HighLevel is the all-in-one sales &amp; marketing platform that agencies can white-label and resell to their clients!

GoHighLevel - It's time to take your Agency to the Next Level

HighLevel is a marketing and advertising platform that captures message leads via voicemail, SMS, emails, FB messenger, and more.

HighLevel - Crunchbase Company Profile & Funding

In this Spotlight Session, we speak ... company for online coaches and consultants. We discuss things like: 💥 You’re only as successful as your least successful client 💥 Most businesses scale to fail in the coaching space Your CSMs are your best sales people 💥 And more! Watch the full session here 👉 https://bit.ly/3OHnhWA #GoHighLevel #SpotlightSession …see more ... Congratulations to Nicholas Scalice, who recently completed the HighLevel Certification ...

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&quot;HighLevel is a white-label marketing platform for digital agencies. Its functionality includes CRM with smart lists, email, two-way SMS, outbound calling, call tracking, call recording, power dialer, form builder, survey builder, funnel builder, website builder, online scheduling, automation ...

HighLevel Company Profile, information, investors, valuation & Funding

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HighLevel Company Profile: Funding & Investors | PitchBook

HighLevel is a company that focuses on providing an all-in-one sales and marketing platform, primarily serving the digital marketing industry. Use the CB Insights Platform to explore HighLevel&#x27;s full profile.

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HighLevel - Provider of text messaging platform for marketing agencies. Raised a total funding of $60M over 1 round from 2 investors.

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It&#x27;s our business to grow yours! Own your market with leading B2B contact data combined with sales intelligence, engagement software, and workflow tools.

HighLevel - Overview, News & Similar companies

Grow your business with High Level Marketing (HLM) - a top digital marketing agency with proven digital strategies to fuel visibility, traffic, and leads.

High Level Marketing | Award Winning Digital Marketing Agency

HighLevel top competitors are HubSpot and they have annual revenue of $91.2M and 521 employees.

HighLevel: Revenue, Competitors, Alternatives

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3. What 1-2 changes have you made in your life to manage stress and get out of the cycle 4. Do you have sufficient support and resources in your company to manage support ? 5. What were the highlevel reasons in your life for this ? 6. What resources have you used to recover from burnout ?

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The Company Object feature is the ability to create a new entity called “Company” and map contacts under them.

How to use the Company Object to group contacts within an ...

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**Help us create the Google Page rank Infra of Web3 \u0026amp; Blockchains **\n\nMuch like how Google’s page rank changed the way we access, discover and interact with information on the web - Blockchains and decentralization of information is changing the way we discover \u0026amp; engage with the information, digital commerce \u0026amp; programmable money

HighLevel

A Digestible High-Level Overview of CPU & GPU Cores

CPU এবং GPU ক্ষমতাগুলির একটি হজমযোগ্য উচ্চ-স্তরের ওভারভিউ

Ein leicht verständlicher Überblick über die CPU- und GPU-Funktionen

Una descripción general digerible de alto nivel de las capacidades de CPU y GPU

Un aperçu digeste de haut niveau des capacités du CPU et du GPU

CPU और GPU क्षमताओं का एक सुपाच्य उच्च-स्तरीय अवलोकन

CPU と GPU の機能について、わかりやすい概要を説明します。

CPU 및 GPU 기능에 대한 소화 가능한 상위 수준 개요

Uma visão geral resumida de alto nível dos recursos de CPU e GPU

Удобный общий обзор возможностей процессора и графического процессора

CPU ve GPU Yeteneklerine Sindirilebilir Üst Düzey Genel Bakış

Tổng quan cấp cao dễ hiểu về khả năng của CPU và GPU

CPU 和 GPU 功能的易懂高级概述

The os package in Python provides a wide range of functionalities for interacting with the operating system. One of the most commonly used aspects of this package is its ability to perform file and folder operations such as iteration, deletion, creation, moving, and opening. In this article, we'll explore how to utilize the os package for these tasks. Iterate Through All the Items Within a Folder You can use the os package in Python to iterate through all the items within a folder using the os.listdir() method. And you can use the os.path.join function to concatenate the folder path with each item in the folder to get the absolute path. Here's an example of how to do it: import os

