This document provides an overview of big data concepts and technologies. It discusses the growth of data, characteristics of big data including volume, variety and velocity. Popular big data technologies like Hadoop, MapReduce, HDFS, Pig and Hive are explained. NoSQL databases like Cassandra, HBase and MongoDB are introduced. The document also covers massively parallel processing databases and column-oriented databases like Vertica. Overall, the document aims to give the reader a high-level understanding of the big data landscape and popular associated technologies.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
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!
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
Introduction to Big Data Technologies & ApplicationsNguyen Cao
Big Data Myths, Current Mainstream Technologies related to Collecting, Storing, Computing & Stream Processing Data. Real-life experience with E-commerce businesses.
Hadoop Master Class : A concise overviewAbhishek Roy
Abhishek Roy will teach a master class on Big Data and Hadoop. The class will cover what Big Data is, the history and background of Hadoop, how to set up and use Hadoop, and tools like HDFS, MapReduce, Pig, Hive, Mahout, Sqoop, Flume, Hue, Zookeeper and Impala. The class will also discuss real world use cases and the growing market for Big Data tools and skills.
Big data refers to datasets that are too large to be managed by traditional database tools. It is characterized by volume, velocity, and variety. Hadoop is an open-source software framework that allows distributed processing of large datasets across clusters of computers. It works by distributing storage across nodes as blocks and distributing computation via a MapReduce programming paradigm where nodes process data in parallel. Common uses of big data include analyzing social media, sensor data, and using machine learning on large datasets.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
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!
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
Introduction to Big Data Technologies & ApplicationsNguyen Cao
Big Data Myths, Current Mainstream Technologies related to Collecting, Storing, Computing & Stream Processing Data. Real-life experience with E-commerce businesses.
Hadoop Master Class : A concise overviewAbhishek Roy
Abhishek Roy will teach a master class on Big Data and Hadoop. The class will cover what Big Data is, the history and background of Hadoop, how to set up and use Hadoop, and tools like HDFS, MapReduce, Pig, Hive, Mahout, Sqoop, Flume, Hue, Zookeeper and Impala. The class will also discuss real world use cases and the growing market for Big Data tools and skills.
Big data refers to datasets that are too large to be managed by traditional database tools. It is characterized by volume, velocity, and variety. Hadoop is an open-source software framework that allows distributed processing of large datasets across clusters of computers. It works by distributing storage across nodes as blocks and distributing computation via a MapReduce programming paradigm where nodes process data in parallel. Common uses of big data include analyzing social media, sensor data, and using machine learning on large datasets.
The document provides an overview of big data and Hadoop, discussing what big data is, current trends and challenges, approaches to solving big data problems including distributed computing, NoSQL, and Hadoop, and introduces HDFS and the MapReduce framework in Hadoop for distributed storage and processing of large datasets.
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012Gigaom
The document discusses the 3 V's of big data: volume, velocity, and variety. It provides examples of how each V impacts data analysis and storage. It also discusses how text data has been a major driver of big data growth and challenges. The key challenges are processing large and diverse datasets quickly enough to keep up with real-time data streams and demands.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of structured, semi-structured and unstructured data that is growing exponentially and is too large for traditional databases to handle. It discusses the 4 V's of big data - volume, velocity, variety and veracity. The document then describes Hadoop as an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It outlines the key components of Hadoop including HDFS, MapReduce, YARN and related modules. The document also discusses challenges of big data, use cases for Hadoop and provides a demo of configuring an HDInsight Hadoop cluster on Azure.
The document discusses big data and NoSQL technologies. It defines big data, discusses its key characteristics of volume, velocity, and variety. It then discusses NoSQL databases as an alternative to traditional SQL databases for handling big data workloads. Specific NoSQL technologies and how they provide more scalability and flexibility for big data are covered. The document also addresses whether NoSQL is replacing SQL databases and argues it depends on the specific use case.
