This document provides an overview of Apache Spark and compares it to Hadoop MapReduce. It defines big data and explains that Spark is a solution for processing large datasets in parallel. Spark improves on MapReduce by allowing in-memory computation using Resilient Distributed Datasets (RDDs) which makes it faster, especially for iterative jobs. Spark is also easier to program with rich APIs. While MapReduce is tolerant, Spark caching improves performance. Both are widely used but Spark sees more adoption for real-time applications due to its speed.
A comprehensive overview on the entire Hadoop operations and tools: cluster management, coordination, injection, streaming, formats, storage, resources, processing, workflow, analysis, search and visualization
Advance Map reduce - Apache hadoop Bigdata training by Design PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the Advance Map reduce concepts of Hadoop and Big Data.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Assessing Graph Solutions for Apache SparkDatabricks
Users have several options for running graph algorithms with Apache Spark. To support a graph data architecture on top of its linear-oriented DataFrames, the Spark platform offers GraphFrames. However, due to the fact that GraphFrames are immutable and not a native graph, there are cases where it might not offer the features or performance needed for certain use cases. Another option is to connect Spark to a real-time, scalable and distributed native graph database such as TigerGraph.
In this session, we compare three options — GraphX, Cypher for Apache Spark, and TigerGraph — for different types of workload requirements and data sizes, to help users select the right solution for their needs. We also look at the data transfer and loading time for TigerGraph.
A comprehensive overview on the entire Hadoop operations and tools: cluster management, coordination, injection, streaming, formats, storage, resources, processing, workflow, analysis, search and visualization
Advance Map reduce - Apache hadoop Bigdata training by Design PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the Advance Map reduce concepts of Hadoop and Big Data.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Assessing Graph Solutions for Apache SparkDatabricks
Users have several options for running graph algorithms with Apache Spark. To support a graph data architecture on top of its linear-oriented DataFrames, the Spark platform offers GraphFrames. However, due to the fact that GraphFrames are immutable and not a native graph, there are cases where it might not offer the features or performance needed for certain use cases. Another option is to connect Spark to a real-time, scalable and distributed native graph database such as TigerGraph.
In this session, we compare three options — GraphX, Cypher for Apache Spark, and TigerGraph — for different types of workload requirements and data sizes, to help users select the right solution for their needs. We also look at the data transfer and loading time for TigerGraph.
Proud to be Polyglot - Riviera Dev 2015Tugdual Grall
New developers and teams are now polyglot :
- they use multiple programming languages (Java, Javascript, Ruby, ...)
- they use multiple persistence store (RDBMS, NoSQL, Hadoop)
In this talk you will learn about the benefits if being polyglot: use the good language or framework for the good cause, select the good persistence for specific constraints.
This presentation will show how developer could mix the Python, NodeJS, AngularJS, SQL with Drill for Hadoop and MongoDB.
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in ProductionCodemotion
What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.
Geospatial Analytics at Scale with Deep Learning and Apache Spark with Tim hu...Databricks
Deep Learning is now the standard in object detection, but it is not easy to analyze large amounts of images, especially in an interactive fashion. Traditionally, there has been a gap between Deep Learning frameworks, which excel at image processing, and more traditional ETL and data science tools, which are usually not designed to handle huge batches of complex data types such as images.
In this talk, we show how manipulating large corpora of images can be accomplished in a few lines of code because of recent developments in Apache Spark. Thanks to Spark’s unique ability to blend different libraries, we show how to start from satellite images and rapidly build complex queries on high level information such as houses or buildings. This is possible thanks to Magellan, a geospatial package, and Deep Learning Pipelines, a library that streamlines the integration of Deep Learning frameworks in Spark. At the end of this session, you will walk away with the confidence that you can solve your own image detection problems at any scale thanks to the power of Spark.