# Replace 'folder_path' with the path of the folder you want to iterate through
folder_path = 'path_to_folder'

# Iterate through all items in the folder
for item in os.listdir(folder_path):
		print(item) 
		# Get the absolute path of the item
		item_path = os.path.join(folder_path, item) 
		# Display the absolute path of the item
		print(item_path) In this example, returns a list of all the items (files and subfolders) in the specified folder. You can then iterate through this list and perform any operations you need for each item. os.listdir(folder_path) Remember to replace with the actual path of the folder you want to iterate through. 'path_to_folder' Deleting Files and Folders The package provides methods to delete both files and folders. Here's how you can achieve this: os To Delete a File: import os
os.remove('file_path/file.txt') To Delete an Empty Folder: import os
os.rmdir('folder_path')  # Replace with the actual folder path To Delete a Non-Empty Folder and All Its Contents: import shutil
shutil.rmtree('folder_path')  # Replace with the actual folder path Remember to replace and with the actual paths of the file or folder you want to delete. Use these functions with caution as they will permanently delete the specified files or folders. 'file_path/file.txt' 'folder_path' Creating Folders Creating a new folder using the package is straightforward: os import os
os.mkdir('new_folder')  # Replace with the desired folder name Moving and Renaming Files/Folders The package allows us to move and rename files and folders as well. Here's an example of moving a file: os import os
os.rename('old_location/file.txt', 'new_location/file.txt') Opening Files and Folders You can also open a folder in the default file explorer using the package. For instance, to open a folder in the File Explorer (Windows): os import os
os.startfile('folder_path')  # Replace with the actual folder path Conclusion The package in Python offers powerful capabilities for file and folder operations, allowing developers to efficiently manage file system tasks within their applications. os By utilizing the methods provided by the package, you can seamlessly iterate, delete, create, move, and open files and folders in your Python programs. os Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on . Happy exploring!👋 LinkedIn Also published here

How to Use the os Module for High-Level File Operations Such as Iterating, Moving, and More

In this article, I’ll talk about the different types of data. As some of you might be aware, data can be broken down into different types. One such categorization that is very useful when you are building a machine-learning pipeline based on the structure of the data. It is as follows: Structured data Semi-structured data . Unstructured data What is the difference between these types of data? Structured data This term refers to data that is organized in a tabular format or in something like a relational database which organizes data in multiple tables which can then be joined together. So structured data presents the easiest type of data to work with. If your data is stored in an SQL database, for example, then most data scientists will find it pretty easy to access the database and then extract insights from the data. That being said, not all databases are created equal. Some databases might be organized in a very bad manner, other databases might be organized in a very easy-to-use manner. But all things being equal, structured data is easy to work with. If you look deep down into how machine learning pipelines are created, well, you always need structured data. So even if you have data that is haphazardly formatted, algorithms steal, digest this data, and then transform it into a structured format. Semi-structured data This term refers to data that is not completely organized but not disorganized either. A good example of this is HTML, JSON, and XML. For those of you who are familiar with HTML or JSON, if you're not, it's very easy to Google JSON and see an example of what a JSON file looks like. You'll very quickly see that JSON seems to follow some kind of structure and it's the same for HTML. You see something which looks like code, but then again, the JSON or HTML are not fully structured, so they're not organized in the table. An HTML file or Adjacent file can look very different from some other HTML or JSON file. This means that there are certain freedoms that the developers of those files take, and this can make it somewhat challenging to work with them. How do data scientists collect data from different sources? A data scientist will have to extract information from the semi-structured data and then restructure it into a tabular format. The challenge here is that there are usually many ways to do that. And this step data can be quite time-consuming depending on the kind of data and how the data is organized. In general, I'm not a huge fan of semi-structured data. I personally, as a data scientist, prefer structured data. Like most data scientists, however, semi-structured data is very useful in domains like social media. Social media is full of text data, image data, video data, and data formats like JSO let us store this data alongside meta information. So, you can store a video, let's say, and then you can store who created this video, comment around this video, etc. This is easier to do using JSON than using SQL, for example. Therefore, semi-structured formats have become so popular in the last ten years. Semi-structured data quite often goes hand in hand with no SQL databases and big data. Unstructured data This term refers to data where there is clearly no structure. For example, data set that consists only of images or videos or audio is an example of an unstructured data set. So, information in an unstructured data set does not follow a preexisting data model. And this makes it quite challenging to work with because someone might have to go through all the data and understand whether some of the data is potentially noisy or have some other issues which are going to prevent a machine-learning pipeline from being successfully built. In most cases, unstructured data in the real world is usually you're going to encounter it in two situations. It's either some sort of open data set or a machine learning competition where someone curates an unstructured data set and you must use this data and try to predict whether a photo contains humans or animals as best as you can. Or the other case where you might encounter structured data is when a data strategy was not designed and somehow a company ended up having structured data instead of semi-structured data. Because really, in most scenarios we expect to see this data alongside some meta information, like when this video showed up, who posted this if we're talking about social media. How does a data scientist digest this type of data? I would expect that in most cases, most of the data should be semi-structured. There are still cases where data might just be unstructured because there is not so much that we can do about it. For example, in customer support, maybe a data set consists of questions and responses, and you want to build a bot based on those questions and responses so it can automatically produce answers to different queries. Well, in this case, probably there's not much you can do to structure the data. In one way or another, you're going to have to end up with an unstructured data set. But unstructured data, even though if it is challenging, quite often it can still be successfully analyzed. In most cases, we're using . There are deep learning algorithms in order to digest this kind of data. And deep learning has been very successful with data like audio data, natural language data, images, and all this sort of stuff. deep learning Conclusion This was a summary of the different types of data that you can encounter in business. We talked about structured data, semi-structured data, and unstructured data. Structured data is usually the low-hanging fruit for a business. And ideally as a business, you want to have a data strategy in place which makes sure that most of your data is stored in a structured format. The reason is that this makes the life of data scientists much easier, and they will be able to spend more time on valuable tasks instead of just data wrangling. Schema structured data and unstructured data have started to become to grow in the last 10 - 15 years. It's the after all. But in most cases, you should try to turn structured data and semi-structured data. And once again, semi-structured data is a difficult topic because of the kind of database you need to choose and how you should organize the different fields, and for what purpose. era of big data