Big Data Processing Using Hadoop InfrastructureDmitry Buzdin
The document discusses using Hadoop infrastructure for big data processing. It describes Intrum Justitia SDC, which has data across 20 countries in various formats and a high number of data objects. Hadoop provides solutions like MapReduce and HDFS for distributed storage and processing at scale. The Hadoop ecosystem includes tools like Hive, Pig, HBase, Impala and Oozie that help process and analyze large datasets. Examples of using Hadoop with Java and integrating it into development environments are also included.
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
This document is a presentation on big data and Hadoop. It introduces big data, how it is growing exponentially, and the challenges of storing and analyzing unstructured data. It discusses how Sears moved to Hadoop to gain insights from all of its customer data. The presentation explains why Hadoop is in high demand, as it allows distributed processing of large datasets across commodity hardware. It provides an overview of the Hadoop ecosystem including HDFS, MapReduce, Hive, HBase and more. Finally, it discusses job opportunities and salaries in big data which are high and growing significantly.
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.
Core concepts and Key technologies - Big Data AnalyticsKaniska Mandal
Big data analytics has evolved beyond batch processing with Hadoop to extract intelligence from data streams in real time. New technologies preserve data locality, allow real-time processing and streaming, support complex analytics functions, provide rich data models and queries, optimize data flow and queries, and leverage CPU caches and distributed memory for speed. Frameworks like Spark and Shark improve on MapReduce with in-memory computation and dynamic resource allocation.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
This document provides an introduction to big data and NoSQL databases. It begins with an introduction of the presenter. It then discusses how the era of big data came to be due to limitations of traditional relational databases and scaling approaches. The document introduces different NoSQL data models including document, key-value, graph and column-oriented databases. It provides examples of NoSQL databases that use each data model. The document discusses how NoSQL databases are better suited than relational databases for big data problems and provides a real-world example of Twitter's use of FlockDB. It concludes by discussing approaches for working with big data using MapReduce and provides examples of using MongoDB and Azure for big data.
The document discusses big data analytics and related topics. It provides definitions of big data, describes the increasing volume, velocity and variety of data. It also discusses challenges in data representation, storage, analytical mechanisms and other aspects of working with large datasets. Approaches for extracting value from big data are examined, along with applications in various domains.
Big data is characterized by 3Vs - volume, velocity, and variety. Hadoop is a framework for distributed processing of large datasets across clusters of computers. It provides HDFS for storage, MapReduce for batch processing, and YARN for resource management. Additional tools like Spark, Mahout, and Zeppelin can be used for real-time processing, machine learning, and data visualization respectively on Hadoop. Benefits of Hadoop include ease of scaling to large data, high performance via parallel processing, reliability through data protection and failover.
This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.
This document discusses big data analytics using Hadoop. It provides an overview of loading clickstream data from websites into Hadoop using Flume and refining the data with MapReduce. It also describes how Hive and HCatalog can be used to query and manage the data, presenting it in a SQL-like interface. Key components and processes discussed include loading data into a sandbox, Flume's architecture and data flow, using MapReduce for parallel processing, how HCatalog exposes Hive metadata, and how Hive allows querying data using SQL queries.
This was presented at NHN on Jan. 27, 2009.
It introduces Big Data, its storages, and its analyses.
Especially, it covers MapReduce debates and hybrid systems of RDBMS and MapReduce.
In addition, in terms of Schema-Free, various non-relational data storages are explained.
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
This document provides an overview of big data and Hadoop. It defines big data as high-volume, high-velocity, and high-variety data that requires new techniques to capture value. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for storage and MapReduce for parallel processing. Benefits of Hadoop are its ability to handle large amounts of structured and unstructured data quickly and cost-effectively at large scales.
The document provides an overview of big data and Hadoop, discussing what big data is, current trends and challenges, approaches to solving big data problems including distributed computing, NoSQL, and Hadoop, and introduces HDFS and the MapReduce framework in Hadoop for distributed storage and processing of large datasets.
