Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
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A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
Big Data Hadoop training at Multisoft Systems imparts skills in effectively using the large set of data for business analytics purpose. Hadoop certification exam can be taken after acquainting the required skills at the training.
This document discusses using machine learning models with HDInsight. It provides an introduction to HDInsight, describing it as a fully managed cloud service that makes it easy to process large amounts of data. It also discusses how HDInsight supports Apache HBase, a NoSQL database modeled after Google BigTable, and Apache Storm, an open-source computation system used to process data streams in real time with Hadoop. Additionally, it describes Apache Spark as providing primitives for fast in-memory computing on clustered systems and how it can be used to load and query data in memory for machine learning models.
The document provides an introduction to big data and Hadoop. It defines big data as large datasets that cannot be processed using traditional computing techniques due to the volume, variety, velocity, and other characteristics of the data. It discusses traditional data processing versus big data and introduces Hadoop as an open-source framework for storing, processing, and analyzing large datasets in a distributed environment. The document outlines the key components of Hadoop including HDFS, MapReduce, YARN, and Hadoop distributions from vendors like Cloudera and Hortonworks.
Open source stak of big data techs open suse asiaMuhammad Rifqi
This document summarizes the key technologies in the open source stack for big data. It discusses Hadoop, the leading open source framework for distributed storage and processing of large data sets. Components of Hadoop include HDFS for distributed file storage and MapReduce for distributed computations. Other related technologies are also summarized like Hive for data warehousing, Pig for data flows, Sqoop for data transfer between Hadoop and databases, and approaches like Lambda architecture for batch and real-time processing. The document provides a high-level overview of implementing big data solutions using open source Hadoop technologies.
This document discusses open source tools for big data analytics. It introduces Hadoop, HDFS, MapReduce, HBase, and Hive as common tools for working with large and diverse datasets. It provides overviews of what each tool is used for, its architecture and components. Examples are given around processing log and word count data using these tools. The document also discusses using Pentaho Kettle for ETL and business intelligence projects with big data.
Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.
Introduction To Big Data Analytics On Hadoop - SpringPeopleSpringPeople
Big data analytics uses tools like Hadoop and its components HDFS and MapReduce to store and analyze large datasets in a distributed environment. HDFS stores very large data sets reliably and streams them at high speeds, while MapReduce allows developers to write programs that process massive amounts of data in parallel across a distributed cluster. Other concepts discussed in the document include data preparation, visualization, hypothesis testing, and deductive vs inductive reasoning as they relate to big data analytics. The document aims to introduce readers to big data analytics using Hadoop and suggests the audience as data analysts, scientists, database managers, and consultants.
This document provides an overview of big data and Hadoop. It introduces big data concepts like the 5 V's of big data and types of big data analytics. It then provides an introduction to Hadoop and describes its master/slave architecture and core components like HDFS (Hadoop Distributed File System), MapReduce, YARN (Yet Another Resource Negotiator). It outlines HDFS concepts such as name node, data node, blocks and fault tolerance. It also gives examples of MapReduce programs and job workflows. Finally, it discusses the Hadoop ecosystem and installation.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
This document provides an overview of big data and Hadoop. It defines big data as large, complex datasets that are difficult to store and process using traditional systems. Examples of big data sources are listed. Hadoop is introduced as an open source framework for distributed processing of large datasets across commodity computers. Key components of Hadoop like HDFS for data storage and MapReduce for parallel processing are described. Reasons for moving to Hadoop include its ability to handle large, unstructured datasets across clusters of servers in a scalable way. The future of data growth and the Hadoop ecosystem are also discussed briefly.
The document summarizes the key components of the big data stack, from the presentation layer where users interact, through various processing and storage layers, down to the physical infrastructure of data centers. It provides examples like Facebook's petabyte-scale data warehouse and Google's globally distributed database Spanner. The stack aims to enable the processing and analysis of massive datasets across clusters of servers and data centers.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
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.
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Mahantesh Angadi
This document provides an introduction to big data and the installation of a single-node Apache Hadoop cluster. It defines key terms like big data, Hadoop, and MapReduce. It discusses traditional approaches to handling big data like storage area networks and their limitations. It then introduces Hadoop as an open-source framework for storing and processing vast amounts of data in a distributed fashion using the Hadoop Distributed File System (HDFS) and MapReduce programming model. The document outlines Hadoop's architecture and components, provides an example of how MapReduce works, and discusses advantages and limitations of the Hadoop framework.