A High Level Explanation of Data Types for Decision Makers

If you are switching from web2 to web3 or if you want to learn web3 then you may have doubts like how web3 is different from web2? Are there any similarities? Can I directly jump into web3 without knowing web2 stuff?. So, in this blog, get answers to your questions by learning the architectures of both. This blog is a high-level overview of web3 application architecture. Let's Get Started How Does Web2.0 Application Work? Terminology Let's recall web2.0 things. Three main things. - Frontend is what the user sees and interacts with your application. Frontend - Backend is where we do all the business logic(secrets 👀). Backend - Where we store all the data. Database When we build an application combining these. This is how we visualize: How this works in simple terms with an example - You interact with the application in the browser. For example, you click a login button, then the browser will talk to the server, then that server will talk to the database. The server will query data(your existing creds), then the server will compare your input with queried data and returns the appropriate response(whether login failed or successful). That's it! Simple stuff. Here we have control over our user's data. How does Web3.0 application work? In web3, the architecture is completely different. Here things are a little bit complicated. There will be no centralized database and server. Terminology Two main things. - Same explanation as in web2. Frontend - A distributed network to store data. Here data is immutable - means once written it can't be changed. Blockchain Here we don't have a centralized server, so how can we query data and do all the business logic? For this, we write something called . Smart contracts are pieces of code we write to talk to the Ethereum blockchain. Between there is which executes our code. smart contracts Ethereum Virtual Machine (EVM) How Does the Frontend Talk to Blockchain? In web2, the client makes a request to the server and the server responds and gives back a response. Here in web3, there is something called nodes. So in order to talk to our smart contracts, we need to talk to these nodes. For that, we can use node providers like , etc... or set up your own node. Alchemy Infura How Do we add/write Data in Web3? We have to the transactions with a . This is a big topic in blockchain, so let's don't go too far(I will cover it in upcoming blogs). In simple terms, if you want to add data to blockchain nodes need a signed transaction. We can use to sign transactions. sign private key Metamask Finally, Conclusion There are lots of buzzwords and new terms in web3, but this is the basic overview of what a web3 application architecture looks like. Now to answer some questions -
There is nothing new in the front end except some dom manipulation. Are there any similarities? -
As you can clearly see, both architectures are completely different. So all you need to know from web2 is how the web works and how the internet works 😅. Can I directly jump into web3 without knowing web2 stuff? This article was first published here