Approximation algorithms for stream and batch processingGabriele Modena
At Improve Digital (http://www.improvedigital.com) we collect and process large amounts of machine generated and behavioral data. Our systems address a variety of use cases that involve both batch and streaming technologies. One common denominator of the overall architecture is the need to share models and workflows across both worlds. Another one is that the analysis of large amounts of data often requires trade-offs; for instance trading accuracy for timeliness in streaming applications. One approach to satisfy these constraints is to make "big data" small. In this talk we will review a number of approximation methods for sketching, summarization and clustering and discuss how they are starting to change the way we think about certain types of analytics, and how they are being integrated into our data pipelines.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
Distributed Stream Processing - Spark Summit East 2017Petr Zapletal
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples.
A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.
Lions and tigers and Spark, oh my! It's hard enough keeping up with the explosion in data, but just keeping track of the tools is a challenge. What is Big Data? How do I become a data scientist? How can I leverage the cloud? (What is the cloud?). These are all tough questions for anyone to answer, let alone the business analyst who does not have a strong programming and technology background. Put your mind at ease - we are here to help.
This talk will introduce the open source processing engine, Spark, highlighting not only it's awesome power but how it fits within the larger data landscape. You will learn why Spark was developed to crunch through Big Data, what MapReduce is, and why Spark can beat the pants off of it in terms of performance and ease of use. You will learn how non-programmers can get started with Spark (warning: there is no escaping code, but you can do it, we promise), where you can find great tutorials on Spark, and how you can use Spark in the cloud with IBM.
At the end of this talk you will be able to firmly place Spark in the Big Data ecosystem and articulate to your colleagues how data processing platforms have evolved to handle large amounts of data. You will know how to get started using Spark and be comfortable enough with Spark syntax to write a few lines of code like the boss you are. This talk will be fast and furious, but fun. Fasten your seatbelts and get ready to learn about Spark.
Hadoop is a Java software framework that supports data-intensive distributed applications and is developed under open source license. It enables applications to work with thousands of nodes and petabytes of data.
Key attributes for modern real time streaming processing and interactive analytics
What is so exciting to me about Spark?
What are some of the myths?
What is missing in Spark for real time?
SnappyData’s mission – fuse Spark with in-memory data management in one unified cluster to offer – OLTP + OLAP + Stream processing + Probabilistic data
Apache Cassandra and Python for Analyzing Streaming Big Data prajods
This presentation was made at the Open Source India Conference Nov 2015. It explains how Apache Spark, pySpark, Cassandra, Node.js and D3.js can be used for creating a platform for visualizing and analyzing streaming big data
2014.02.13 (Strata) Graph Analysis with One Trillion Edges on Apache GiraphAvery Ching
(Abstract from Strata talk)
http://strataconf.com/strata2014/public/schedule/detail/32137
Graph analytics have applications beyond large web scale organizations. Many computing problems can be efficiently expressed and processed as a graph and can lead to useful insights that drive product and business decisions
While you can express graph algorithms as SQL queries in Hive or Hadoop MapReduce programs, an API designed specifically for graph processing makes writing many iterative graph computations (such as page rank, connected components, label propagation, graph-based clustering, etc.) easy to express in simpler and easier to understand code. Apache Giraph provides such a native graph processing API, runs on existing Hadoop infrastructure and can directly access HDFS and/or Hive tables.
This talk describes our efforts at Facebook to scale Apache Giraph to very large graphs of up to one trillion edges and how we run Apache Giraph in production. We will also talk about several algorithms that we have implemented and their use cases.
Apache Spark vs rest of the world – Problems and Solutions by Arkadiusz Jachn...Big Data Spain
Apache Spark is a great solution for building Big Data applications. It provides really fast SQL-like processing, machine learning library, and streaming module for near real time processing of data streams. Unfortunately, during application development and production deployments we often encounter many difficulties in mixing various data sources or bulk loading of computed data to SQL or NoSQL databases
https://www.bigdataspain.org/2017/talk/apache-spark-vs-rest-of-the-world-problems-and-solutions
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
I gave this talk at Buzzwords just now to fill in for an ill speaker.
The topics include things that are being added to or taken out of Mahout. These include cruft (out), fast clustering (in), nearest neighbor search (in), Pig bindings for Mahout (who knows).