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012Gigaom
The document discusses the 3 V's of big data: volume, velocity, and variety. It provides examples of how each V impacts data analysis and storage. It also discusses how text data has been a major driver of big data growth and challenges. The key challenges are processing large and diverse datasets quickly enough to keep up with real-time data streams and demands.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of structured, semi-structured and unstructured data that is growing exponentially and is too large for traditional databases to handle. It discusses the 4 V's of big data - volume, velocity, variety and veracity. The document then describes Hadoop as an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It outlines the key components of Hadoop including HDFS, MapReduce, YARN and related modules. The document also discusses challenges of big data, use cases for Hadoop and provides a demo of configuring an HDInsight Hadoop cluster on Azure.
The document discusses big data and NoSQL technologies. It defines big data, discusses its key characteristics of volume, velocity, and variety. It then discusses NoSQL databases as an alternative to traditional SQL databases for handling big data workloads. Specific NoSQL technologies and how they provide more scalability and flexibility for big data are covered. The document also addresses whether NoSQL is replacing SQL databases and argues it depends on the specific use case.
Big Data Processing Using Hadoop InfrastructureDmitry Buzdin
The document discusses using Hadoop infrastructure for big data processing. It describes Intrum Justitia SDC, which has data across 20 countries in various formats and a high number of data objects. Hadoop provides solutions like MapReduce and HDFS for distributed storage and processing at scale. The Hadoop ecosystem includes tools like Hive, Pig, HBase, Impala and Oozie that help process and analyze large datasets. Examples of using Hadoop with Java and integrating it into development environments are also included.
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
This document is a presentation on big data and Hadoop. It introduces big data, how it is growing exponentially, and the challenges of storing and analyzing unstructured data. It discusses how Sears moved to Hadoop to gain insights from all of its customer data. The presentation explains why Hadoop is in high demand, as it allows distributed processing of large datasets across commodity hardware. It provides an overview of the Hadoop ecosystem including HDFS, MapReduce, Hive, HBase and more. Finally, it discusses job opportunities and salaries in big data which are high and growing significantly.
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.
Core concepts and Key technologies - Big Data AnalyticsKaniska Mandal
Big data analytics has evolved beyond batch processing with Hadoop to extract intelligence from data streams in real time. New technologies preserve data locality, allow real-time processing and streaming, support complex analytics functions, provide rich data models and queries, optimize data flow and queries, and leverage CPU caches and distributed memory for speed. Frameworks like Spark and Shark improve on MapReduce with in-memory computation and dynamic resource allocation.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
This document provides an introduction to big data and NoSQL databases. It begins with an introduction of the presenter. It then discusses how the era of big data came to be due to limitations of traditional relational databases and scaling approaches. The document introduces different NoSQL data models including document, key-value, graph and column-oriented databases. It provides examples of NoSQL databases that use each data model. The document discusses how NoSQL databases are better suited than relational databases for big data problems and provides a real-world example of Twitter's use of FlockDB. It concludes by discussing approaches for working with big data using MapReduce and provides examples of using MongoDB and Azure for big data.
The document discusses big data analytics and related topics. It provides definitions of big data, describes the increasing volume, velocity and variety of data. It also discusses challenges in data representation, storage, analytical mechanisms and other aspects of working with large datasets. Approaches for extracting value from big data are examined, along with applications in various domains.
Big data is characterized by 3Vs - volume, velocity, and variety. Hadoop is a framework for distributed processing of large datasets across clusters of computers. It provides HDFS for storage, MapReduce for batch processing, and YARN for resource management. Additional tools like Spark, Mahout, and Zeppelin can be used for real-time processing, machine learning, and data visualization respectively on Hadoop. Benefits of Hadoop include ease of scaling to large data, high performance via parallel processing, reliability through data protection and failover.
This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.
This document discusses big data analytics using Hadoop. It provides an overview of loading clickstream data from websites into Hadoop using Flume and refining the data with MapReduce. It also describes how Hive and HCatalog can be used to query and manage the data, presenting it in a SQL-like interface. Key components and processes discussed include loading data into a sandbox, Flume's architecture and data flow, using MapReduce for parallel processing, how HCatalog exposes Hive metadata, and how Hive allows querying data using SQL queries.