A High Level Introduction to Web2.0 and Web3.0

When I try to introduce the concept of AI DApps, I often find that it is particularly difficult when people lack an accurate grasp of what machine learning is. There is an overwhelming amount of information online about machine learning targeted toward audiences with different levels of technical expertise. In this series, I introduce machine learning at different technical levels, with the aim of providing a basic framework that helps you understand machine learning, regardless of your background, starting at the highest level. Traditional Programs vs. Machine Learning Programs In traditional programming, programmers write programs, which are made of lines of code that instruct computers to perform certain tasks. For example, a programmer can write a program to detect whether the word “book” exists in a news article. Each program has an input and output, and in this case, when you input the text of a news article, the program outputs whether the word “book” exists. Machine learning programs are just like traditional computer programs: You give it an input, it gives you an output. As a typical example, a handwriting recognition machine learning program takes as an input an image of a handwritten digit, and gives you as an output of which digit it is (e.g. 3). So if machine learning programs are so similar to traditional programs, why is there all this hype about machine learning? Where is the magic? Magic: Training Machine Learning Programs The magic lies in the process of writing ML programs. Whereas traditional programs are written with a rule-based system, machine learning programs are written (trained) with probability and statistics. As a simplified example, if we want to write a program to recognize Ed Sheeran, in traditional programming, you would tell the computers, “ok, Ed Sheeran has red hair, green eyes, pale skin, wide mouth. If you see these features, it’s Ed Sheeran.” In machine learning, instead of telling computers what Ed Sheeran looks like, you show the computers some photos of Ed Sheeran vs. not Ed Sheeran; the computer then learn via statistics about what Ed Sheeran looks like and when given a new image, it can tell you whether it is Ed Sheeran. As you can tell, in cases like this, the machine learning approach is much more effective because there are too many scenarios you have to enumerate for a rule-based system: what if in this image Ed Sheeran is wearing glasses? What if he has a beard? Even if we get “red hair, green eyes, pale skin, wide mouth” in the image, how do we know it’s Ed Sheeran as opposed to some random ginger dudes? On the other hand, with the statistics-based approach, you could train the computer to recognize Ed Sheeran by showing it enough examples of Ed Sheeran vs. not Ed Sheeran. This new programming paradigm has created so much hype because it has allowed us to create programs that were previously impossible with the traditional programming paradigm: examples include voice recognition, object recognition, autonomous driving etc. In particular, computers now can be taught / programmed to perform tasks that traditionally could only be done by humans and thereby replace human jobs - just go to any Amazon warehouse to witness for yourself. Note that people too often conflate the execution (inference) of ML programs and the training of ML programs. This distinction is quite important, especially for understanding concepts such as AI DApps. While the process of writing an ML program is different from writing a traditional program, at runtime, an ML program behaves more or less just like a traditional program in that given an input, it gives you an output. In the next article, I will go into further details about how exactly a machine can learn via statistics and probability. Stay tuned! Footnotes: - Some nuances are left out for the sake of readability. Please leave a comment if you find certain parts confusing. - For the purpose of this article ML model = ML program; ML inference = ML execution; writing an ML program = training an ML program)