Lecture4 big data technology foundationshktripathy
The document discusses big data architecture and its components. It explains that big data architecture is needed when analyzing large datasets over 100GB in size or when processing massive amounts of structured and unstructured data from multiple sources. The architecture consists of several layers including data sources, ingestion, storage, physical infrastructure, platform management, processing, query, security, monitoring, analytics and visualization. It provides details on each layer and their functions in ingesting, storing, processing and analyzing large volumes of diverse data.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
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.
This document discusses big data analysis using Hadoop and proposes a system for validating data entering big data systems. It provides an overview of big data and Hadoop, describing how Hadoop uses MapReduce and HDFS to process and store large amounts of data across clusters of commodity hardware. The document then outlines challenges in validating big data and proposes a utility that would extract data from SQL and Hadoop databases, compare records to identify mismatches, and generate reports to ensure only correct data is processed.
The document provides an introduction to big data and Hadoop. It defines big data as large datasets that cannot be processed using traditional computing techniques due to the volume, variety, velocity, and other characteristics of the data. It discusses traditional data processing versus big data and introduces Hadoop as an open-source framework for storing, processing, and analyzing large datasets in a distributed environment. The document outlines the key components of Hadoop including HDFS, MapReduce, YARN, and Hadoop distributions from vendors like Cloudera and Hortonworks.
Open source stak of big data techs open suse asiaMuhammad Rifqi
This document summarizes the key technologies in the open source stack for big data. It discusses Hadoop, the leading open source framework for distributed storage and processing of large data sets. Components of Hadoop include HDFS for distributed file storage and MapReduce for distributed computations. Other related technologies are also summarized like Hive for data warehousing, Pig for data flows, Sqoop for data transfer between Hadoop and databases, and approaches like Lambda architecture for batch and real-time processing. The document provides a high-level overview of implementing big data solutions using open source Hadoop technologies.
This document discusses open source tools for big data analytics. It introduces Hadoop, HDFS, MapReduce, HBase, and Hive as common tools for working with large and diverse datasets. It provides overviews of what each tool is used for, its architecture and components. Examples are given around processing log and word count data using these tools. The document also discusses using Pentaho Kettle for ETL and business intelligence projects with big data.
Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.
Introduction To Big Data Analytics On Hadoop - SpringPeopleSpringPeople
Big data analytics uses tools like Hadoop and its components HDFS and MapReduce to store and analyze large datasets in a distributed environment. HDFS stores very large data sets reliably and streams them at high speeds, while MapReduce allows developers to write programs that process massive amounts of data in parallel across a distributed cluster. Other concepts discussed in the document include data preparation, visualization, hypothesis testing, and deductive vs inductive reasoning as they relate to big data analytics. The document aims to introduce readers to big data analytics using Hadoop and suggests the audience as data analysts, scientists, database managers, and consultants.
This document provides an overview of big data and Hadoop. It introduces big data concepts like the 5 V's of big data and types of big data analytics. It then provides an introduction to Hadoop and describes its master/slave architecture and core components like HDFS (Hadoop Distributed File System), MapReduce, YARN (Yet Another Resource Negotiator). It outlines HDFS concepts such as name node, data node, blocks and fault tolerance. It also gives examples of MapReduce programs and job workflows. Finally, it discusses the Hadoop ecosystem and installation.
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
This document provides an overview of big data and Hadoop. It defines big data as large, complex datasets that are difficult to store and process using traditional systems. Examples of big data sources are listed. Hadoop is introduced as an open source framework for distributed processing of large datasets across commodity computers. Key components of Hadoop like HDFS for data storage and MapReduce for parallel processing are described. Reasons for moving to Hadoop include its ability to handle large, unstructured datasets across clusters of servers in a scalable way. The future of data growth and the Hadoop ecosystem are also discussed briefly.
The document summarizes the key components of the big data stack, from the presentation layer where users interact, through various processing and storage layers, down to the physical infrastructure of data centers. It provides examples like Facebook's petabyte-scale data warehouse and Google's globally distributed database Spanner. The stack aims to enable the processing and analysis of massive datasets across clusters of servers and data centers.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
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.
