Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
In the age of Big Data and large volume analytics there is a lot to cover and a lot to learn. While at Microsoft developing Windows HDInsight and now developing a one of kind Big Data product at my own company Big Data Perspective, San Francisco I have lived last several years covering Big Data at various level. This talk is customized for database and business intelligence (BI) professionals, programmers, Hadoop administrators, researchers, technical architects, operations engineers, data analysts, and data scientists understand the core concepts of Big Data Analytics on Hadoop. This webinar will be useful for those, who wants to know what is Hadoop, and how they can take advantage just by spending few dollars to run the cluster. The webinar is great for those who are looking to deploy their first data cluster and run MapReduce jobs to discover insights.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
In the age of Big Data and large volume analytics there is a lot to cover and a lot to learn. While at Microsoft developing Windows HDInsight and now developing a one of kind Big Data product at my own company Big Data Perspective, San Francisco I have lived last several years covering Big Data at various level. This talk is customized for database and business intelligence (BI) professionals, programmers, Hadoop administrators, researchers, technical architects, operations engineers, data analysts, and data scientists understand the core concepts of Big Data Analytics on Hadoop. This webinar will be useful for those, who wants to know what is Hadoop, and how they can take advantage just by spending few dollars to run the cluster. The webinar is great for those who are looking to deploy their first data cluster and run MapReduce jobs to discover insights.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
Big Data and Architectural Patterns on AWS - Pop-up Loft Tel AvivAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
AWS re:Invent 2016: ElastiCache Deep Dive: Best Practices and Usage Patterns ...Amazon Web Services
In this session, we provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Introduction to key architectural concepts to build a data lake using Amazon S3 as the storage layer and making this data available for processing with a broad set of analytic options including Amazon EMR and open source frameworks such as Apache Hadoop, Spark, Presto, and more.
Peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns of our Memcached and Redis offerings and how customers have used them for in-memory operations and achieved improved latency and throughput for applications. During this session, we review best practices, design patterns, and anti-patterns related to Amazon ElastiCache.
ElastiCache Deep Dive: Best Practices and Usage Patterns - March 2017 AWS Onl...Amazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Amazon Elasticache Deep Dive - March 2017 AWS Online Tech TalksAmazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Best Practices with IoT Security - February Online Tech TalksAmazon Web Services
AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. This tech talk will introduce the best practices for IoT Security in the cloud and the access control mechanisms used by AWS IoT. These mechanisms can be used to not only securely build and provision devices, but also to integrate devices with other AWS services to create secure solutions.
Learning Objectives:
• Learn common Internet of Things security issues
• Learn about AWS IoT security and access control mechanisms
• Learn how to build secure interactions with the AWS Cloud
Introduction to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of Spot EC2 instances to reduce costs, and other Amazon EMR architectural best practices.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Big-Data Hadoop Tutorials - MindScripts Technologies, Pune amrutupre
MindScripts Technologies, is the leading Big-Data Hadoop Training institutes in Pune, providing a complete Big-Data Hadoop Course with Cloud-Era certification.
https://www.learntek.org/big-data-and-hadoop-training/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This slide gives a simple and purposeful knowledge about popular Hadoop platforms.
From simple definition to importance of Hadoop in modern era the presentation also introduces Hadoop service providers along with its core components.
Do go through it once and comment below with your feedback. I am sure that this slide will help many in presenting basics of Hadoop for their projects or business purpose.
The crisp information has been generated after going through detailed information available on internet as well as research papers
this is a presentation on hadoop basics. Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models.
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.
Show drafts
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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.
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
1. Hadoop In Action
When
Where
Tuesday 06-12-2016
06:00 PM -08:00 PM
Badir Program for
Technology Incubators
#DataRiyadh DataGeeks DataGeeksarabia
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data
transformations on it , save our result and show it via BI tool.
presented by
Mahmoud Yassin
Hadoop:
-Hadoop quick
definition.
-Why Hadoop?
-Hadoop ecosystem.
-Tools to be used.
Practical part:
-What’s the current setup?
-Ambari look.
-Current installed systems.
-Use case high-level
description.
-Steps to develop the use
case?
Use case:
-Locating the data.
-Ingest the data into the HDFS
-See how the files got created in
HDFS
-Feed other data from DB.
-Data querying via Hive and
MapReduce
-Hive table creation.
-Running transudation job via Pig.
-Check the Hive metastore.
-Connect BI to Hadoop.
-Sqoop basic commands
-End to End look solution.
3. Agenda:
Hadoop:
-Hadoop quick definition.
-Why Hadoop?
-Hadoop ecosystem.
-Tools to be used.
Practical part:
-What’s the current setup?
-Ambari look.
-Current installed systems.
-Use case high-level description.
-Steps to develop the use case?
Use case:
-Locating the data.
-Ingest the data into the HDFS
-See how the files got created in HDFS
-Feed other data from DB.