Proud to be Polyglot - Riviera Dev 2015Tugdual Grall
New developers and teams are now polyglot :
- they use multiple programming languages (Java, Javascript, Ruby, ...)
- they use multiple persistence store (RDBMS, NoSQL, Hadoop)
In this talk you will learn about the benefits if being polyglot: use the good language or framework for the good cause, select the good persistence for specific constraints.
This presentation will show how developer could mix the Python, NodeJS, AngularJS, SQL with Drill for Hadoop and MongoDB.
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in ProductionCodemotion
What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.
Geospatial Analytics at Scale with Deep Learning and Apache Spark with Tim hu...Databricks
Deep Learning is now the standard in object detection, but it is not easy to analyze large amounts of images, especially in an interactive fashion. Traditionally, there has been a gap between Deep Learning frameworks, which excel at image processing, and more traditional ETL and data science tools, which are usually not designed to handle huge batches of complex data types such as images.
In this talk, we show how manipulating large corpora of images can be accomplished in a few lines of code because of recent developments in Apache Spark. Thanks to Spark’s unique ability to blend different libraries, we show how to start from satellite images and rapidly build complex queries on high level information such as houses or buildings. This is possible thanks to Magellan, a geospatial package, and Deep Learning Pipelines, a library that streamlines the integration of Deep Learning frameworks in Spark. At the end of this session, you will walk away with the confidence that you can solve your own image detection problems at any scale thanks to the power of Spark.
Approximation algorithms for stream and batch processingGabriele Modena
At Improve Digital (http://www.improvedigital.com) we collect and process large amounts of machine generated and behavioral data. Our systems address a variety of use cases that involve both batch and streaming technologies. One common denominator of the overall architecture is the need to share models and workflows across both worlds. Another one is that the analysis of large amounts of data often requires trade-offs; for instance trading accuracy for timeliness in streaming applications. One approach to satisfy these constraints is to make "big data" small. In this talk we will review a number of approximation methods for sketching, summarization and clustering and discuss how they are starting to change the way we think about certain types of analytics, and how they are being integrated into our data pipelines.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
Distributed Stream Processing - Spark Summit East 2017Petr Zapletal
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples.
A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.
Lions and tigers and Spark, oh my! It's hard enough keeping up with the explosion in data, but just keeping track of the tools is a challenge. What is Big Data? How do I become a data scientist? How can I leverage the cloud? (What is the cloud?). These are all tough questions for anyone to answer, let alone the business analyst who does not have a strong programming and technology background. Put your mind at ease - we are here to help.
This talk will introduce the open source processing engine, Spark, highlighting not only it's awesome power but how it fits within the larger data landscape. You will learn why Spark was developed to crunch through Big Data, what MapReduce is, and why Spark can beat the pants off of it in terms of performance and ease of use. You will learn how non-programmers can get started with Spark (warning: there is no escaping code, but you can do it, we promise), where you can find great tutorials on Spark, and how you can use Spark in the cloud with IBM.
At the end of this talk you will be able to firmly place Spark in the Big Data ecosystem and articulate to your colleagues how data processing platforms have evolved to handle large amounts of data. You will know how to get started using Spark and be comfortable enough with Spark syntax to write a few lines of code like the boss you are. This talk will be fast and furious, but fun. Fasten your seatbelts and get ready to learn about Spark.
Hadoop is a Java software framework that supports data-intensive distributed applications and is developed under open source license. It enables applications to work with thousands of nodes and petabytes of data.
Key attributes for modern real time streaming processing and interactive analytics
What is so exciting to me about Spark?
What are some of the myths?
What is missing in Spark for real time?