Machine Learning: A High Level Overview

The decision to welcome the help of outside investors is never an easy one, so it seems unfair that after most businesses make the decision of turning to they then have to embark on an arduous path towards gaining a windfall. venture capital (The path to success acquiring venture capital can be a long and winding one - but ultimately rewarding too. Image: FitSmallBusiness ) The last thing investors want is to lose money and this means that forensic levels of research are required to go into the analysis of a business idea before they agree to part with their cash. Because of this, it can be profoundly difficult to build an interest in your endeavour - especially if you’re seen as an inexperienced entrepreneur. Developing a level of appeal for your business ideas and ambitions is essential in , but this can be a tricky process for founders. Luckily, there’s plenty of schools of thought on how entrepreneurs can build an attractive proposition to investors. Let’s take a look at six significant steps you can take to make your business more attractive to venture capital firms: attaining venture capital Get your financials in check You may have faith in the quality of your USP and general business model, but it’s impossible to assume this faith will be shared by anyone else. Ensuring the certification of your numbers and processes from a reputable accounting firm will pay dividends in building a level of trust between your figures and the investor reading them. Furthermore, this approach will provide you with an insight into the true value of your company and reaffirm your proposed offers and . counter-offers to potential lenders Having officially backed up numbers will save you time, cost and may even give you the chance to increase your available leverage. Be sure to get your business audited before propositioning venture capital firms. The length of the audit doesn’t have to be long, but ideally the more years you’re accountable for, the more chance you have of winning your prospective investor’s trust. Networking may pay dividends There are very few areas of life where it doesn’t benefit you to conduct a little bit of networking, and in the world of acquiring venture capital, it’s no different. It’s rarely ‘what you know,’ but ‘who you know,’ as the saying goes - and in an entrepreneurial world, it’s great to conduct a little bit of networking in order to make a soft sell of your pitch in a more relaxing environment. to engage in, it makes for an excellent tactic in more ways than one. For example, it would be great if you ran into the perfect investor who loved your informal pitch enough to declare their interest in your business - but it would also be an excellent experience if you received some constructive criticism of your soft sell prior to wasting time and money on a pitch that would’ve otherwise been picked apart. While this approach would require a little bit of self-confidence Optimise your margins It’s likely that we’ve all been in a position in our lives where we feel the need to squeeze every penny, and this has never been more pertinent than when formulating a cash flow forecast in a bid to attract venture capital. The Catch 22 here is that most newer businesses need to spend money to keep afloat - and the likely reason that you’re even looking for the support of an external investor is that you need a windfall to match your spending ambition. But it’s vital that you demonstrate your ability to maintain careful financial control of your endeavour, and show it to venture capital firms in a way that highlights your aversion to superfluous spending. Seek out venture capital endeavours that cater to your business (Image showing demographics of U.S. venture capitalists. Image: Statista ) Different venture capital firms cater to different areas in the industry. For example, some boast a portfolio centered around , while others could be focusing on specific countries. remote AR One such company is , which focuses (but not limits) its investments on Armenian entrepreneurs around the globe. Its founder, Alexander Smbatyan said: “Armenian entrepreneurs and professionals have successfully created some of the most advanced technologies either by starting their own companies and/or working in other companies around the world. We are not limiting the fund people within Armenia, this would be shortsighted and irrational. Armenians live all over the world and they are proud of their culture and don’t want to lose their identity. Potentially this creates a huge global pool of entrepreneurs, professionals, capital, companies and knowledge which can be leveraged and scaled in any of the world's economies.” Aybuben Ventures This illustrates the diversity of potential investors out there who could be interested in providing for your business to flourish. With proper research and preparation, businesses in various niches with can find a suitable investor. different backgrounds Formal pitches can be time-consuming work and expensive, so it really pays to take the time to conduct some research into the venture capital firms that may have a significant interest in investing in your idea. Ambition is good, but missing budgets could be fatal Of course, you have faith in your business, no entrepreneur would be very successful if they didn’t believe in their own endeavour. But it’s important to not overreach when budgeting prior to seeking funds. The most vital part of to a potential investor hinges on establishing a level of trust and confidence in your vision. If your company evidently fails to hit its financial targets then it will inevitably plant seeds of doubt into the minds of your venture capital firm’s decision-makers. pitching your business On the flip side, if you beat your numbers, the chances are that investors would move faster to close a deal through fear of losing the transaction. Practice patience Finally, remember to practice patience. Attaining capital from external sources is almost always a drawn-out process. Fundraising usually lasts around six months but there’s always a chance it will take longer - particularly if your endeavour is new and yet to gain a track record of hitting its targets. It could be tempting to make concessions in order to close on your windfall faster, but be sure to carefully select your desired figures and equity and set limits. The chances are that in a few years down the line you’ll be reaping the rewards of your more measured approach.

