This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
The document discusses YARN (Yet Another Resource Negotiator), which is the cluster resource management layer of Hadoop. It describes the limitations of the previous Hadoop 1.0 architecture where MapReduce was responsible for both data processing and resource management. YARN was created to address these limitations by separating resource management from data processing. It discusses the components of YARN including the Resource Manager, Node Manager, Containers, and Application Master. It also provides examples of workloads that can run on YARN beyond MapReduce and describes the YARN architecture and how applications run on the YARN framework.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
This document provides an overview of a talk on Apache Spark. It introduces the speaker and their background. It acknowledges inspiration from a previous Spark training. It then outlines the structure of the talk, which will include: a brief history of big data; a tour of Spark including its advantages over MapReduce; and explanations of Spark concepts like RDDs, transformations, and actions. The document serves to introduce the topics that will be covered in the talk.
Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
This document provides an introduction and overview of Apache Spark with Python (PySpark). It discusses key Spark concepts like RDDs, DataFrames, Spark SQL, Spark Streaming, GraphX, and MLlib. It includes code examples demonstrating how to work with data using PySpark for each of these concepts.
This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
This presentations is first in the series of Apache Spark tutorials and covers the basics of Spark framework.Subscribe to my youtube channel for more updates https://www.youtube.com/channel/UCNCbLAXe716V2B7TEsiWcoA
Apache Spark is an open-source distributed processing engine that is up to 100 times faster than Hadoop for processing data stored in memory and 10 times faster for data stored on disk. It provides high-level APIs in Java, Scala, Python and SQL and supports batch processing, streaming, and machine learning. Spark runs on Hadoop, Mesos, Kubernetes or standalone and can access diverse data sources using its core abstraction called resilient distributed datasets (RDDs).
- Apache Spark is an open-source cluster computing framework that is faster than Hadoop for batch processing and also supports real-time stream processing.
- Spark was created to be faster than Hadoop for interactive queries and iterative algorithms by keeping data in-memory when possible.
- Spark consists of Spark Core for the basic RDD API and also includes modules for SQL, streaming, machine learning, and graph processing. It can run on several cluster managers including YARN and Mesos.
This document provides an overview of Apache Spark, including its history, features, architecture and use cases. Spark started in 2009 at UC Berkeley and was later adopted by the Apache Foundation. It provides faster processing than Hadoop by keeping data in memory. Spark supports batch, streaming and interactive processing on large datasets using its core abstraction called resilient distributed datasets (RDDs).
Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It extends the MapReduce model of Hadoop to efficiently use it for more types of computations, which includes interactive queries and stream processing.
Spark is one of Hadoop's subproject developed in 2009 in UC Berkeley's AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top-level Apache project from Feb-2014.
This document shares some basic knowledge about Apache Spark.
Getting Started with Apache Spark (Scala)Knoldus Inc.
In this session, we are going to cover Apache Spark, the architecture of Apache Spark, Data Lineage, Direct Acyclic Graph(DAG), and many more concepts. Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
An Engine to process big data in faster(than MR), easy and extremely scalable way. An Open Source, parallel, in-memory processing, cluster computing framework. Solution for loading, processing and end to end analyzing large scale data. Iterative and Interactive : Scala, Java, Python, R and with Command line interface.
Cassandra and SparkSQL: You Don't Need Functional Programming for Fun with Ru...Databricks
Did you know almost every feature of the Spark Cassandra connector can be accessed without even a single Monad! In this talk I’ll demonstrate how you can take advantage of Spark on Cassandra using only the SQL you already know! Learn how to register tables, ETL data, and analyze query plans all from the comfort of your very own JDBC Client. Find out how you can access Cassandra with ease from the BI tool of your choice and take your analysis to the next level. Discover the tricks of debugging and analyzing predicate pushdowns using the Spark SQL Thrift Server. Preview the latest developments of the Spark Cassandra Connector.
Streaming Big Data with Spark, Kafka, Cassandra, Akka & Scala (from webinar)Helena Edelson
This document provides an overview of streaming big data with Spark, Kafka, Cassandra, Akka, and Scala. It discusses delivering meaning in near-real time at high velocity and an overview of Spark Streaming, Kafka and Akka. It also covers Cassandra and the Spark Cassandra Connector as well as integration in big data applications. The presentation is given by Helena Edelson, a Spark Cassandra Connector committer and Akka contributor who is a Scala and big data conference speaker working as a senior software engineer at DataStax.
