Big data
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This document discusses big data concepts including the 5 V's of big data (volume, velocity, variety, veracity, and value). It covers big data storage sizes from megabytes to zettabytes and the processes of collecting, discovering, processing, and managing data. Key challenges discussed are handling different data types at large scales, timeliness, privacy and security. The document also outlines developing analytics tools and models, business intelligence, and storage considerations. It describes analyzing data to find patterns and derive meaning to make decisions. Popular big data technologies like Hadoop, MapReduce, R and Cassandra are listed. The document emphasizes extracting information from data using analytics to predict outcomes. It recommends combining Hadoop and R using Rhadoop
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to effectively analyze this kind of information is now seen as a key competitive advantage to better inform decisions. In order to do so, organizations employ Sentiment Analysis (SA) techniques on these data. However, the usage of social media around the world is ever-increasing, which considerably accelerates massive data generation and makes traditional SA systems unable to deliver useful insights. Such volume of data can be efficiently analyzed using the combination of SA techniques and Big Data technologies. In fact, big data is not a luxury but an essential necessary to make valuable predictions. However, there are some challenges associated with big data such as quality that could highly affect the SA systems’ accuracy that use huge volume of data. Thus, the quality aspect should be addressed in order to build reliable and credible systems. For this, the goal of our research work is to consider Big Data Quality Metrics (BDQM) in SA that rely of big data. In this paper, we first highlight the most eloquent BDQM that should be considered throughout the Big Data Value Chain (BDVC) in any big data project. Then, we measure the impact of BDQM on a novel SA method accuracy in a real case study by giving simulation results.
Dm1.1
The document discusses data mining and provides details on key aspects of the data mining process. It describes how data mining is used to extract knowledge from data and identifies the main steps in the knowledge discovery process as data cleaning, integration, selection, transformation, mining, pattern evaluation and presentation. It also outlines different types of data that can be mined, including relational databases, data warehouses, transactional data, and advanced database systems. Common data mining techniques are discussed like classification, clustering, association rule mining and anomaly detection.
Big Data Fabric Capability Maturity Model
The document outlines the evolution of an organization's data and analytics capabilities from initial siloed and inconsistent usage to a mature and world-class "data-driven" state. Key stages included establishing data stewardship, developing a data value council, implementing a metadata catalogue and defining data quality, and building out an enhanced "Big Data Fabric" platform to support predictive and prescriptive analytics as well as artificial intelligence applications. The organization ultimately reached a world-class level where data drives business value through insights and automation, and the data team coaches others to become "Data Citizens."
Big data, Hadoop and Hive
This document discusses big data and Hadoop. It begins by defining big data and explaining the volume, variety, and velocity of data being generated. It then provides an overview of Hadoop, including its origins, architecture, core components of HDFS and MapReduce, and Hadoop ecosystems like Hive. HDFS provides a distributed file system for large data storage, while MapReduce allows processing of large datasets in parallel across clusters. Hive allows querying and analysis of large datasets stored in Hadoop. Overall, the document serves as an introduction to big data concepts and the Hadoop framework for distributed storage and processing of large and diverse datasets.
Data science
This document provides an overview of data science. It discusses the history of data science and how it has evolved with larger amounts of diverse data available. Specifically, it notes that data science now focuses on providing actionable insights from data rather than just exposing raw data. It also defines key concepts in data science like data mining, statistics, and the types of data involved. Finally, it outlines the common techniques, tools, and applications of data science, such as machine learning, visualization, and using data science to improve customer experiences.
Big data unit 2
Big data analytics tools from vendors like IBM, Tableau, and SAS can help organizations process and analyze big data. For smaller organizations, Excel is often used, while larger organizations employ data mining, predictive analytics, and dashboards. Business intelligence applications include OLAP, data mining, and decision support systems. Big data comes from many sources like web logs, sensors, social networks, and scientific research. It is defined by the volume, variety, velocity, veracity, variability, and value of the data. Hadoop and MapReduce are common technologies for storing and analyzing big data across clusters of machines. Stream analytics is useful for real-time analysis of data like sensor data.
Introduction to Big Data
Big data refers to large volumes of structured and unstructured data that can be analyzed to reveal patterns and trends. It is characterized by 3 Vs - volume, velocity, and variety. Hadoop and associated tools like HDFS, MapReduce, Hive and NoSQL databases are used to handle big data. These tools provide scalability, flexibility and support both structured and unstructured data. Understanding big data analytics provides opportunities in data science and IT jobs and benefits industries like banking, healthcare, manufacturing and more through real-time insights.
Key-Value NoSQL Database
This document discusses different types of distributed databases. It covers data models like relational, aggregate-oriented, key-value, and document models. It also discusses different distribution models like sharding and replication. Consistency models for distributed databases are explained including eventual consistency and the CAP theorem. Key-value stores are described in more detail as a simple but widely used data model with features like consistency, scaling, and suitable use cases. Specific key-value databases like Redis, Riak, and DynamoDB are mentioned.
Big data & hadoop
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.