How to Boost Your High Level Business Objectives to Appeal to VCs

What is Spark? Let’s take a look under the hood In we introduced a problem: copious, never ending streams of data, and it’s solution: Apache Spark. Here in Part II we’ll focus on Spark’s internal architecture and data structures, and in Part III we’ll focus more on Spark’s available APIs and Functions in Java. my last post In pioneer days they used oxen for heavy pulling, and when one ox couldn’t budge a log, they didn’t try to grow a larger ox. We shouldn’t be trying for bigger computers, but for more systems of computers — Grace Hopper With the scale of data growing at a rapid and ominous pace, we needed a way to process potential petabytes of data quickly, and we simply couldn’t make a single computer process that amount of data at a reasonable pace. This problem is solved by creating a cluster of machines to perform the work for you, but how do those machines work together to solve the common problem? Meet Spark Photo by on Jez Timms Unsplash Spark is the cluster computing framework for large-scale data processing. Spark offers a set of libraries in 3 languages (Java, Scala, Python) for its unified computing engine. What does this definition actually mean? Unified: With Spark, there is no need to piece together an application out of multiple APIs or systems. Spark provides you with enough built-in APIs to get the job done Computing Engine: Spark handles loading data from various file systems and runs computations on it, but does not store any data itself permanently. Spark operates entirely in memory — allowing unparalleled performance and speed Libraries: Spark is comprised of a series of libraries built for data science tasks. Spark includes libraries for SQL (SparkSQL), Machine Learning (MLlib), Stream Processing (Spark Streaming and Structured Streaming), and Graph Analytics (GraphX) The Spark Application Every Spark Application consists of a and a set of distributed worker processes ( ). Driver Executors Spark Driver The Driver runs the main() method of our application and is where the SparkContext is created. The Spark Driver has the following duties: Runs on a node in our cluster, or on a client, and schedules the job execution with a cluster manager Responds to user’s program or input Analyzes, schedules, and distributes work across the exectors Stores metadata about the running application and conveniently exposes it in a webUI Spark Executors An executor is a distributed process responsible for the execution of tasks. Each Spark Application has its own set of executors, which stay alive for the life cycle of a single Spark application. Executors perform all data processing of a Spark job Stores results in memory, only persisting to disk when specifically instructed by the driver program Returns results to the driver once they have been completed Each node can have anywhere from 1 executor per node to 1 executor per core Spark’s Application Workflow When you submit a job to Spark for processing, there is a lot that goes on behind the scenes. Our Standalone Application is kicked off, and initializes its SparkContext. Only after having a SparkContext can an app be referred to as a Driver Our Driver program asks the Cluster Manager for resources to launch its executors The Cluster Manager launches the executors Our Driver runs our actual Spark code Executors run tasks and send their results back to the driver SparkContext is stopped and all executors are shut down, returning resources back to the cluster MaxTemperature, Revisited Lets take a deeper look at the Spark Job we wrote in to find max temperature by country. This abstraction hid a lot of set-up code, including the initialization of our SparkContext, lets fill in the gaps: Part I MaxTemperature Spark Setup Remember that Spark is a framework, in this case implemented in Java. It isn’t until line 16 that Spark needs to do any work at all. Sure, we initialized our SparkContext, however loading data into an RDD is the first bit of code that requires work be sent to our executors. By now you may have seen the term “RDD” appear multiple times, it’s about time we define it. Spark Architecture Overview Spark has a well-defined layered architecture with loosely coupled components based on two primary abstractions: Resilient Distributed Datasets (RDDs) Directed Acyclic Graph (DAG) Resilient Distributed Datasets RDDs are essentially the building blocks of Spark: everything is comprised of them. Even Sparks higher-level APIs (DataFrames, Datasets) are composed of RDDs under the hood. What does it mean to be a Resilient Distributed Dataset? Resilient: Since Spark runs on a cluster of machines, data-loss from hardware failure is a very real concern, so RDDs are fault tolerant and can rebuild themselves in the event of failure Distributed: A single RDD is stored on a series of different nodes in the cluster, belonging to no single source (and no single point of failure). This way our cluster can operate on our RDD in parallel Dataset: A collection of values — this one you should probably have known already All data we work with in Spark will be stored inside some form of RDD — it is therefore imperative to fully understand them. Spark offers a slew of “Higher Level” APIs built on top of RDDs designed to abstract away complexity, namely the DataFrame and Dataset. With a strong focus on Read-Evaluate-Print-Loops (REPLs), Spark-Submit and the Spark-Shell in Scala and Python are targeted toward Data Scientists, who often desire repeat analysis on a dataset. The RDD is still imperative to understand, as it’s the underlying structure of all data in Spark. An RDD is colloquially equivalent to: “Distributed Data Structure”. A JavaRDD<String> is essentially just a List<String> dispersed amongst each node in our cluster, with each node getting