The document provides an overview of Apache Spark, including what it is, its ecosystem, features, and architecture. Some key points:
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It is up to 100x faster than Hadoop for iterative/interactive algorithms.
- Spark features include its RDD abstraction, lazy evaluation, and use of DAGs to optimize performance. It supports Scala, Java, Python, and R.
- The Spark ecosystem includes tools like Spark SQL, MLlib, GraphX, and Spark Streaming. It can run on Hadoop YARN, Mesos, or in standalone mode.
- Spark's architecture includes the SparkContext,
Unit II Real Time Data Processing tools.pptxRahul Borate
Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. It overcomes limitations of Hadoop by running 100 times faster in memory and 10 times faster on disk. Spark uses resilient distributed datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for faster processing.
Jumpstart on Apache Spark 2.2 on DatabricksDatabricks
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
Jump Start on Apache® Spark™ 2.x with Databricks Databricks
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
Using pySpark with Google Colab & Spark 3.0 previewMario Cartia
Spark is an open source engine for large-scale data processing. It provides APIs in multiple languages including Python. Spark includes libraries for SQL, streaming, machine learning and runs on single machines or clusters. The document discusses Spark's architecture, RDD API, transformations and actions, shuffle operations, persistence, and structured APIs like DataFrames and SQL.
This document is a presentation on Apache Spark that compares its performance to MapReduce. It discusses how Spark is faster than MapReduce, provides code examples of performing word counts in both Spark and MapReduce, and explains features that make Spark suitable for big data analytics such as simplifying data analysis, providing built-in machine learning and graph libraries, and speaking multiple languages. It also lists many large companies that use Spark for applications like recommendations, business intelligence, and fraud detection.
This document discusses Apache Spark, an open-source cluster computing framework. It summarizes that Spark allows for in-memory processing to reduce I/O, is optimized for speed, can operate both in-memory and on disk, supports streaming data and machine learning algorithms, integrates DataFrames and graphs, and can leverage Hadoop for resource management. Major companies like IBM, Cloudera and eBay use Spark for applications like recommendations, business intelligence, and data analytics.
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Is Email Marketing Really Effective In 2024?Rakesh Jalan
Slide 1
Is Email Marketing Really Effective in 2024?
Yes, Email Marketing is still a great method for direct marketing.
Slide 2
In this article we will cover:
- What is Email Marketing?
- Pros and cons of Email Marketing.
- Tools available for Email Marketing.
- Ways to make Email Marketing effective.
Slide 3
What Is Email Marketing?
Using email to contact customers is called Email Marketing. It's a quiet and effective communication method. Mastering it can significantly boost business. In digital marketing, two long-term assets are your website and your email list. Social media apps may change, but your website and email list remain constant.
Slide 4
Types of Email Marketing:
1. Welcome Emails
2. Information Emails
3. Transactional Emails
4. Newsletter Emails
5. Lead Nurturing Emails
6. Sponsorship Emails
7. Sales Letter Emails
8. Re-Engagement Emails
9. Brand Story Emails
10. Review Request Emails
Slide 5
Advantages Of Email Marketing
1. Cost-Effective: Cheaper than other methods.
2. Easy: Simple to learn and use.
3. Targeted Audience: Reach your exact audience.
4. Detailed Messages: Convey clear, detailed messages.
5. Non-Disturbing: Less intrusive than social media.
6. Non-Irritating: Customers are less likely to get annoyed.
7. Long Format: Use detailed text, photos, and videos.
8. Easy to Unsubscribe: Customers can easily opt out.
9. Easy Tracking: Track delivery, open rates, and clicks.
10. Professional: Seen as more professional; customers read carefully.
Slide 6
Disadvantages Of Email Marketing:
1. Irrelevant Emails: Costs can rise with irrelevant emails.
2. Poor Content: Boring emails can lead to disengagement.
3. Easy Unsubscribe: Customers can easily leave your list.
Slide 7
Email Marketing Tools
Choosing a good tool involves considering:
1. Deliverability: Email delivery rate.
2. Inbox Placement: Reaching inbox, not spam or promotions.
3. Ease of Use: Simplicity of use.
4. Cost: Affordability.
5. List Maintenance: Keeping the list clean.
6. Features: Regular features like Broadcast and Sequence.
7. Automation: Better with automation.
Slide 8
Top 5 Email Marketing Tools:
1. ConvertKit
2. Get Response
3. Mailchimp
4. Active Campaign
5. Aweber
Slide 9
Email Marketing Strategy
To get good results, consider:
1. Build your own list.
2. Never buy leads.
3. Respect your customers.
4. Always provide value.
5. Don’t email just to sell.
6. Write heartfelt emails.
7. Stick to a schedule.
8. Use photos and videos.
9. Segment your list.
10. Personalize emails.
11. Ensure mobile-friendliness.
12. Optimize timing.
13. Keep designs clean.
14. Remove cold leads.
Slide 10
Uses of Email Marketing:
1. Affiliate Marketing
2. Blogging
3. Customer Relationship Management (CRM)
4. Newsletter Circulation
5. Transaction Notifications
6. Information Dissemination
7. Gathering Feedback
8. Selling Courses
9. Selling Products/Services
Read Full Article:
https://digitalsamaaj.com/is-email-marketing-effective-in-2024/
How to Store Data on the Odoo 17 WebsiteCeline George
Here we are going to discuss how to store data in Odoo 17 Website.
It includes defining a model with few fields in it. Add demo data into the model using data directory. Also using a controller, pass the values into the template while rendering it and display the values in the website.
Webinar Innovative assessments for SOcial Emotional SkillsEduSkills OECD
Presentations by Adriano Linzarini and Daniel Catarino da Silva of the OECD Rethinking Assessment of Social and Emotional Skills project from the OECD webinar "Innovations in measuring social and emotional skills and what AI will bring next" on 5 July 2024
Understanding and Interpreting Teachers’ TPACK for Teaching Multimodalities i...Neny Isharyanti
Presented as a plenary session in iTELL 2024 in Salatiga on 4 July 2024.
The plenary focuses on understanding and intepreting relevant TPACK competence for teachers to be adept in teaching multimodality in the digital age. It juxtaposes the results of research on multimodality with its contextual implementation in the teaching of English subject in the Indonesian Emancipated Curriculum.
The Jewish Trinity : Sabbath,Shekinah and Sanctuary 4.pdfJackieSparrow3
we may assume that God created the cosmos to be his great temple, in which he rested after his creative work. Nevertheless, his special revelatory presence did not fill the entire earth yet, since it was his intention that his human vice-regent, whom he installed in the garden sanctuary, would extend worldwide the boundaries of that sanctuary and of God’s presence. Adam, of course, disobeyed this mandate, so that humanity no longer enjoyed God’s presence in the little localized garden. Consequently, the entire earth became infected with sin and idolatry in a way it had not been previously before the fall, while yet in its still imperfect newly created state. Therefore, the various expressions about God being unable to inhabit earthly structures are best understood, at least in part, by realizing that the old order and sanctuary have been tainted with sin and must be cleansed and recreated before God’s Shekinah presence, formerly limited to heaven and the holy of holies, can dwell universally throughout creation
How to Install Theme in the Odoo 17 ERPCeline George
With Odoo, we can select from a wide selection of attractive themes. Many excellent ones are free to use, while some require payment. Putting an Odoo theme in the Odoo module directory on our server, downloading the theme, and then installing it is a simple process.
Beginner's Guide to Bypassing Falco Container Runtime Security in Kubernetes ...anjaliinfosec
This presentation, crafted for the Kubernetes Village at BSides Bangalore 2024, delves into the essentials of bypassing Falco, a leading container runtime security solution in Kubernetes. Tailored for beginners, it covers fundamental concepts, practical techniques, and real-world examples to help you understand and navigate Falco's security mechanisms effectively. Ideal for developers, security professionals, and tech enthusiasts eager to enhance their expertise in Kubernetes security and container runtime defenses.
Views in Odoo - Advanced Views - Pivot View in Odoo 17Celine George
In Odoo, the pivot view is a graphical representation of data that allows users to analyze and summarize large datasets quickly. It's a powerful tool for generating insights from your business data.
The pivot view in Odoo is a valuable tool for analyzing and summarizing large datasets, helping you gain insights into your business operations.
Ardra Nakshatra (आर्द्रा): Understanding its Effects and RemediesAstro Pathshala
Ardra Nakshatra, the sixth Nakshatra in Vedic astrology, spans from 6°40' to 20° in the Gemini zodiac sign. Governed by Rahu, the north lunar node, Ardra translates to "the moist one" or "the star of sorrow." Symbolized by a teardrop, it represents the transformational power of storms, bringing both destruction and renewal.
