When learning Apache Spark, where should a person begin? What are the key fundamentals when learning Apache Spark? Resilient Distributed Datasets, Spark Drivers and Context, Transformations, Actions.
This lecture was intended to provide an introduction to Apache Spark's features and functionality and importance of Spark as a distributed data processing framework compared to Hadoop MapReduce. The target audience was MSc students with programming skills at beginner to intermediate level.
Introduction to Apache Spark Workshop at Lambda World 2015 on October 23th and 24th, 2015, celebrated in Cádiz. Speakers: @fperezp and @juanpedromoreno
Github Repo: https://github.com/47deg/spark-workshop
Apache Spark is rapidly emerging as the prime platform for advanced analytics in Hadoop. This briefing is updated to reflect news and announcements as of July 2014.
My presentation on Java User Group BD Meet up # 5.0 (JUGBD#5.0)
Apache Spark™ is a fast and general engine for large-scale data processing.Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Big Data Processing with Apache Spark 2014mahchiev
Apache Spark™ is a fast and general engine for large-scale data processing. It has gained enormous popularity recently with its speed and ease of use and is currently replacing traditional Hadoop MapReduce. We'll talk about:
1. What is Big Data ?
2. The Map-Reduce paradigm
3. What does Apache Spark do?
4. Finally, we'll make a quick demo
This lecture was intended to provide an introduction to Apache Spark's features and functionality and importance of Spark as a distributed data processing framework compared to Hadoop MapReduce. The target audience was MSc students with programming skills at beginner to intermediate level.
Introduction to Apache Spark Workshop at Lambda World 2015 on October 23th and 24th, 2015, celebrated in Cádiz. Speakers: @fperezp and @juanpedromoreno
Github Repo: https://github.com/47deg/spark-workshop
Apache Spark is rapidly emerging as the prime platform for advanced analytics in Hadoop. This briefing is updated to reflect news and announcements as of July 2014.
My presentation on Java User Group BD Meet up # 5.0 (JUGBD#5.0)
Apache Spark™ is a fast and general engine for large-scale data processing.Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Big Data Processing with Apache Spark 2014mahchiev
Apache Spark™ is a fast and general engine for large-scale data processing. It has gained enormous popularity recently with its speed and ease of use and is currently replacing traditional Hadoop MapReduce. We'll talk about:
1. What is Big Data ?
2. The Map-Reduce paradigm
3. What does Apache Spark do?
4. Finally, we'll make a quick demo
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
Apache spark on Hadoop Yarn Resource Managerharidasnss
How we can configure the spark on apache hadoop environment, and why we need that compared to standalone cluster manager.
Slide also includes docker based demo to play with the hadoop and spark on your laptop itself. See more on the demo codes and other documentation here - https://github.com/haridas/hadoop-env
Story of architecture evolution of one project from zero to Lambda Architecture. Also includes information on how we scaled cluster as soon as architecture is set up.
Contains nice performance charts after every architecture change.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Introducing Hadoop for bigdata, and demo the hadoop cluster setup on your machine using docker based hadoop. This setup can be used for quick prototyping or trying any of the hadoop tools locally.
Check this repo for more details about the demo part - https://github.com/haridas/hadoop-env
Big Data visualization with Apache Spark and Zeppelinprajods
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
How we can make use of Kubernetes as Resource Manager for Spark. What are the Pros and Cons of Spark Resource manager are discussed on this slides and the associated tutorial.
Refer this github project for more details and code samples : https://github.com/haridas/hadoop-env
Building a REST API with Cassandra on Datastax Astra Using Python and NodeAnant Corporation
DataStax Astra provides the ability to develop and deploy data-driven applications with a cloud-native service, without the hassles of database and infrastructure administration. In this webinar, we are going to walk you through creating a REST API and exposing that to your Cassandra database.
Webinar Link: https://www.youtube.com/watch?v=O64pJa3eLqs&t=20s
http://www.agildata.com/agildata-hosts-big-data-meetup-featuring-apache-spark/
Slides for talks given at the Denver Java Users Group, Boulder Java Users Group, Denver/Boulder Big Data Users Group.
Dan and Andy will spend an evening rolling up our sleeves with you to try out some real-world use cases for Apache Spark.
We’ll cover Spark’s RDD API, the DataFrame API, as well as the brand new Dataset API.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
Apache spark on Hadoop Yarn Resource Managerharidasnss
How we can configure the spark on apache hadoop environment, and why we need that compared to standalone cluster manager.
Slide also includes docker based demo to play with the hadoop and spark on your laptop itself. See more on the demo codes and other documentation here - https://github.com/haridas/hadoop-env
Story of architecture evolution of one project from zero to Lambda Architecture. Also includes information on how we scaled cluster as soon as architecture is set up.