SnappyData’s mission – fuse Spark with in-memory data management in one unified cluster to offer – OLTP + OLAP + Stream processing + Probabilistic data
Apache Cassandra and Python for Analyzing Streaming Big Data prajods
This presentation was made at the Open Source India Conference Nov 2015. It explains how Apache Spark, pySpark, Cassandra, Node.js and D3.js can be used for creating a platform for visualizing and analyzing streaming big data
2014.02.13 (Strata) Graph Analysis with One Trillion Edges on Apache GiraphAvery Ching
(Abstract from Strata talk)
http://strataconf.com/strata2014/public/schedule/detail/32137
Graph analytics have applications beyond large web scale organizations. Many computing problems can be efficiently expressed and processed as a graph and can lead to useful insights that drive product and business decisions
While you can express graph algorithms as SQL queries in Hive or Hadoop MapReduce programs, an API designed specifically for graph processing makes writing many iterative graph computations (such as page rank, connected components, label propagation, graph-based clustering, etc.) easy to express in simpler and easier to understand code. Apache Giraph provides such a native graph processing API, runs on existing Hadoop infrastructure and can directly access HDFS and/or Hive tables.
This talk describes our efforts at Facebook to scale Apache Giraph to very large graphs of up to one trillion edges and how we run Apache Giraph in production. We will also talk about several algorithms that we have implemented and their use cases.
Apache Spark vs rest of the world – Problems and Solutions by Arkadiusz Jachn...Big Data Spain
Apache Spark is a great solution for building Big Data applications. It provides really fast SQL-like processing, machine learning library, and streaming module for near real time processing of data streams. Unfortunately, during application development and production deployments we often encounter many difficulties in mixing various data sources or bulk loading of computed data to SQL or NoSQL databases
https://www.bigdataspain.org/2017/talk/apache-spark-vs-rest-of-the-world-problems-and-solutions
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
I gave this talk at Buzzwords just now to fill in for an ill speaker.
The topics include things that are being added to or taken out of Mahout. These include cruft (out), fast clustering (in), nearest neighbor search (in), Pig bindings for Mahout (who knows).
This presentation attempts to summarize content the speaker thinks is important for Nigerian Developers to take their apps to the next level. It contains a summary of specific Android sessions delivered at Google I/O 2016 and was presented at the Google I/O Extended 16 event in Lagos, Nigeria.
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis.
With Dask and Numba, you can NumPy-like and Pandas-like code and have it run very fast on multi-core systems as well as at scale on many-node clusters.
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
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A performance analysis of OpenStack Cloud vs Real System on Hadoop ClustersKumari Surabhi
It introduces the performance analysis of OpenStack Cloud with the commodity computers in the big data environments. It concludes that the data storage and analysis in hadoop cluster in cloud is more flexible and easily scalable than the real system cluster. It also concludes the cluster in commodities computers are faster than the cloud clusters.
a comprehensive good introduction to the the Big data world in AWS cloud, hadoop, Streaming, batch, Kinesis, DynamoDB, Hbase, EMR, Athena, Hive, Spark, Piq, Impala, Oozie, Data pipeline, Security , Cost, Best practices
[@NaukriEngineering] Introduction to Android ONaukri.com
The presentation provides an introduction to Android O esp. from the standpoint of changes that developers have to incorporate to migrate their apps to it.
[@NaukriEngineering] Introduction to Galera clusterNaukri.com
This presentation talks about the advantages of Galera Cluster over traditional MySQL M-M replication. It highlights key features of Galera Cluster like synchronous replication, seamless scalability even for write operations, automatic membership control etc. It gives a brief about the prerequisites for migrating to Galera Cluster from MySQL M-M replication.
[@NaukriEngineering] BDD implementation using CucumberNaukri.com
BDD is a software development methodology in which an application is specified and designed by describing how its behavior should appear to an outside observer. It can easily be implemented using Cucumber and Java. Cucumber is a software tool that runs automated acceptance tests written in BDD format.
A presentation on implementing feature toggles in an efficient way esp. when we have large number of toggles such that they do not create technical debt for us.
[@NaukriEngineering] Mobile Web app scripts execution using AppiumNaukri.com
Testers used to run mobile site automation scripts on windows browsers which is not a real time mobile browser. To get the real time ROI on automation scripts of mobile site, it is necessary that automation code should be executed in real time mobile browser. Appium is the tool which eases the execution of automated test scripts on real chrome browser on android phones. The presentation throws some light on appium architecture and how it helps in executing the mobile web app test scripts on real time android chrome browser.