About Astro Pathshala
Astro Pathshala is a renowned astrology institute offering comprehensive astrology courses and personalized astrological consultations for over 20 years. Founded by Gurudev Sunil Vashist ji, Astro Pathshala has been a beacon of knowledge and guidance in the field of Vedic astrology. With a team of experienced astrologers, the institute provides in-depth courses that cover various aspects of astrology, including Nakshatras, planetary influences, and remedies. Whether you are a beginner seeking to learn astrology or someone looking for expert astrological advice, Astro Pathshala is dedicated to helping you navigate life's challenges and unlock your full potential through the ancient wisdom of Vedic astrology.
For more information about their courses and consultations, visit Astro Pathshala.
(T.L.E.) Agriculture: Essentials of GardeningMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏.𝟎)-𝐅𝐢𝐧𝐚𝐥𝐬
Lesson Outcome:
-Students will understand the basics of gardening, including the importance of soil, water, and sunlight for plant growth. They will learn to identify and use essential gardening tools, plant seeds, and seedlings properly, and manage common garden pests using eco-friendly methods.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial | Simplilearn
2. What’s in it for you?
1. History of Spark
What’s in it for you?
3. What’s in it for you?
1. History of Spark
2. What is Spark?
What’s in it for you?
4. What’s in it for you?
1. History of Spark
2. What is Spark?
3. Hadoop vs Spark
What’s in it for you?
5. What’s in it for you?
1. History of Spark
2. What is Spark?
3. Hadoop vs Spark
4. Components of Apache Spark
What’s in it for you?
Spark Core
Spark SQL
Spark Streaming
Spark MLlib
GraphX
6. What’s in it for you?
1. History of Spark
2. What is Spark?
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark Architecture
What’s in it for you?
7. What’s in it for you?
1. History of Spark
2. What is Spark?
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark Architecture
6. Applications of Spark
What’s in it for you?
8. What’s in it for you?
1. History of Spark
2. What is Spark?
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark Architecture
6. Applications of Spark
7. Spark Use Case
What’s in it for you?
9. History of Apache Spark
Started as a project at UC
Berkley AMPLab
2009
10. History of Apache Spark
Started as a project at UC
Berkley AMPLab
Open sourced under a
BSD license
2009
2010
11. History of Apache Spark
Started as a project at UC
Berkley AMPLab
Open sourced under a
BSD license
Spark became an Apache top
level project
2009
2010
2013
12. History of Apache Spark
Started as a project at UC
Berkley AMPLab
Open sourced under a
BSD license
Spark became an Apache top
level project
Used by Databricks to sort
large-scale datasets and set a
new world record
2009
2010
2013
2014
14. What is Apache Spark?
Apache Spark is an open-source data processing engine to store and process data in
real-time across various clusters of computers using simple programming constructs
15. What is Apache Spark?
Support various programming languages
Apache Spark is an open-source data processing engine to store and process data in
real-time across various clusters of computers using simple programming constructs
16. What is Apache Spark?
Support various programming languages Developers and data scientists incorporate
Spark into their applications to rapidly
query, analyze, and transform data at
scale
Query Analyze Transform
Apache Spark is an open-source data processing engine to store and process data in
real-time across various clusters of computers using simple programming constructs
18. Hadoop vs Spark
Processing data using MapReduce in Hadoop is slow
Spark processes data 100 times faster than MapReduce as it is done in-
memory
19. Hadoop vs Spark
Processing data using MapReduce in Hadoop is slow
Spark processes data 100 times faster than MapReduce as it is done in-
memory
Performs batch processing of data Performs both batch processing and real-time processing of data
20. Hadoop vs Spark
Processing data using MapReduce in Hadoop is slow
Spark processes data 100 times faster than MapReduce as it is done in-
memory
Performs batch processing of data Performs both batch processing and real-time processing of data
Hadoop has more lines of code. Since it is written in Java, it takes
more time to execute
Spark has fewer lines of code as it is implemented in Scala
21. Hadoop vs Spark
Processing data using MapReduce in Hadoop is slow
Spark processes data 100 times faster than MapReduce as it is done in-
memory
Performs batch processing of data Performs both batch processing and real-time processing of data
Hadoop has more lines of code. Since it is written in Java, it takes
more time to execute
Spark has fewer lines of code as it is implemented in Scala
Hadoop supports Kerberos authentication, which is difficult to manage Spark supports authentication via a shared secret. It can also
run on YARN leveraging the capability of Kerberos
23. Spark Features
Fast processing
Spark contains Resilient Distributed
Datasets (RDD) which saves time
taken in reading, and writing
