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Big data is a term that refers to a collection of large and complicated data sets as well as a large amount of data, data management capabilities, social media analytics, and real-time data. https://www.learnbay.co/data-science-course/data-science-courses-in-hyderabad/
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This document provides an overview of big data, including definitions of key terms like data, big data, and examples of big data. It describes why big data is important, how big data analytics works, and the benefits it provides. It outlines different types of big data like structured, unstructured, and semi-structured data. It also discusses characteristics of big data like volume, velocity, variety, and veracity. Additionally, it identifies primary sources of big data and examples of big data tools and software. Finally, it briefly discusses how big data and machine learning are related and how AI can be used to enhance big data analytics.
Identify and analyze the greatest insights from big data
Identify and analyze the greatest insights from big data by leveraging technologies that can effectively handle the growing volume, velocity, and variety of data.
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The document proposes MphasiS AGIL Analytics Life Cycle Business Style (MAALBS) as a framework for providing big data services. MAALBS follows a phased approach including requirements gathering, design, development, testing and deployment. It aims to help clients extract value from large, diverse datasets in a cost-effective manner while ensuring quality. The framework is intended to address challenges from big data's high volume, velocity and variety by standardizing processes around verification, validation and delivering business value. MphasiS believes MAALBS can help organizations accelerate innovation, lower costs and ensure high quality for big data projects.
Bigdata analysis in supply chain managment
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Know The What, Why, and How of Big Data_.pdf
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
Choosing the Right Big Data Architecture for your Business
This document discusses choosing the right data architecture for big data projects. It begins by acknowledging big data comes in many types, from structured transactional data to unstructured text data. It then presents several big data architectures and platforms that are suitable for different data types and use cases, such as relational databases, NoSQL databases, data grids, and distributed file systems. The document emphasizes that one size does not fit all and the right choice depends on the specific data and business needs.
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This document provides an overview of big data, including definitions of key terms like data, big data, and examples of big data. It describes why big data is important, how big data analytics works, and the benefits it provides. It outlines different types of big data like structured, unstructured, and semi-structured data. It also discusses characteristics of big data like volume, velocity, variety, and veracity. Additionally, it identifies primary sources of big data and examples of big data tools and software. Finally, it briefly discusses how big data and machine learning are related and how AI can be used to enhance big data analytics.
Identify and analyze the greatest insights from big data
Identify and analyze the greatest insights from big data by leveraging technologies that can effectively handle the growing volume, velocity, and variety of data.
Virtual Data Steward: Data Management 3.0
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Bigdata analysis in supply chain managment
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
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Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
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This document discusses choosing the right data architecture for big data projects. It begins by acknowledging big data comes in many types, from structured transactional data to unstructured text data. It then presents several big data architectures and platforms that are suitable for different data types and use cases, such as relational databases, NoSQL databases, data grids, and distributed file systems. The document emphasizes that one size does not fit all and the right choice depends on the specific data and business needs.
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The Road to Data-Driven Decision Making:
1) Enhance Intelligence
2) Delve into Big Data
3) Leverage the Cloud
4) Build for the Future
What's the Big Deal About Big Data?
This document discusses big data and provides an overview of key concepts and technologies. It defines big data as large volumes of data in various formats that are growing rapidly. It describes the four V's of big data - volume, velocity, variety, and value. The document then provides an overview of big data technologies like columnar databases, NoSQL, and Hadoop that are designed to handle large and complex data sets.
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Big data vs datawarehousing
Big data and data warehousing both aim to extract business value from data. Data warehousing stores structured data from internal systems in a centralized location for analysis. Big data handles large, complex, and unstructured data both within and outside the organization. It uses tools for storage, analysis, and visualization of diverse data types. A case study shows how a shoe company uses both approaches - a data warehouse for core transaction data and big data to analyze external sources and predict customer behavior. While different in scope and data sources, both can provide valuable insights when used together.
Big data vs datawarehousing
Big data and data warehousing both aim to extract business value from data. Data warehousing stores structured data from internal systems in a centralized location for analysis. Big data handles large, complex, and unstructured data both within and outside the organization. It uses tools for storage, analysis, and visualization of diverse data types. A case study shows how a shoe company uses both approaches - a data warehouse for core transaction data and big data to analyze external sources and predict customer behavior. While different in scope and data sources, both can provide valuable insights when used together.
Bda assignment can also be used for BDA notes and concept understanding.
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
Connecting and Exploiting Big Data
This document discusses the importance of connecting and exploiting big data through linking various data sources. It argues that simply creating new large data silos does not provide much value, and that true value is derived when numerous sources are brought together to create a single reference point. The document also discusses how linked data provides advantages over traditional big data approaches by structuring data around semantic triples that can interconnect different records and provide a bigger picture view. However, it cautions that linked data also requires rigorous controls and understanding to avoid simply creating another unstructured data source.
Cloud and business agility
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
Big Data Analytics
This document provides an overview of big data analytics, including:
1) It defines big data and describes its key characteristics of variety, velocity, and volume.
2) It outlines common types of big data like structured, unstructured, and semi-structured data.
3) It lists sources of big data such as social media, the cloud, the web, databases, and the Internet of Things.
4) It discusses challenges of big data like rapid growth, storage, security, and integrating diverse data sources.
Introduction to Big Data
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
Guide to big data analytics
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
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This document provides an overview of big data analytics, including:
1) It defines big data and describes its key characteristics of variety, velocity, and volume.
2) It outlines common types of big data like structured, unstructured, and semi-structured data.
3) It lists sources of big data such as social media, the cloud, the web, databases, and the Internet of Things.
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Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
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Big data is a mix of structured, semistructured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects,
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
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