The Hadoop Ecosystem for Developers
This document provides an overview of the Hadoop ecosystem. It begins with introducing big data challenges around volume, variety, and velocity of data. It then introduces Hadoop as an open-source framework for distributed storage and processing of large datasets across clusters of computers. The key components of Hadoop are HDFS (Hadoop Distributed File System) for distributed storage and high throughput access to application data, and MapReduce as a programming model for distributed computing on large datasets. HDFS stores data reliably using data replication across nodes and is optimized for throughput over large files and datasets.
1 PSUT Big Data Class, introduction
This document provides an introduction to big data, including definitions and key concepts. It discusses the volume, velocity, and variety of big data sources and challenges in storage and processing large amounts of data from many different sources. Examples of big data applications are given in several domains like healthcare, retail, and social media. Distributed systems and architectures like Hadoop and MapReduce are introduced as approaches to analyze huge volumes of data across multiple servers and storage areas. Challenges in distributed systems and how big data is driving new technologies are also summarized.
How to build a data stack from scratch
This document outlines the layers of a data stack from data generation to data visualization. It discusses topics like data collection and transport protocols, data storage formats and technologies, data processing paradigms like MapReduce and streaming, data analysis techniques including aggregation and machine learning, and data visualization software. Industry examples of implementing components of the data stack at companies like Akamai, Inmobi, and Helpshift are also provided.
Combining Data Mining and Machine Learning for Effective User Profiling
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
Data mining techniques unit 1
This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.
Bigdata
Big data analytics involves collecting and analyzing large, complex datasets. There are three key aspects of big data: volume, referring to the large size of datasets; velocity, meaning the speed of data input and processing; and variety, the different data types including text, audio, video and more. Hadoop is an open-source framework that allows processing and querying vast amounts of data across clusters of computers. It uses HDFS for distributed storage and MapReduce as a processing paradigm to break work into parallelized chunks. R can be used with Hadoop for advanced analytics and visualization of large datasets stored in Hadoop.
Что такое Data Science
Data science involves collecting and analyzing large amounts of data to discover patterns and make predictions. It is an interdisciplinary field that uses techniques from mathematics, statistics, machine learning, and domain expertise. The key steps in a data science project are to explore the data through preprocessing, visualization, and modeling techniques; build a model using methods like machine learning algorithms, clustering, or decision trees; and apply the model to make predictions or other insights. Popular tools for data science include R, Python, and packages within them for statistical analysis, machine learning, and data visualization.
Architectures styles and deployment on the hadoop
This document discusses architectural patterns and best practices for big data and Hadoop. It covers why enterprises are rethinking their data strategies, modernizing data warehouses, and important design considerations like decoupling systems and using hybrid architectures. Best practices for data pipelines, storage, processing and analytics are provided. Key criteria for selecting tools, file formats, databases and machine learning approaches are outlined. The document also discusses data modeling in Hadoop, typical directory structures, and partitioning strategies.
Big data Intro - Presentation to OCHackerz Meetup Group
Introduction to Big Data
Hadoop ,MapReduce – Storage, Processing
Machine Learning – Analytics
Visualization
Big data by Mithlesh sadh
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
MIS: Business Intelligence
This document discusses big data and business intelligence. It defines big data as large volumes of varied data that is collected and processed rapidly. It outlines different types of primary BI systems including reporting systems, data mining systems, knowledge management systems, and expert systems. It also discusses challenges with raw data and the need for data warehousing to extract, clean, and prepare data from different sources for BI processing and analysis. Finally, it provides an example of how a mountain resort (MRV) could use BI and data warehousing to develop a data storage plan, generate reports on repeat business, identify high-value customers, and analyze equipment usage.
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Combining Data Mining and Machine Learning for Effective User Profiling
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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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.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Big data
- 4. •MEGA BYTE •GIGA BYTE •TERA BYTE •PETA BYTE •EXTRA BYTE •ZETTA BYTE
- 6. •How to handle Heterogeneity •Scalability (volume) •Structured / Semi structured/ Unstructured data handling •Timeliness response •Handle privacy and security issues
- 7. •Development of analytics •Design & develop appropriate data models •Developments of tools •Creating Data warehouse & Mining techniques •Business intelligence •Storage consideration
- 8. •Variety of Formats •Ambiguity of Data •Abstracted layer of Hierarchy •Lack of Meta Data
- 9. •Finding patterns • Deriving meaning • Making decisions • Responding to the world with intelligence
- 10. •Lexical Analysis •Syntactic Analysis •Semantic Analysis •Sentimental Analysis
- 11. •Big Table •Business Intelligence •Cassandra •Hadoop and MapReduce •R
- 12. •Deals with extracting information from data •Use this information to predict outcomes •Find patterns that predict similar outcome in future
- 13. •Hadoop and Mapreduce •Integrating R and Hadoop •Tools like RHIPE and RHadoop
- 14. By combining capability of Hadoop and R by using Rhadoop tool the performance and accuracy of data analytics can be increased
- 15. THANKS
- 16. Queries ?