operations and hence, it runs almost
ten to hundred times faster than
Hadoop
25. Spark Features
Flexible
Spark supports multiple languages
and allows the developers to write
applications in Java, Scala, R, or
Python
In-memory
computingFast processing
26. Spark Features
Fault tolerance
Spark contains Resilient Distributed
Datasets (RDD) that are designed to
handle the failure of any worker
node in the cluster. Thus, it ensures
that the loss of data reduces to zero
Flexible
In-memory
computingFast processing
27. Spark Features
Better analytics
Spark has a rich set of SQL queries,
machine learning algorithms,
complex analytics, etc. With all these
functionalities, analytics can be
performed better
Fault toleranceFlexible
In-memory
computingFast processing
35. Spark Core
Spark Core
Spark Core is the base engine for large-scale parallel and distributed
data processing
36. Spark Core
Spark Core
Spark Core is the base engine for large-scale parallel and distributed
data processing
It is responsible for:
memory management fault recovery
scheduling, distributing and
monitoring jobs on a cluster
interacting with storage
systems
37. Resilient Distributed Dataset
Spark Core
Spark Core is embedded with RDDs (Resilient Distributed Datasets), an
immutable fault-tolerant, distributed collection of objects that can be operated on
in parallel
RDD
Transformation Action
These are operations (such as reduce,
first, count) that return
a value after running a computation on
an RDD
These are operations (such as map, filter,
join, union) that are performed on an RDD
that yields a new RDD containing the
result
39. Spark SQL
Spark SQL framework component is used for structured and semi-structured data
processing
Spark SQL
SQL
40. Spark SQL
Spark SQL framework component is used for structured and semi-structured data
processing
Spark SQL
SQL
DataFrame DSL Spark SQL and HQL
DataFrame API
Data Source API
CSV JSON JDBC
Spark SQL Architecture
42. Spark Streaming
Spark Streaming is a lightweight API that allows developers to perform batch
processing and real-time streaming of data with ease
Spark
Streaming
Streaming
Provides secure, reliable, and fast processing of live data
streams
43. Spark Streaming
Spark Streaming is a lightweight API that allows developers to perform batch
processing and real-time streaming of data with ease
Spark
Streaming
Streaming
Provides secure, reliable, and fast processing of live data
streams
Streaming Engine
Input data
stream
Batches of
input data
Batches of
processed
data
45. Spark MLlib
MLlib is a low-level machine learning library that is simple to use,
is scalable, and compatible with various programming languages
MLlib
MLlib
MLlib eases the deployment and development of
scalable machine learning algorithms
46. Spark MLlib
MLlib is a low-level machine learning library that is simple to use,
is scalable, and compatible with various programming languages
MLlib
MLlib
MLlib eases the deployment and development of
scalable machine learning algorithms
It contains machine learning libraries that have an
implementation of various machine learning algorithms
Clustering Classification Collaborative
Filtering
49. GraphX
GraphX is Spark’s own Graph Computation Engine and data store
GraphX
Provides a uniform tool for ETL Exploratory data analysis
Interactive graph computations
51. Master Node
Driver Program
SparkContext
• Master Node has a Driver Program
• The Spark code behaves as a driver
program and creates a SparkContext,
which is a gateway to all the Spark
functionalities
Apache Spark uses a master-slave architecture that consists of a driver, that runs on a
master node, and multiple executors which run across the worker nodes in the cluster
Spark Architecture
52. Master Node
Driver Program
SparkContext Cluster Manager
• Spark applications run as independent
sets of processes
on a cluster
• The driver program & Spark context
takes care of the job execution within
the cluster
Spark Architecture
53. Master Node
Driver Program
SparkContext Cluster Manager
Cache
Task Task
Executor
Worker Node
Cache
Task Task
Executor
Worker Node
• A job is split into multiple tasks that are
distributed over the worker node
• When an RDD is created in Spark
context, it can be distributed across
various nodes
• Worker nodes are slaves that run
different tasks
Spark Architecture
54. Master Node
Driver Program
SparkContext Cluster Manager
Cache
Task Task
Executor
Worker Node
Cache
Task Task
Executor
Worker Node
• The Executor is responsible for the
execution of these tasks
• Worker nodes execute the tasks
assigned by the Cluster Manager and
return the results back to the
SparkContext
Spark Architecture
55. Spark Cluster Managers
Standalone mode
1
By default, applications
submitted to the
standalone mode cluster
will run in FIFO order,
and each application will
try to use all available
nodes
56. Spark Cluster Managers
Standalone mode
1 2
By default, applications
submitted to the
standalone mode cluster
will run in FIFO order,
and each application will
try to use all available
nodes
Apache Mesos is an
open-source project to
manage computer
clusters, and can also run
Hadoop applications
57. Spark Cluster Managers