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Mahantesh Angadi
This document provides an introduction to big data and the installation of a single-node Apache Hadoop cluster. It defines key terms like big data, Hadoop, and MapReduce. It discusses traditional approaches to handling big data like storage area networks and their limitations. It then introduces Hadoop as an open-source framework for storing and processing vast amounts of data in a distributed fashion using the Hadoop Distributed File System (HDFS) and MapReduce programming model. The document outlines Hadoop's architecture and components, provides an example of how MapReduce works, and discusses advantages and limitations of the Hadoop framework.
Lecture4 big data technology foundationshktripathy
The document discusses big data architecture and its components. It explains that big data architecture is needed when analyzing large datasets over 100GB in size or when processing massive amounts of structured and unstructured data from multiple sources. The architecture consists of several layers including data sources, ingestion, storage, physical infrastructure, platform management, processing, query, security, monitoring, analytics and visualization. It provides details on each layer and their functions in ingesting, storing, processing and analyzing large volumes of diverse data.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
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.
This document discusses big data analysis using Hadoop and proposes a system for validating data entering big data systems. It provides an overview of big data and Hadoop, describing how Hadoop uses MapReduce and HDFS to process and store large amounts of data across clusters of commodity hardware. The document then outlines challenges in validating big data and proposes a utility that would extract data from SQL and Hadoop databases, compare records to identify mismatches, and generate reports to ensure only correct data is processed.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and processes large amounts of data in parallel using MapReduce. The core components of Hadoop are HDFS for storage, MapReduce for processing, and YARN for resource management. Hadoop allows for scalable and cost-effective solutions to various big data problems like storage, processing speed, and scalability by distributing data and computation across clusters.
The document discusses analyzing temperature data using Hadoop MapReduce. It describes importing a weather dataset from the National Climatic Data Center into Eclipse to create a MapReduce program. The program will classify days in the Austin, Texas data from 2015 as either hot or cold based on the recorded temperature. The steps outlined are: importing the project, exporting it as a JAR file, checking that the Hadoop cluster is running, uploading the input file to HDFS, and running the JAR file with the input and output paths specified. The goal is to analyze temperature variation and find the hottest/coldest days of the month/year from the large climate dataset.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
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
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
The document provides an overview of Hadoop, including:
- What Hadoop is and its core modules like HDFS, YARN, and MapReduce.
- Reasons for using Hadoop like its ability to process large datasets faster across clusters and provide predictive analytics.
- When Hadoop should and should not be used, such as for real-time analytics versus large, diverse datasets.
- Options for deploying Hadoop including as a service on cloud platforms, on infrastructure as a service providers, or on-premise with different distributions.
- Components that make up the Hadoop ecosystem like Pig, Hive, HBase, and Mahout.
This document provides an overview of Hadoop and Big Data. It begins with introducing key concepts like structured, semi-structured, and unstructured data. It then discusses the growth of data and need for Big Data solutions. The core components of Hadoop like HDFS and MapReduce are explained at a high level. The document also covers Hadoop architecture, installation, and developing a basic MapReduce program.
Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects. Systems that process and store big data have become common in organizations, combined with tools that support big data analytics uses such as improving operations, providing better customer performance, and creating personalized marketing campaigns. Hadoop is an open-source framework for distributed storage and processing of large data sets across clusters of computers. It includes projects like HDFS, MapReduce, YARN, and common utilities.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It was developed based on Google papers describing Google File System (GFS) for reliable distributed data storage and MapReduce for distributed parallel processing. Hadoop uses HDFS for storage and MapReduce for processing in a scalable, fault-tolerant manner on commodity hardware. It has a growing ecosystem of projects like Pig, Hive, HBase, Zookeeper, Spark and others that provide additional capabilities for SQL queries, real-time processing, coordination services and more. Major vendors that provide Hadoop distributions include Hortonworks and Cloudera.
This document provides an overview of Hadoop, including:
- Prerequisites for getting the most out of Hadoop include programming skills in languages like Java and Python, SQL knowledge, and basic Linux skills.
- Hadoop is a software framework for distributed processing of large datasets across computer clusters using MapReduce and HDFS.