This was presented at NHN on Jan. 27, 2009.
It introduces Big Data, its storages, and its analyses.
Especially, it covers MapReduce debates and hybrid systems of RDBMS and MapReduce.
In addition, in terms of Schema-Free, various non-relational data storages are explained.
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
This document provides an overview of big data and Hadoop. It defines big data as high-volume, high-velocity, and high-variety data that requires new techniques to capture value. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for storage and MapReduce for parallel processing. Benefits of Hadoop are its ability to handle large amounts of structured and unstructured data quickly and cost-effectively at large scales.
Big Data Analytics & Trends Presentation discusses what big data is, why it's important, definitions of big data, data types and landscape, characteristics of big data like volume, velocity and variety. It covers data generation points, big data analytics, example scenarios, challenges of big data like storage and processing speed, and Hadoop as a framework to solve these challenges. The presentation differentiates between big data and data science, discusses salary trends in Hadoop/big data, and future growth of the big data market.
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
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 Cloud computing and Big Data-HadoopNagarjuna D.N
Cloud Computing Evolution
Why Cloud Computing needed?
Cloud Computing Models
Cloud Solutions
Cloud Jobs opportunities
Criteria for Big Data
Big Data challenges
Technologies to process Big Data- Hadoop
Hadoop History and Architecture
Hadoop Eco-System
Hadoop Real-time Use cases
Hadoop Job opportunities
Hadoop and SAP HANA integration
Summary
Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by large volumes of data in various formats that are difficult to process using traditional data management and analysis systems. It is generated from sources like user interactions, sensors and systems logs. Tools like Hadoop and NoSQL databases enable storing, processing and analyzing big data. Organizations apply big data analytics to areas such as healthcare, retail and security. While big data poses privacy and management challenges, it also provides opportunities to gain insights and make improved decisions. The big data industry is growing rapidly and expected to be worth over $100 billion.
This document provides an overview of big data including:
- It defines big data and discusses its key characteristics of volume, velocity, and variety.
- It describes sources of big data like social media, sensors, and user clickstreams. Tools for big data include Hadoop, MongoDB, and cloud computing.
- Applications of big data analytics include smarter healthcare, traffic control, and personalized marketing. Risks include privacy and high costs. Benefits include better decisions, opportunities for new businesses, and improved customer experiences.
- The future of big data is strong with worldwide revenues projected to grow from $5 billion in 2012 to over $50 billion in 2017, creating millions of new jobs for data scientists and analysts
Big Data brings big promise and also big challenges, the primary and most important one being the ability to deliver Value to business stakeholders who are not data scientists!
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
This document provides an introduction to a course on big data and analytics. It outlines the following key points:
- The instructor and TA contact information and course homepage.
- The course will cover foundational data analytics, Hadoop/MapReduce programming, graph databases, and other big data topics.
- Big data is defined as data that is too large or complex for traditional database tools to process. It is characterized by high volume, velocity, and variety.
- Examples of big data sources and the exponential growth of data volumes are provided. Real-time analytics and fast data processing are also discussed.
Big data is being collected from many sources like the web, social networks, and businesses. Hadoop is an open source software framework that can process large datasets across clusters of computers. It uses a programming model called MapReduce that allows automatic parallelization and fault tolerance. Hadoop uses commodity hardware and can handle various data formats and large volumes of data distributed across clusters. Companies like Cloudera provide tools and services to help users manage and analyze big data with Hadoop.
This document provides an introduction to a course on big data. It outlines the instructor and TA contact information. The topics that will be covered include data analytics, Hadoop/MapReduce programming, graph databases and analytics. Big data is defined as data sets that are too large and complex for traditional database tools to handle. The challenges of big data include capturing, storing, analyzing and visualizing large, complex data from many sources. Key aspects of big data are the volume, variety and velocity of data. Cloud computing, virtualization, and service-oriented architectures are important enabling technologies for big data. The course will use Hadoop and related tools for distributed data processing and analytics. Assessment will include homework, a group project, and class
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
This document provides an overview of big data including:
- It defines big data and describes its three key characteristics: volume, velocity, and variety.