Standalone mode
1 2 3
By default, applications
submitted to the
standalone mode cluster
will run in FIFO order,
and each application will
try to use all available
nodes
Apache Mesos is an
open-source project to
manage computer
clusters, and can also run
Hadoop applications
Apache YARN is the
cluster resource manager
of Hadoop 2. Spark can
be run on YARN
58. Spark Cluster Managers
Standalone mode
1 2 3 4
By default, applications
submitted to the
standalone mode cluster
will run in FIFO order,
and each application will
try to use all available
nodes
Apache Mesos is an
open-source project to
manage computer
clusters, and can also run
Hadoop applications
Apache YARN is the
cluster resource manager
of Hadoop 2. Spark can
be run on YARN
Kubernetes is an open-
source system for
automating deployment,
scaling, and management
of containerized
applications
60. Applications of Spark
Banking
JPMorgan uses Spark to detect
fraudulent transactions, analyze the
business spends of an individual to
suggest offers, and identify patterns
to decide how much to invest and
where to invest
61. Applications of Spark
Banking E-Commerce
JPMorgan uses Spark to detect
fraudulent transactions, analyze the
business spends of an individual to
suggest offers, and identify patterns
to decide how much to invest and
where to invest
Alibaba uses Spark to analyze large
sets of data such as real-time
transaction details, browsing history,
etc. in the form of Spark jobs and
provides recommendations to its users
62. Applications of Spark
Banking E-Commerce Healthcare
JPMorgan uses Spark to detect
fraudulent transactions, analyze the
business spends of an individual to
suggest offers, and identify patterns
to decide how much to invest and
where to invest
Alibaba uses Spark to analyze large
sets of data such as real-time
transaction details, browsing history,
etc. in the form of Spark jobs and
provides recommendations to its users
IQVIA is a leading healthcare company
that uses Spark to analyze patient’s
data, identify possible health issues,
and diagnose it based on their medical
history
63. Applications of Spark
Banking E-Commerce Healthcare Entertainment
JPMorgan uses Spark to detect
fraudulent transactions, analyze the
business spends of an individual to
suggest offers, and identify patterns
to decide how much to invest and
where to invest
Alibaba uses Spark to analyze large
sets of data such as real-time
transaction details, browsing history,
etc. in the form of Spark jobs and
provides recommendations to its users
IQVIA is a leading healthcare company
that uses Spark to analyze patient’s
data, identify possible health issues,
and diagnose it based on their medical
history
Entertainment and gaming companies
like Netflix and Riot games use
Apache Spark to showcase relevant
advertisements to their users based on
the videos that they watch, share, and
like
66. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Video streaming is a challenge, especially with
increasing demand for high-quality streaming
experiences
67. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Video streaming is a challenge, especially with
increasing demand for high-quality streaming
experiences
Conviva collects data about video streaming
quality to give their customers visibility into the end-
user experience they are delivering
68. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva delivers a better
quality of service to its customers by removing the
screen buffering and learning in detail about the
network conditions in real-time
69. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva delivers a better
quality of service to its customers by removing the
screen buffering and learning in detail about the
network conditions in real-time
This information is stored in the video player to
manage live video traffic coming from 4 billion video
feeds every month, to ensure maximum retention
70. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva has
created an auto diagnostics alert
71. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva has
created an auto diagnostics alert
It automatically detects anomalies
along the video streaming pipeline and
diagnoses the root cause of the issue
72. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva has
created an auto diagnostics alert
It automatically detects anomalies
along the video streaming pipeline and
diagnoses the root cause of the issue
Reduces waiting time before the
video starts
73. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva has
created an auto diagnostics alert
It automatically detects anomalies
along the video streaming pipeline and
diagnoses the root cause of the issue
Reduces waiting time before the
video starts
Avoids buffering and recovers the
video from a technical error
74. Spark Use Case
Conviva is one of the world’s leading video streaming companies
Using Apache Spark, Conviva has
created an auto diagnostics alert
It automatically detects anomalies
along the video streaming pipeline and
diagnoses the root cause of the issue
Reduces waiting time before the
video starts
Avoids buffering and recovers the
video from a technical error
Goal is to maximize the viewer
engagement