Internet companies with huge traffic and millions of users have tasks involved that cannot be served in a request. RabbitMQ can process tasks or communication between different app components asynchronously but close to real time.
This presentation gives a brief understanding of docker architecture, explains what docker is not, followed by a description of basic commands and explains CD/CI as an application of docker.
[@NaukriEngineering] Git Basic Commands and HacksNaukri.com
This presentation is not to explain why we use Git and the benefits of using Git over SVN.
But, it is about how to use the simplest and basic functionalities of Git. And small hacks to make our lives easy.
IndexedDB is an HTML5 API that allows us to store/retrieve large amount of data on user’s browser. It does not have any storage limit, and is hence better than other browser storages.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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2. Agenda
● What is Big Data?
● What is the solution of Big data?
● How Apache Spark can help us?
● Apache Spark advantages over Hadoop MapReduce
3. What is Big Data?
● Lots of Data (Terabytes or Petabytes).
● Large and complex.
● Difficult to deal using Relational Databases.
● Challenges faced in - searching, storing, transfer, analysis, visualisation.
● Require Parallel processing on 100s of machines.
4. Hadoop MapReduce
● Allows distributed processing of large datasets across clusters.
● It is open source database management with scale out storage and distributed
processing.
● Characteristics:
○ Economical
○ Scalable
○ Reliable
○ Flexible
5. MapReduce
● Map - Data is converted into tuples (key/value pair).
● Reduce - Takes input from map and combines input from map to form smaller set of
tuples.
● Advantages
○ Scale data
○ Parallel Processing
○ Fast
○ Built in fault tolerant
7. Shortcomings of MapReduce
1. Slow for Iterative Jobs.
2. Slow for Interactive Ad-hoc queries.
3. Operations - Forces task be of type Map and Reduce.
4. Difficult to program - Even simple join operations also require extensive code.
Lacks data sharing. Data sharing done through stable storage (HDFS)→ slow.
Slow due to replication and Disk I/O but it is essential for fault tolerance.
Can we use memory? How will it be fault tolerant?
8. Apache Spark
● Developed in 2009 by UC Berkeley.
● Processing engine.
● Used for speed, ease of use, and sophisticated analytics.
● It is based on Hadoop MapReduce but it extends MapReduce for performing
more types of computations.
● Spark participated in Daytona Gray category, Spark sorted 100 TB of data (1
trillion records) the same data three time faster using ten times fewer
machines as compared to Hadoop.
9. Apache Spark
● Improves efficiency through
○ In-memory data sharing.
○ General computation graph.
● Improves usability through
○ Rich APIs in Java, Scala, Python.
○ Interactive Shell.
HOW ??
Upto 100x faster in memory
and 10x faster on disk
Upto 2-5x less code
10. Resilient Distributed Dataset (RDD)
● Fundamental Data Structure of Apache Spark.
● Read-only collection of objects partitioned across a set of machines.
● Perform In-memory Computation.
● Build on transformation operations like map, filter etc.
● Fault tolerant through lineage.
● Features:
○ Immutable
○ Parallel
○ Cacheable
○ Lazy Evaluated
11. Resilient Distributed Dataset (RDD)
Two types of operation can be performed:
● Transformation
○ Create new RDD from existing RDD.
○ Creates DAG.
○ Lazily evaluated.
○ Increases efficiency by not returning large dataset.
○ Eg. GroupByKey, ReduceByKey, filter.
● Action
○ All queries are executed.
○ Performs computation.
○ Returns result to driver program.
○ Eg. collect, count, take.