- It explains how big data is stored, selected, and processed using techniques like Hadoop and NoSQL databases.
- It discusses some common sources of big data, tools used to analyze it, and applications of big data analytics across different industries.
Introduction to Big Data and NoSQL.
This presentation was given to the Master DBA course at John Bryce Education in Israel.
Work is based on presentations by Michael Naumov, Baruch Osoveskiy, Bill Graham and Ronen Fidel.
What exactly is big data? The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources.
This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
2. Agenda
• Big Data Overview
• Hadoop Theory and Practice
• MapReduce in Action
• NoSQL
• MPP Database
• What’s hot?
3. Big Five IT Trends
• Mobile
• Social Media
• Cloud Computing
• Consumerization of IT
• Big Data
4. Big Data Era
• The coming of the Big Data Era is a chance for
everyone in the technology world to decide into which
camp they fall, as this era will bring the biggest
opportunity for companies and individuals in the
technology since the dawn of the Internet.
− Rob Thomas, IBM Vice President, Business Development
6. 6
Big Data – a growing torrent
• 2 billion internet users
• 5 billion mobile phones in use in 2010.
• 30 billion pieces of content shared on Facebook every month.
• 7TB of data are processed by Twitter every day,
• 10TB of data are processed by Facebook every day.
• 40% projected growth in global data generated per year.
• 235T data collected by US library of Congress in April 2011
• 15 out of 17 sectors in the US have more data stored per company
than the US library of Congress.
• 90% of the data in the world today has been created in the last two
years alone.
7. Data Rich World
• Data capture and collection
− Sensor data, Mobile device, Social Network, Web clickstream,
Traffic monitoring, Multimedia content, Smart energy meters,
DNA analysis, Industry machines in the age of Internet of
Things, Consumer activities – communicating, browsing,
buying, sharing, searching – create enormous trails of data.
• Data Storage
− Cost of storage is reduced tremendously
− Seagate 3 TB Barracuda @ $149.99 from Amazon.com (4.9¢/GB)
8. Technology world has changed
• Users: 2,000 users vs. a potential user base of 2 billion
• Applications: Online transaction system vs. Web applications.
• Application architecture: centralized vs. scale-up
• Infrastructure: a commodity box has more computational power
than a supercomputer a decade ago
• 80% percent of the world’s information is unstructured.
• Unstructured information is growing at 15 times the rate of
structured information.
• Database architecture has not kept pace
9. A Sample Case – Big Data
• ShopSavvy5 – mobile shopping App
− 40,000+ retailers
− Millions shoppers
− Millions retail store locations
− 240M+ product pictures and user action shots
− 3040M+ product attributes (color, size, features etc.)
− 14,720M+ prices from retailers
− 100+ price requests per second
− delivering real-time inventory and price information
10. A Sample Case – Big Data (Cont)
• ShopSavvy Architecture
− An entirely new platform, ProductCloud, leverages the
latest Big Data tool like Cassandra, Hadoop, and Mahout,
maintains HUGE histories of prices, products, scans and
locations that number in the hundreds of billions of items.
− Open architecture layers tools like Mahout on top of the
platform to enable new features like price prediction, user
recommendations, product categorization and product
resolution.
13. What is “Big Data”
• The term Big Data applies to information that can’t be
processed or analyzed using traditional processes or tool.
• Big Data creates values in several ways
− Create transparency
− Enabling experimentation to discover needs, expose
variability, and improve performance
− Segmenting population to customize actions
− Replacing/supporting human decision making with machine
algorithms
− Innovating new business models, products, and services, e.g.
risk estimation.
14. 14
Big Data = Big Value
• $300 billion potential annual value to US health – more than double
the total annual health care spending in Spain.
• $350 billion potential annual value to Europe’s public sector
administration – more than GDP of Greece.
• $600 billion potential annual consumer surplus from using personal
location data globally.
• 60% potential increase in retailer’s operating margins possible with
big data.