several different chunks of our List. With Spark, we need to think in a distributed context, always. RDDs work by splitting up their data into a series of partitions to be stored on each executor node. Each node will then perform its work only on its own partitions. This is what makes Spark so powerful: If an executor dies or a task fails Spark can rebuild just the partitions it needs from the original source and re-submit the task for completion. Spark RDD partitioned amongst executors RDD Operations RDDs are Immutable, meaning that once they are created, they cannot be altered in any way, they can only be transformed. The notion of transforming RDDs is at the core of Spark, and Spark Jobs can be thought of as nothing more than any combination of these steps: Loading data into an RDD Transforming an RDD Performing an Action on an RDD In fact, every Spark job I’ve written is comprised of exclusively those types of tasks, with vanilla Java for flavour. Spark defines a set of APIs for working with RDDs that can be broken down into two large groups: and . Transformations Actions Transformations create a new RDD from an existing one. Actions return a value, or values, to the Driver program after running a computation on its RDD. For example, the function weatherData.map() is a transformation that passes each element of an RDD through a function. map is an RDD action that aggregates all the elements of an RDD using some function and returns the final result to the driver program. Reduce Lazy Evaluation “I choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it. — Bill Gates” All transformations in Spark are lazy. This means that when we tell Spark to create an RDD via transformations of an existing RDD, it won’t generate that dataset until a specific action is performed on it or one of it's children. Spark will then perform the transformation and the action that triggered it. This allows Spark to run much more efficiently. Let’s re-examine the function declarations from our earlier Spark example to identify which functions are actions and which are transformations: 16: JavaRDD<String> weatherData = sc.textFile(inputPath); Line 16 is neither an action or a transformation; it’s a function of , our . sc JavaSparkContext 17: JavaPairRDD<String, Integer> tempsByCountry = weatherData.mapToPair(new Func..... Line 17 is a of the RDD, in it we map each line of to a pair comprised of (City, Temperature) transformation weatherData weatherData 26: JavaPairRDD<String, Integer> maxTempByCountry = tempsByCountry.reduce(new Func.... Line 26 is also a because we are iterating over key-value pairs. its a transformation of in which we reduce each city to its highest recorded temperature. transformation tempsByCountry 31: maxTempByCountry.saveAsHadoopFile(destPath, String.class, Integer.class, TextOutputFormat.class); Finally on line 31 we trigger a Spark : saving our RDD to our file system. Since Spark subscribes to the lazy execution model, it isn’t until this line that Spark generates , , and before finally saving our result. action weatherData tempsByCountry maxTempsByCountry Directed Acyclic Graph Whenever an action is performed on an RDD, Spark creates a DAG, a finite direct graph with no directed cycles (otherwise our job would run forever). Remember that a graph is nothing more than a series of connected vertices and edges, and this graph is no different. Each vertex in the DAG is a Spark function, some operation performed on an RDD (map, mapToPair, reduceByKey, etc). In MapReduce, the DAG consists of two vertices: Map → Reduce. In our above example of MaxTemperatureByCountry, the DAG is a little more involved: parallelize → map → mapToPair → reduce → saveAsHadoopFile The DAG allows Spark to optimize its execution plan and minimize shuffling. We’ll discuss the DAG in greater depth in later posts, as it’s outside the scope of this Spark overview. Evaluation Loops With our new vocabulary, let us re-examine the problem with MapReduce as I defined in , quoted below: Part I MapReduce excels at batch data processing, however it lags behind when it comes to repeat analysis and small feedback loops. The only way to reuse data between computations is to write it to an external storage system (a la HDFS)” ‘Re-use data between computations’? Sounds like an RDD that can have multiple actions performed on it! Lets suppose we have a file “data.txt” and want to accomplish two computations: Total length of all lines in the file Length of the longest line in the file In MapReduce, each task would require a separate job or a fancy MulitpleOutputFormat implementation. Spark makes this a breeze in just four simple steps: Load contents of into an RDD data.txt JavaRDD<String> lines = sc.textFile("data.txt"); 2. Map each line of ‘ ’ to its length lines (Lambda functions used for brevity) JavaRDD<Integer> lineLengths = lines.map(s -> s.length()); 3. To solve for total length: reduce to find the total line length sum, in this case the sum of every element in the RDD lineLengths int totalLength = lineLengths.reduce((a, b) -> a + b); 4. To solve for longest length: reduce to find the maximum line length lineLengths int maxLength = lineLengths.reduce((a, b) -> Math.max(a,b)); Note that steps 3 and 4 are RDD actions, so they return a result to our Driver program, in this case a Java int. Also recall that Spark is lazy and refuses to do any work until it sees an action, in this case it will not begin any real work until step 3. Next Steps So far we’ve introduced our data problem and its solution: Apache Spark. We reviewed Spark’s architecture and workflow, it’s flagship internal abstraction (RDD), and its execution model. Next we’ll look into Functions and Syntax in Java, getting progressively more technical as we dive deeper into the framework.

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