This document compares MapReduce and Spark frameworks. It discusses their histories and basic functionalities. MapReduce uses input, map, shuffle, and reduce stages, while Spark uses RDDs (Resilient Distributed Datasets) and transformations and actions. Spark is easier to program than MapReduce due to its interactive mode, but MapReduce has more supporting tools. Performance benchmarks show Spark is faster than MapReduce for sorting. The hardware and developer costs of Spark are also lower than MapReduce.
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.
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
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
Using pySpark with Google Colab & Spark 3.0 previewMario Cartia
Apache Spark is the Big Data opensource framework used by the world's leading companies for the implementation of advanced analytics. The talk will introduce the architecture, the modules and the main functionalities of the framework showing some practical examples in Python that do not require the installation of any software on your machine using the Colab tool made available by Google.
The talk will also introduce the new features available on the preview version of the upcoming release 3.0
Introduction to Apache Spark Developer TrainingCloudera, Inc.
Apache Spark is a next-generation processing engine optimized for speed, ease of use, and advanced analytics well beyond batch. The Spark framework supports streaming data and complex, iterative algorithms, enabling applications to run 100x faster than traditional MapReduce programs. With Spark, developers can write sophisticated parallel applications for faster business decisions and better user outcomes, applied to a wide variety of architectures and industries.
Learn What Apache Spark is and how it compares to Hadoop MapReduce, How to filter, map, reduce, and save Resilient Distributed Datasets (RDDs), Who is best suited to attend the course and what prior knowledge you should have, and the benefits of building Spark applications as part of an enterprise data hub.
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.
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
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
Using pySpark with Google Colab & Spark 3.0 previewMario Cartia
Apache Spark is the Big Data opensource framework used by the world's leading companies for the implementation of advanced analytics. The talk will introduce the architecture, the modules and the main functionalities of the framework showing some practical examples in Python that do not require the installation of any software on your machine using the Colab tool made available by Google.
The talk will also introduce the new features available on the preview version of the upcoming release 3.0
Introduction to Apache Spark Developer TrainingCloudera, Inc.
Apache Spark is a next-generation processing engine optimized for speed, ease of use, and advanced analytics well beyond batch. The Spark framework supports streaming data and complex, iterative algorithms, enabling applications to run 100x faster than traditional MapReduce programs. With Spark, developers can write sophisticated parallel applications for faster business decisions and better user outcomes, applied to a wide variety of architectures and industries.
Learn What Apache Spark is and how it compares to Hadoop MapReduce, How to filter, map, reduce, and save Resilient Distributed Datasets (RDDs), Who is best suited to attend the course and what prior knowledge you should have, and the benefits of building Spark applications as part of an enterprise data hub.
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
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
This slide deck is used as an introduction to the internals of Apache Spark, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
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.
Data Quality With or Without Apache Spark and Its EcosystemDatabricks
Few solutions exist in the open-source community either in the form of libraries or complete stand-alone platforms, which can be used to assure a certain data quality, especially when continuous imports happen. Organisations may consider picking up one of the available options – Apache Griffin, Deequ, DDQ and Great Expectations. In this presentation we’ll compare these different open-source products across different dimensions, like maturity, documentation, extensibility, features like data profiling and anomaly detection.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
Parallelizing Existing R Packages with SparkRDatabricks
R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. With the release of Spark 2.0, the R API officially supports executing user code on distributed data. This is done through a family of apply() functions. In this talk, Hossein Falaki gives an overview of this new functionality in SparkR. Using this API requires some changes to regular code with dapply(). This talk will focus on how to correctly use this API to parallelize existing R packages. Most important topics of consideration will be performance and correctness when using the apply family of functions in SparkR.
Speaker: Hossein Falaki
This talk was originally presented at Spark Summit East 2017.
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
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
This slide deck is used as an introduction to the internals of Apache Spark, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
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.
Data Quality With or Without Apache Spark and Its EcosystemDatabricks
Few solutions exist in the open-source community either in the form of libraries or complete stand-alone platforms, which can be used to assure a certain data quality, especially when continuous imports happen. Organisations may consider picking up one of the available options – Apache Griffin, Deequ, DDQ and Great Expectations. In this presentation we’ll compare these different open-source products across different dimensions, like maturity, documentation, extensibility, features like data profiling and anomaly detection.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
Parallelizing Existing R Packages with SparkRDatabricks
R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. With the release of Spark 2.0, the R API officially supports executing user code on distributed data. This is done through a family of apply() functions. In this talk, Hossein Falaki gives an overview of this new functionality in SparkR. Using this API requires some changes to regular code with dapply(). This talk will focus on how to correctly use this API to parallelize existing R packages. Most important topics of consideration will be performance and correctness when using the apply family of functions in SparkR.
Speaker: Hossein Falaki
This talk was originally presented at Spark Summit East 2017.
Back in summer of 2014, we launched the results of a survey on Java 8, which shared a lot of information we were looking for, but also contained a small golden nugget of data that we didn’t expect: that out of more than 3000 developers surveyed, a shocking 17% of them reported using Apache Spark in production.
So we did another survey with 2100+ respondents drilling down into what developers, data scientists, executives and organizations are looking forward to with Apache Spark. You can download the full version of the report for the whole story, but here is a sneak peak into the findings that we discovered.
The full version is at: http://typesafe.com/blog/apache-spark-preparing-for-the-next-wave-of-reactive-big-data
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Last year, in Apache Spark 2.0, Databricks introduced Structured Streaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0, Databricks has been hard at work building first-class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality, in addition to the existing connectivity of Spark SQL, makes it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse, or arriving in real-time from Kafka/Kinesis.
In this session, Das will walk through a concrete example where – in less than 10 lines – you read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. He’ll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
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
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Apache Spark is all the rage these days. People who work with Big Data, Spark is a household name for them. We have been using it for quite some time now. So we already know that Spark is lightning-fast cluster computing technology, it is faster than Hadoop MapReduce.
If you ask any of these Spark techies, how Spark is fast, they would give you a vague answer by saying Spark uses DAG to carry out the in-memory computations.
So, how far is this answer satisfiable?
Well to a Spark expert, this answer is just equivalent to a poison.
Let’s try to understand how exactly spark is handling our computations through DAG.
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. It’s exactly what a Cassandra cluster needs to deliver real-time, ad-hoc querying of operational data at scale.
In this talk, we’ll explore Spark and see how it works together with Cassandra to deliver a powerful open-source big data analytic solution.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
8. RDD - Resilient Distributed Dataset
RDD is fault-tolerant collection of elements
distributed across many servers on which we can
perform parallel operations.
19. Easy of use
- Spark is easier to program and include an
interactive mode.
- Hadoop MapReduce is harder to program
but many tools are available to make it
easier.