• 140,000 to 190,000 more deep analytic talent positions, and 1.5
million data-savvy managers needed to take full advantage of big
data in the United States.
• Gartner predicts that “Big Data will deliver transformational benefits
to enterprises within 2 to 5 years"
15. Characteristics of Big Data
• Volume – Terabytes Zettabytes
• Variety – structured, semi-structured, unstructured data
• Velocity – Batch -> Streaming Data, Real-time
17. Traditional Data Warehouse vs. Big Data
• Traditional warehouses
− mostly idea for analyzing structured data and producing
insights with known and relatively stable measurements.
• Big Data solutions
− idea for analyzing not only raw structured data, but semi-
structured and structured data from a wide variety of
sources.
− idea when all of the data needs to be analyzed versus a
sample of data.
− Idea for iterative and exploratory analysis when business
measures are not predetermined.
18. CAP Theorem
• CAP
− Consistency
− Availability
− Tolerance to network Partitions
• Consistency models
− Strong consistency
− Weak consistency
− Eventual consistency
• Architectures
− CA: traditional relational database
− AP: NoSQL database
19. ACID vs. BASE
• ACID
− Atomicity
− Consistency
− Isolation
− Durability
• BASE
− Basically available
− Soft-state
− Eventual consistency
20. Lower Priorities
• No Complex querying functionality
− No support for SQL
− CRUD operations through database specific API
• No support for joins
− Materialize simple join results in the relevant row
− Give up normalization of data?
• No support for transactions
− Most data stores support single row transactions
− Tunable consistency and availability (e.g., Dynamo)
Achieve high scalability
21. Why sacrifice Consistency?
• It is a simple solution
− nobody understands what sacrificing P means
− sacrificing A is unacceptable in the Web
− possible to push the problem to app developer
• C not needed in many applications
− Banks do not implement ACID (classic example wrong)
− Airline reservation only transacts reads (Huh?)
− MySQL et al. ship by default in lower isolation level
• Data is noisy and inconsistent anyway
− making it, say, 1% worse does not matter
22. Important Design Goals
• Scale out: designed for scale
− Commodity hardware
− Low latency updates
− Sustain high update/insert throughput
• Elasticity – scale up and down with load
• High availability – downtime implies lost revenue
− Replication (with multi-mastering)
− Geographic replication
− Automated failure recovery
23. A Brief History of Hadoop
• Hadoop is an open source project of the Apache Foundation.
• Hadoop has its origins in Apache Nutch, an open source web search engine, itself
a part of the Lucene project.
• In 2003, Google published a paper that described the architecture of Google’s
distributed filesystem, called GFS.
• In 2004, Google published the paper that introduced MapReduce.
• It is a framework written in Java originally developed by Doug Cutting, the creator
of Apache Lucene, who named it after his son's toy elephant.
• 2004 - Initial versions of what is now Hadoop Distributed Filesystem and Map-
Reduce implemented.
• January 2006 — Doug Cutting joins Yahoo!.
• February 2006 —Adoption of Hadoop by Yahoo! Grid team.
• April 2006—Sort benchmark (10 GB/node) run on 188 nodes in 47.9 hours.
24. A Brief History of Hadoop (Cont)
• January 2007—Research cluster reaches 900 nodes.
• In January 2008, Hadoop was made its own top-level project at
Apache. By this time, Hadoop was being used by many other
companies such as Facebook and the New York Times.
• In February 2008, Yahoo! announced that its production search index
was being generated by a 10,000-node Hadoop cluster.
• In April 2008, Hadoop broke a world record to become the fastest
system to sort a terabyte of data.
• March 2009 — 17 clusters with a total of 24,000 nodes.
• April 2009 — Won the minute sort by sorting 500 GB in 59 seconds
(on 1,400 nodes) and the 100 terabyte sort in 173 minutes (on 3,400
nodes).
25. Hadoop Echosystem
• Common - A set of components for distributed filesystems and general I/O
• Avro - A serialization system for efficient data storage.