- Core Hadoop components include HDFS for storage, MapReduce for distributed processing, and YARN for resource management.
- The Hadoop ecosystem also includes components like HBase, Pig, Hive, Mahout, Sqoop and others that provide additional functionality.
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It reliably stores and processes gobs of information across many commodity computers. Key components of Hadoop include the HDFS distributed file system for high-bandwidth storage, and MapReduce for parallel data processing. Hadoop can deliver data and run large-scale jobs reliably in spite of system changes or failures by detecting and compensating for hardware problems in the cluster.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
At APTRON Delhi, we believe in hands-on learning. That's why our Hadoop training in Delhi is designed to give you practical experience working with Hadoop. You'll work on real-world projects and learn from experienced instructors who have worked with Hadoop in the industry.
https://bit.ly/3NnvsHH
The Hadoop tutorial is a comprehensive guide on Big Data Hadoop that covers what is Hadoop, what is the need of Apache Hadoop, why Apache Hadoop is most popular, How Apache Hadoop works?
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul
Artificial intelligence is changing how businesses operate. Companies are using AI agents to automate tasks, reduce time spent on repetitive work, and focus more on high-value activities. Noah Loul, an AI strategist and entrepreneur, has helped dozens of companies streamline their operations using smart automation. He believes AI agents aren't just tools—they're workers that take on repeatable tasks so your human team can focus on what matters. If you want to reduce time waste and increase output, AI agents are the next move.
TrsLabs - Fintech Product & Business ConsultingTrs Labs
Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity
This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots.
📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In Francechb3
Big Data Hadoop Technology
1. Big Data Hadoop
Presented by:- Rahul Sharma
B-Tech(Cloud Technology) 2nd year
Poornima University (I.Nurture)
2. What is
Hadoop?
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 tasks or jobs.
3. What is the
use of Hadoop
technology?
Hadoop is an open source, Java-based programming
framework that supports the processing and storage of
extremely large data sets in a distributed computing
environment. It is part of the Apache project sponsored by the
Apache Software Foundation
4. Why is
Hadoop
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.
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.
5. What are the
challenges
of using
Hadoop?
Full-fledged data management and governance- Hadoop does
not have easy-to-use, full-feature tools for data management,
data cleansing, governance and metadata. Especially lacking
are tools for data quality and standardization.
Data security- Another challenge centers around the
fragmented data security issues, though new tools and
technologies are surfacing. The Kerberos authentication
protocol is a great step toward making Hadoop environments
secure.
6. How Is
Hadoop Being
Used?
Low-cost storage and data archive- The modest cost of commodity
hardware makes Hadoop useful for storing and combining data
such as transactional, social media, sensor, machine, scientific,
click streams, etc. The low-cost storage lets you keep information
that is not deemed currently critical but that you might want to
analyze later.
IoT and Hadoop- Things in the IoT need to know what to
communicate and when to act. At the core of the IoT is a
streaming, always on torrent of data. Hadoop is often used as the
data store for millions or billions of transactions. You can then
continuously improve these instructions, because Hadoop is
constantly being updated with new data that doesn’t match
previously defined patterns.
7. How Is
Hadoop Being
Used?
Complement your data warehouse- We're now seeing Hadoop
beginning to sit beside data warehouse environments, as well as
certain data sets being offloaded from the data warehouse into
Hadoop or new types of data going directly to Hadoop. The end
goal for every organization is to have a right platform for storing
and processing data of different schema, formats, etc. to support
different use cases that can be integrated at different levels.
8. How It Works
and a Hadoop
Glossary.
"Currently, four core modules are included in the basic framework
from the Apache Foundation:"
Hadoop Common – the libraries and utilities used by other Hadoop
modules.
Hadoop Distributed File System (HDFS) – the Java-based scalable
system that stores data across multiple machines without prior
organization.
YARN – (Yet Another Resource Negotiator) provides resource
management for the processes running on Hadoop.
MapReduce – a parallel processing software framework. It is
comprised of two steps. Map step is a master node that takes inputs
and partitions them into smaller subproblems and then distributes
them to worker nodes. After the map step has taken place, the
master node takes the answers to all of the subproblems and
combines them to produce output.