Contains nice performance charts after every architecture change.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Introducing Hadoop for bigdata, and demo the hadoop cluster setup on your machine using docker based hadoop. This setup can be used for quick prototyping or trying any of the hadoop tools locally.
Check this repo for more details about the demo part - https://github.com/haridas/hadoop-env
Big Data visualization with Apache Spark and Zeppelinprajods
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
How we can make use of Kubernetes as Resource Manager for Spark. What are the Pros and Cons of Spark Resource manager are discussed on this slides and the associated tutorial.
Refer this github project for more details and code samples : https://github.com/haridas/hadoop-env
Building a REST API with Cassandra on Datastax Astra Using Python and NodeAnant Corporation
DataStax Astra provides the ability to develop and deploy data-driven applications with a cloud-native service, without the hassles of database and infrastructure administration. In this webinar, we are going to walk you through creating a REST API and exposing that to your Cassandra database.
Webinar Link: https://www.youtube.com/watch?v=O64pJa3eLqs&t=20s
http://www.agildata.com/agildata-hosts-big-data-meetup-featuring-apache-spark/
Slides for talks given at the Denver Java Users Group, Boulder Java Users Group, Denver/Boulder Big Data Users Group.
Dan and Andy will spend an evening rolling up our sleeves with you to try out some real-world use cases for Apache Spark.
We’ll cover Spark’s RDD API, the DataFrame API, as well as the brand new Dataset API.
In this webinar, we'll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
Introduction to Big Data Analytics using Apache Spark and Zeppelin on HDInsig...Alex Zeltov
Introduction to Big Data Analytics using Apache Spark on HDInsights on Azure (SaaS) and/or HDP on Azure(PaaS)
This workshop will provide an introduction to Big Data Analytics using Apache Spark using the HDInsights on Azure (SaaS) and/or HDP deployment on Azure(PaaS) . There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Spark, the ultra-fast, general purpose big data computing platform provides some very flexible options for processing and accessing data. In a previous meetup we covered PySpark and the Schema RDD. In this session we reviewed and expanded on this, with an in-depth exploration of Spark SQL.
- Overview of Spark in the Hadoop ecosystem
- Deep dive into Spark SQL with step by steps on how to implement and use it
If you have questions about the presentation or want to learn more about our services, please visit our website: http://casertaconcepts.com/
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.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
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.
Workshop - How to Build Recommendation Engine using Spark 1.6 and HDP
Hands-on - Build a Data analytics application using SPARK, Hortonworks, and Zeppelin. The session explains RDD concepts, DataFrames, sqlContext, use SparkSQL for working with DataFrames and explore graphical abilities of Zeppelin.
b) Follow along - Build a Recommendation Engine - This will show how to build a predictive analytics (MLlib) recommendation engine with scoring This will give a better understanding of architecture and coding in Spark for ML.
Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop : Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products such as Oracle Big Data SQL on the Oracle Big Data Appliance along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we'll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete "data fabric" solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
3. Assumptions
• You want to learn Apache Spark, but need to know where
to begin
• You need to know the fundamentals of Spark in order to
progress in your learning of Spark
• You need to evaluate if Spark could be an appropriate fit
for your use cases or career growth
One or more of the following
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4. In a nutshell, why spark?
• Engine for efficient large-scale processing. It’s faster than
Hadoop MapReduce
• Spark can complement your existing Hadoop investments
such as HDFS and Hive
• Rich ecosystem including support for SQL, Machine
Learning, Steaming and multiple language APIs such as
Scala, Python and Java
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6. Spark Essentials
• Resilient Distributed Datasets (RDD)
• Transformers
• Actions
• Spark Driver Programs and SparkContext
To begin, you need to know:
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7. Resilient Distributed Datasets (RDDs)
• RDDs are Spark’s primary abstraction for data
interaction (lazy, in memory)
• RDDs are an immutable, distributed collection of
elements separated into partitions
• There are multiple types of RDDs
• RDDs can be created from an external data sets such as
Hadoop InputFormats, text files on a variety of file
systems or existing RDDs via a Spark Transformations
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8. Transformations
• RDD functions which return pointers to new RDDs
(remember: lazy)
• map, flatMap, filter, etc.
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9. Actions
• RDD functions which return values to the driver
• reduce, collect, count, etc.
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11. Spark Driver Programs and Context
• Spark driver is a program that declares transformations and
actions on RDDs of data
• A driver submits the serialized RDD graph to the master
where the master creates tasks. These tasks are delegated to
the workers for execution.
• Workers are where the tasks are actually executed.
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12. Driver Program and SparkContext
Image borrowed from http://spark.apache.org/docs/latest/cluster-overview.html
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13. References
• For course information and discount coupons, visit http://
www.supergloo.com/
• Learning Spark Book Summary http://www.amazon.com/
Learning-Spark-Summary-Lightning-Fast-Deconstructed-
ebook/dp/B019HS7USA/
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