• MapReduce - A distributed data processing model and execution
environment that runs on large clusters of commodity machines.
• HDFS - A distributed filesystem.
• Pig - A data flow language for exploring very large datasets.
• Hive - A distributed data warehouse system.
• Hbase - A distributed, column-oriented database.
• ZoopKeeper - A distributed, highly available coordination service.
• Sqoop - A tool for efficiently moving data between relational databases
and HDFS.
26. Hadoop Distributed File System - HDFS
• Hadoop filesystem that runs on top of existing file system
• Designed to handle very large files with streaming data access
patterns
• Use blocks to store a file or parts of file
− 64MB (default), 128MB (recommended) - compare to 4KB in UNIX
• 1 HDFS block is supported by multiple operation system blocks
• Advantages of blocks
− Big throughput
− Fixed size - easy to calculate how many fit on a disk
− A file can be larger than any single disk in the network
− Fits well with replication to provide fault tolerance and availability
29. Hadoop Node Type
• HDFS nodes
• NameNode
• One per cluster, manages the filesystem namespace and meta data, large memory
requirements, keep entire filesystem metadata in memory
• DataNode
• Many per cluster, manages blocks with data and servers them to clients
• MapReduce nodes
• JobTracker
• One per cluster, receives job requests, schedules and monitors MapReduce jobs on
task trackers
• TaskTracker
• Many per cluster, each TaskTracker spawns Java Virtual Machines to run your map or
reduce task.
32. Before MapReduce…
• Large scale data processing was difficult!
− Managing hundreds or thousands of processors
− Managing parallelization and distribution
− I/O Scheduling
− Status and monitoring
− Fault/crash tolerance
• MapReduce provides all of these, easily!
33. MapReduce Overview
• What is it?
− Programming model used by Google
− A combination of the Map and Reduce models with an
associated implementation
− Used for processing and generating large data sets
• How does it solve our previously mentioned problems?
− MapReduce is highly scalable and can be used across
many computers.
− Many small machines can be used to process jobs that
normally could not be processed by a large machine.
37. Map Abstraction
• Inputs a key/value pair
– Key is a reference to the input value
– Value is the data set on which to operate
• Evaluation
– Function defined by user
– Applies to every value in value input
– Might need to parse input
• Produces a new list of key/value pairs
– Can be different type from input pair
39. Reduce Abstraction
• Starts with intermediate Key / Value pairs
• Ends with finalized Key / Value pairs
• Starting pairs are sorted by key
• Iterator supplies the values for a given key to the Reduce function.
• Typically a function that:
− Starts with a large number of key/value pairs
− One key/value for each word in all files being greped (including multiple entries
for the same word)
− Ends with very few key/value pairs
− One key/value for each unique word across all the files with the number of
instances summed into this entry
• Broken up so a given worker works with input of the same key.
42. Why is this approach better?
• Creates an abstraction for dealing with complex
overhead
− The computations are simple, the overhead is messy
• Removing the overhead makes programs much smaller
and thus easier to use
− Less testing is required as well. The MapReduce libraries can
be assumed to work properly, so only user code needs to be
tested
• Division of labor also handled by the MapReduce
libraries, so programmers only need to focus on the
actual computation
43. MapReduce Advantages
• Automatic Parallelization:
− Depending on the size of RAW INPUT DATA instantiate
multiple MAP tasks
− Similarly, depending upon the number of intermediate <key,
value> partitions instantiate multiple REDUCE tasks
• Run-time:
− Data partitioning
− Task scheduling
− Handling machine failures
− Managing inter-machine communication
• Completely transparent to the programmer/analyst/user
44. MapReduce: A step backwards?
• Don’t need 1000 nodes to process petabytes:
− Parallel DBs do it in fewer than 100 nodes
• No support for schema:
− Sharing across multiple MR programs difficult
• No indexing:
− Wasteful access to unnecessary data
• Non-declarative programming model:
− Requires highly-skilled programmers
• No support for JOINs:
− Requires multiple MR phases for the analysis
45. MapReduce VS Parallel DB
• Web application data is inherently distributed on a large number of
sites:
− Funneling data to DB nodes is a failed strategy
• Distributed and parallel programs difficult to develop:
− Failures and dynamics in the cloud
• Indexing:
− Sequential Disk access 10 times faster than random access.
