Apache Hadoop is an open source framework that allows you to process large data sets (a.k.a Big Data) across clusters using simple programming models. This TechTalk will introduce you to real-life usages of Hadoop, so you can better understand when to use it, as well as describing its components and the first steps to setup a Hadoop cluster.
By Dina Abu Khader - System Administrator
YouTube video: http://www.youtube.com/watch?v=pSjP171i-gM
Apache Hadoop is an open source framework that allows you to process large data sets (a.k.a Big Data) across clusters using simple programming models. This TechTalk will introduce you to real-life usages of Hadoop, so you can better understand when to use it, as well as describing its components and the first steps to setup a Hadoop cluster.
By Dina Abu Khader - System Administrator
YouTube video: http://www.youtube.com/watch?v=pSjP171i-gM
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
Introduction to Hadoop.
What are Hadoop, MapReeduce, and Hadoop Distributed File System.
Who uses Hadoop?
How to run Hadoop?
What are Pig, Hive, Mahout?
“BIG DATA” is data that is big in
volume
velocity and
Variety
“TODAY’S BIG MAY BE TOMMOROW’S NORMAL”
Varieties deals with a wide range of data types
Structured data - RDMS
Semi – structured data – HTML,XML
Unstructured data – audios, videos, emails, photos, pdf, social media
hadoop
It was created by DOUG CUTTING and MICHEAL CAFARELLA in 2005
2003 – NUTCH open source search engine( lucene ,sphinx ,etc…)
(google published some papers mentioning about DFS and MAP REDUCE)
After yahoo took this initiative step
Then the creation of hadoop took place
Hadoop 0.1.0 was relesed april 2006
As of now hadoop 2.8 is available
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
Introduction to Hadoop.
What are Hadoop, MapReeduce, and Hadoop Distributed File System.
Who uses Hadoop?
How to run Hadoop?
What are Pig, Hive, Mahout?
“BIG DATA” is data that is big in
volume
velocity and
Variety
“TODAY’S BIG MAY BE TOMMOROW’S NORMAL”
Varieties deals with a wide range of data types
Structured data - RDMS
Semi – structured data – HTML,XML
Unstructured data – audios, videos, emails, photos, pdf, social media
hadoop
It was created by DOUG CUTTING and MICHEAL CAFARELLA in 2005
2003 – NUTCH open source search engine( lucene ,sphinx ,etc…)
(google published some papers mentioning about DFS and MAP REDUCE)
After yahoo took this initiative step
Then the creation of hadoop took place
Hadoop 0.1.0 was relesed april 2006
As of now hadoop 2.8 is available
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
Hadoop is one of the booming and innovative data analytics technology which can effectively handle Big Data problems and achieve the data security. It is an open source and trending technology which involves in data collection, data processing and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms.
Hadoop is an open source, distributed computation platform, that is very important in the worlds of search, analytics, and big data. Donald Miner, a Solutions Architect at Greenplum, will give an hour presentation that will focus on ways to get started with Hadoop and provide advice on how successfully utilize the platform
Specific topics of discussion include how Hadoop works, what Hadoop should and should not be used for, MapReduce design patterns, and the upcoming synergy of SQL and NoSQL in Hadoop.
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.
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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.
2. Outline
• Big Data
• Hadoop
• Hadoop Cluster
• Hadoop Ecosystem
• HDFS
• MapReduce
• Demo
3. Big Data
• There’s no one definition for ‘big data’, it’s a very subjective term.
4. Big Data
• Most people would consider a data set of terabytes or more to be ‘big data’,
but there are certainly people using Hadoop with great success on smaller
chunks of data than that.
• One reasonable definition is that it’s data which can’t comfortably be
processed on a single machine.
5. The 3 V’s of Big Data
• Volume refers to the size of data that you’re dealing with.
• Variety refers to the fact that the data is often coming from lots of different
sources and in many different formats
• Velocity refers to the speed at which the data is being generated
6. Hadoop
• The logo and the name comes from Doug Cutting son’s elephant toy.
• Started as a search engine project called Nutch in 2003 by Doug Cutting
and Mike Cafarella.
• Implemented Google’s white paper about distributed file system.
• Invested by Yahoo in 2006 and become a open-source project.
• Also in 2016 Hadoop 0.1.0 released
7. Hadoop Cluster
The core Hadoop project consists of a way to store data, known as the
Hadoop Distributed File System, or HDFS, and a way to process the data,
called MapReduce. The key concept is that we split the data up and store it
across a collection of machines, known as a cluster. Then, when we want to
process the data, we process it where it’s actually stored. Rather than
retrieving the data from a central server, instead it’s already on the cluster,
and we can process it in place.
Store in HDFS
Process with
MapReduce