional Programming
Characteristics Imperative Functional
State changes Important Non-existent
Order of execution Important Low importance
Primary flow control
Loops, conditions and method
(function) calls
Function calls
Primary manipulation unit
Instances of structures or
classes
Functions
13. Characteristics of FP
● Immutable data
● Lazy evaluation
● No side effects
● Referential transparency
● Functions are first class citizens
14. Immutable data
Mutable Immutable
var collection = [1,2,3,4]
for ( i = 0; i<collection.length; i++) {
collection[i]+=1;
}
Uses loop for updating
collection is updated in place
val collection = [1,2,3,4]
val newCollection = collection.map(value
=> value +1)
Uses transformation for change
Creates a new copy of collection. Leaves
collection intact
15. Advantages of FP
● Better modularity
● Shorter code
● Increased developer productivity
● Less number of bugs
● Perfect for distributed computing because of immutability
● Easier debugging
16. Limitations of FP
● Real world programs have to do side effects
● Difficult to understand for the beginner
● Very difficult to debug the nature of lazy evaluation
● Memory intensive
18. Scala History
● Martin Odersky is the creator
● Curious about compilers from beginning
● Created first compiler in 1995 - Pizza
○ Generics,Function pointers, Case class and pattern matching
● Led to javac compiler and generics eventually
○ Java is a strict language and hard to implement these new ideas
19. Scala History cont..
● 1999 - Started working on a language
○ Combines Object orientation with functional programming
○ Created Funnel language
○ It was not a user friendly language
○ New languages with less libraries are hard to adopt
● In 2001, design for Scala started, released first in 2003
● Scala is not an extension of Java
● Scala translates into JAVA byte code
● One implementation of .Net compatible version, now out dated
20. Scala Philosophy
● Growable language
○ Embed new DSL/add new types
● Scalable language
○ Same concepts in small and large applications
● Deep rather than broad
○ Focus on abstraction and composition
21. Why scala
● Hybrid programming language
● Statically typed
● Runs on the jvm
● Can execute java code
23. Everything is an Object
● Supports object oriented programming
● Including primitive types, everything in scala is an object
Example:
● val x = 10
● x.toString()
28. Operators are also a Function
For example
● val sum = 2 + 3
Is same as,
● val sum = 2.+(3) //because ‘+’ operator here is a function
29. Functions in Scala
● Higher order function
● Anonymous function
● Currying
● Tail recursion
30. Defining a Function in Scala
● Defining a function
In Scala -
def sum(x:Int,y:Int) : Int = x+y
In Java -
public int sum(int x,int y){
return x+y;
}
31. Assigning function to variable
Example :
def add = (x:Int,y:Int) => x+y
add(2,3) //should return 5
32. Higher Order Functions
Function that takes functions as a parameters or return function as
a result is called higher order functions
Example : com.ganesh.functions.HigherOrderFunctions.scala
33. Anonymous Function
Scala provides a relatively lightweight syntax for defining
anonymous functions.
Example:
(x: Int) => x + 1
Shorthand for above function is,
new Function1[Int, Int] {
def apply(x: Int): Int = x + 1
}
34. Currying
Currying allows to turn a function that expects two arguments into a
function that expects only one, and that function returns a function
that expects the second argument. Creating basically a chain of
functions.
Example : com.ganesh.functions.Currying.scala
35. Implicit Parameters
The final parameter list on a method can be marked implicit, which
means the values will be taken from the context in which they are
called.
● It means that if no value is supplied when called, the compiler
will look for an implicit value and pass it in for you.
● Implicits are useful in defining default implementations
available. When you need a custom implementation, you can
pass one in explicitly.
Example : com.ganesh.implicits.Implicits.scala
36. Tail Recursion
● Tail recursion is a special kind of recursion where the recursive
call is the very last thing in the function.
● tail-recursive function will only use a single stack frame as
opposed to hundreds or even thousands in case of normal
recursive function.
● Tail recursion is much faster because it executes using single
stack frame
Example : com.ganesh.functions.TailRecursion.scala
37. Transformation on Collections
We can perform different transformations on collection using
collection API.
Example:
com.ganesh.transformations.SimpleTransformations.scala
com.ganesh.transformations.AdvancedTransformations.scala
38. Error Handling
Scala does not have checked exceptions like Java, so you can't do
something like this to force a programmer to deal with an
exception.
Example :
com.ganesh.exceptionhandling.ExceptionThrowing.scala
com.ganesh.exceptionhandling.UsingOptions.scala
39. References
● https://www.coursera.org/learn/progfun1/ - Scala course by
Martin Odersky
● http://www.dipmat.unict.it/~barba/PROG-LANG/PROGRAMMI-
TESTI/READING-MATERIAL/ShortIntroFPprog-lang.htm -
Functional programming and lambda calculus
● Functional Programming in Scala