-Data querying via Hive and MapReduce
-Hive table creation.
-Running transudation job via Pig.
-Check the Hive metastore.
- Connect BI to Hadoop.
4. What is Hadoop
The Apache Hadoop software library is a framework that allows for the distributed
processing of large data sets across clusters of computers using simple programming
models.
Hadoop is an open-source software framework for storing data and running
applications on clusters of commodity hardware. It provides massive storage for any
kind of data, enormous processing power and the ability to handle virtually limitless
concurrent tasks or jobs.
#DataRiyadh
5. Why Hadoop is important ?
Ability to store and process huge amounts of
any kind of data, quickly.
With data volumes and varieties constantly
increasing, especially from social media and the
Internet of Things (IoT), that's a key
consideration.
Computing power. Hadoop's distributed computing model processes big data fast. The
more computing nodes you use, the more processing power you have.
Fault tolerance. Data and application processing are protected against hardware failure.
If a node goes down, jobs are automatically redirected to other nodes to make sure the
distributed computing does not fail. Multiple copies of all data are stored automatically.
6. Why Hadoop is important ?
Flexibility. Unlike traditional relational databases, you
don’t have to preprocess data before storing it. You
can store as much data as you want and decide how
to use it later. That includes unstructured data like
text, images and videos.
Low cost. The open-source framework is free and uses commodity hardware
to store large quantities of data.
Scalability. You can easily grow your system to handle more data simply by
adding nodes. Little administration is required.
Scalability
Horizontal scaling means that you scale by adding more
machines into your pool of resources
Vertical scaling means that you scale by adding more
power (CPU, RAM) to an existing machine #DataRiyadh
8. Hadoop | Data Ingestion
Apache Sqoop is a tool designed for efficiently transferring bulk data between
Apache Hadoop and structured data stores such as relational databases.
#DataRiyadh
9. Hadoop | Data Storage Layer
Hadoop Distributed File System (HDFS) offers a way to store large files across
multiple machines. Hadoop and HDFS was derived from Google File System
(GFS) paper.
#DataRiyadh
11. Hadoop | Data Processing Layer
MapReduce is the heart of Hadoop. It is this programming paradigm that
allows for massive scalability across hundreds or thousands of servers in a
Hadoop cluster with a parallel, distributed algorithm.
#DataRiyadh
13. Hadoop | Data Processing Layer
A scripting SQL based language and execution environment for creating complex
MapReduce transformations. Functions are written in Pig Latin (the language)
and translated into executable MapReduce jobs. Pig also allows the user to
create extended functions (UDFs) using Java.
#DataRiyadh
14. Hadoop | Data Querying Layer
A distributed data warehouse built on top of HDFS to manage and organize
large amounts of data. Hive provides a query language based on SQL semantic
(HiveQL) which is translated by the runtime engine to MapReduce jobs for
querying the data.
#DataRiyadh
15. Hadoop | Management Layer
intuitive, easy-to-use Hadoop management web UI. Apache Ambari was
donated by Hortonworks team. It's a powerful and nice interface for Hadoop
and other typical applications from the Hadoop ecosystem.
16. Hadoop | Management Layer
is an open-source Web interface that supports Apache Hadoop and its
ecosystem, licensed under the Apache v2 license
19. Current Setup
is a subsidiary of Dell Technologies, that provides cloud and virtualization
software and services.
http://www.vmware.com/
20. Current Setup
The VM make it easy to quickly get hands-on with CDH for testing, demo, and
self-learning purposes, and include Cloudera Manager for managing your
cluster. Cloudera QuickStart VM also includes a tutorial, sample data, and scripts
for getting started.
http://www.cloudera.com/downloads/quickstart_vms/5-8.html
23. The case:
Data Sources
HDFS :
A platform for manipulating data stored in HDFS via a high-level
language called Pig Latin. It does data extractions, transformations
and loading, and basic analysis in patch mode
File
Video
RDBMS
A platform for manipulating data
stored in HDFS via a high-level
language called Pig Latin. It does
data extractions, transformations
and loading, and basic analysis in
patch mode
A data warehousing and SQL-like
query language that presents data
in the form of tables. Hive
programming is similar to database
programming.
open source massively parallel
processing (MPP) SQL query
engine for data stored in a
computer cluster running Apache
Hadoop.
Connect BI tools
to the Hadoop
cluster
Cloudera CDH cluster
24. Basic Linux Commands
cat [filename] Display file’s contents to the standard output
device
(usually your monitor).
cd /directorypath Change to directory.
chmod [options] mode filename Change a file’s permissions.
clear Clear a command line screen/window for a
fresh start.
cp [options] source destination Copy files and directories.
ls [options] List directory contents.
mkdir [options] directory Create a new directory.
mv [options] source destination Rename or move file(s) or directories.
pwd Display the pathname for the current
directory.touch filename Create an empty file with the specified name.
who [options] Display who is logged on.