16. Creating RDD
# Creates a list of animal.
animals = ['cat', 'dog', 'elephant', 'cat', 'mouse', ’cat’]
# Parallelize method is used to create RDD from list. Here “animalRDD” is created.
#sc is Object of Spark Context.
animalRDD = sc.parallelize(animals)
# Since RDD is lazily evaluated, to print it we perform an action operation, i.e.
collect() which is used to print the RDD.
print animalRDD.collect()
Output - ['cat', 'dog', 'elephant', 'cat', 'mouse', 'cat']
17. Creating RDD from file
#The file words.txt has names of animals through which animalsRDD is made.
animalsRDD = sc.textFile('/path/to/file/words.txt')
#collect() is the action operation.
print animalsRDD.collect()
18. Map operation on RDD
‘’’’’ To count the frequency of animals, we make (key/value) pair - (animal,1) for all
the animals and then perform reduce operation which counts all the values.
Lambda is used to write inline functions in python.
‘’’’’
mapRDD = animalRDD.map(lambda x:(x,1))
print mapRDD.collect()
Output - [('cat',1), ('dog',1), ('elephant',1), ('cat',1), ('mouse',1), ('cat',1)]
19. Reduce operation on RDD
‘’’’’ reduceByKey is used to perform reduce operation on same key. So in its
arguments, we have defined a function to add the values for same key. Hence, we
get the count of animals.
‘’’’’
reduceRDD = mapRDD.reduceByKey(lambda x,y:x+y)
print reduceRDD.collect()
Output - [('cat',3), ('dog',1), ('elephant',1), ('mouse',1)]
20. Filter operation on RDD
‘’’’’ Filter all the animals obtained from reducedRDD with count greater than 2. x is
a tuple made of (animal, count), i.e. x[0]=animal name and x[1]=count of animal.
Therefore we filter the reduceRDD based on x[1]>2.
‘’’’’
filterRDD = reduceRDD.filter(lambda x:x[1]>2)
print filterRDD.collect()
Output - [('cat',3)]
24. Spark vs. Hadoop
● Performance
○ Spark better as it does in-memory computation.
○ Hadoop is good for one pass ETL jobs and where data does not fit in memory.
● Ease of use
○ Spark is easier to program and provides API in Java, Scala, R, Python.
○ Spark has an interactive mode.
○ Hadoop MapReduce is more difficult to program but many tools are available to
make it easier.
● Cost
○ Spark is cost effective according to benchmark, though staffing can be costly.
● Compatibility
○ Compatibility to data types and data sources is the same for both.
25. Spark vs. Hadoop
● Data Processing
○ Spark can perform real time processing and batch processing.
○ Hadoop MapReduce is good for batch processing. Hadoop requires storm for real
time processing, Giraph for graph processing, Mahout for machine learning.
● Fault tolerant
○ Hadoop MapReduce is slightly more tolerant.
● Caching
○ Spark can cache the input data.
26. Applications
Companies that uses Hadoop and Spark are:
● Hadoop - Hadoop is used good for static operation.
○ Dell, IBM, Cloudera, AWS and many more.
● Spark
○ Real-time marketing campaign, online product recommendations etc.
○ eBay, Amazon, Yahoo, Nokia and many more.
○ Data mining 40x times faster than Hadoop (Conviva).
○ Traffic Prediction via EM (Mobile Millennium).
○ DNA Sequence Analysis (SNAP).
○ Twitter Spam Classification (Monarch).
27. Apache Spark helping companies grow in
their business
● Spark Helps Pinterest Identify Trends - Using Spark, Pinterest is able to
identify—and react to—developing trends as they happen.
● Netflix Leans on Spark for Personalization Aid - Netflix uses Spark to support
real-time stream processing for online recommendations and data monitoring.
28. Libraries of Apache Spark
Spark provides libraries to provide generality. We can combine these libraries
seamlessly in the same application to provide more functionality.
Libraries provided by Apache Spark are:
1. Spark Streaming - It supports scalable and fault tolerant processing of
streaming data.
2. Spark SQL - It allows spark to work with structured data.
3. Spark MLlib - It provides scalable machine learning library and has machine
learning and statistical algorithms.
4. Spark GraphX - It is used to compute graphs over data.
Refer http://spark.apache.org/docs/latest/ for more information.