− Not clear if indexing is the right strategy.
• Complex queries:
− DB community needs to JOIN hands with MR
46. NoSQL Movement
• Initially used for: “Open-Source relational database that did not expose
SQL interface”
• Popularly used for: “non-relational, distributed data stores that often
did not attempt to provide ACID guarantees”
• Gained widespread popularity through a number of open source
projects
− HBase, Cassandra, MongDB, Redis, …
• Scale-out, elasticity, flexible data model, high availability
47. Data in Real World
• There real data sets that don’t make sense in the
relational model, nor modern ACID databases.
• Fit what into where?
− Trees
− Semi-structured data
− Web content
− Multi-dimensional cubes
− Graphs
48. NoSQL Database Technology
• Not only SQL
− No schema, more dynamic data model
− Denormalizing, no join
− CAP theory
− Auto-sharding (elasticity)
− Distributed query support
− Integrated caching
50. Key Value Stores
• Key-Valued data model
− Key is the unique identifier
− Key is the granularity for consistent access
− Value can be structured or unstructured
• Gained widespread popularity
− In house: Bigtable (Google), PNUTS (Yahoo!), Dynamo
(Amazon)
− Open source: HBase, Hypertable, Cassandra, Voldemort
• Popular choice for the modern breed of web-applications
51. Cassandra – A NoSQL Database
• An open source, distributed store for structured data
that scales-out on cheap, commodity hardware
• Simplicity of Operations
• Transparency
• Very High Availability
• Painless Scale-Out
• Solid, Predictable Performance on Commodity and
Cloud Servers
53. Column Oriented – Data Structure
• Tuples: {“key”: {“name”: “value”: “timestamp”} }
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 })
Row Key
jim
age: 36
2011/01/01 12:35
car: camaro
2011/01/01
12:35
gender: M
2011/01/01
12:35
carol
age: 37
2011/01/01 12:35
car: subaru
2011/01/01
12:35
gender: F
2011/01/01
12:35
johnny
age: 12
2011/01/01 12:35
gender: M
2011/01/01
12:35
suzy
age: 10
2011/01/01 12:35
gender: F
2011/01/01
12:35
54. Massively Parallel Processing (MPP) DB
• Vertica (HP)
• Greenplum (EMC)
• Netezza (IBM)
• Teradata (NCR)
• Kognitio
− In memory analytic
− No need for data partition or indexing
− Scans data in excess of 650 million rows per second per server. Linear
scalability means 100 nodes can scan over 650 billion rows per
second!
55. Vertica
• Supports logical relational models, SQL, ACID transactions, JDBC
• Columnar Store Architecture
− 50x--‐1000x faster by eliminating costly disk IO
− offers aggressive data compression to reduce storage costs by up to 90%
• 20x – 100x faster than traditional RDBMS data warehouse, runs on commodity
hardware
• Scale-out MPP Architecture
• Real-time loading and querying
• In-Database Analytics
• Automatic high availability
• Natively support grid computing
• Natively support MapReduce and Hadoop
56. Machine Learning
• Machine learning systems automate decision making on
data, automatically producing outputs like product
recommendations or groupings.
• WEKA - a Java-based framework and GUI for machine
learning algorithms.
• Mahout - an open source framework that can run
common machine learning algorithms on massive
datasets.
59. References
• Big data: The next frontier for innovation, competition and
productivity, McKinsey Global Institute, May 2011
• Understanding Big Data, IBM, 2012
• NoSQL Database Technology Whitepaper, CouchBase
• Big Data and Cloud Computing: Current State and Future
Opportunities, 2011
• Hadoop Definitive Guide
• How Do I Cassandra, Nov 2011
• BigDataUniversity.com
• youtube.com/ibmetinfo
• ……