Introduction to SARA's Hadoop Hackathon - dec 7th 2010Evert Lammerts
This document summarizes an agenda for the SARA Hadoop Hackathon on December 7, 2010. It provides background on Hadoop and how it relates to earlier technologies like Nutch and MapReduce. It then outlines the agenda for the day which includes introductions, presentations on MapReduce at University of Twente and a kickoff for the hackathon project building period. An optional tour of the SARA facilities is also included. The day will conclude with presentations of hackathon results.
Hadoop is an open-source software platform for distributed storage and processing of large datasets across clusters of computers. It was designed to scale up from single servers to thousands of machines, with very high fault tolerance. The document outlines the history of Hadoop, why it was created, its core components HDFS for storage and MapReduce for processing, and provides an example word count problem. It also includes information on installing Hadoop and additional resources.
Toulouse Data Science meetup - Apache zeppelinGérard Dupont
Apache Zeppelin is a web-based notebook for interactive data analytics. It allows for interactive coding and visualization with out-of-the-box support for Spark integration. Some key features include interactive notebooks, built-in visualization options, and extensibility through additional interpreters and custom visualization. While it is easy to configure and use, installation from source is required for customization and it currently lacks multi-user support.
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon UniversityNodejsFoundation
Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analysing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyse text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.
BigScience is a one-year research workshop involving over 800 researchers from 60 countries to build and study very large multilingual language models and datasets. It was granted 5 million GPU hours on the Jean Zay supercomputer in France. The workshop aims to advance AI/NLP research by creating shared models and data as well as tools for researchers. Several working groups are studying issues like bias, scaling, and engineering challenges of training such large models. The first model, T0, showed strong zero-shot performance. Upcoming work includes further model training and papers.
Introduction to Spark: Or how I learned to love 'big data' after all.Peadar Coyle
Slides from a talk I will give in early 2016 at the Luxembourg Data Science Meetup. Aim is to give an introduction to Apache Spark, from a Machine Learning experts point of view. Based on various other tutorials out there. This will be aimed at non-specialists.
“BIG DATA” is data that is big in
volume
velocity and
Variety
“TODAY’S BIG MAY BE TOMMOROW’S NORMAL”
Varieties deals with a wide range of data types
Structured data - RDMS
Semi – structured data – HTML,XML
Unstructured data – audios, videos, emails, photos, pdf, social media
hadoop
It was created by DOUG CUTTING and MICHEAL CAFARELLA in 2005
2003 – NUTCH open source search engine( lucene ,sphinx ,etc…)
(google published some papers mentioning about DFS and MAP REDUCE)
After yahoo took this initiative step
Then the creation of hadoop took place
Hadoop 0.1.0 was relesed april 2006
As of now hadoop 2.8 is available
This document provides an overview of big data and Hadoop. It introduces big data concepts and architectures, describes the Hadoop ecosystem including its core components of HDFS and MapReduce. It also provides an example of how MapReduce works for a word count problem, splitting the documents, mapping to count word frequencies, and reducing to sum the counts. The document aims to give the reader an understanding of big data and how Hadoop is used for distributed storage and processing of large datasets.
This document discusses how to deal with nested lists in R using the purrr, furrr, and future packages. It summarizes working with nested list data from a JSON response, including adding custom IDs to nested data frames and parallelizing the process using future_map to speed it up. Anonymous functions are also discussed as they are used with the apply functions in purrr, and examples are provided of their syntax.
CityLABS Workshop: Working with large tablesEnrico Daga
This document discusses working with large tables and big data processing. It introduces distributed computing as an approach to process large datasets by distributing data across multiple nodes and parallelizing operations. The document then outlines using Apache Hadoop and the MK Data Hub cluster to distribute data storage and processing. It demonstrates how to use tools like Hue, Hive, and Pig to analyze tabular data in a distributed manner at scale. Finally, hands-on examples are provided for computing TF-IDF statistics on the large Gutenberg text corpus.
Analysis of historical movie data by BHADRABhadra Gowdra
Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Data engineering and analytics using pythonPurna Chander
This document provides an overview of data engineering and analytics using Python. It discusses Jupyter notebooks and commonly used Python modules for data science like Pandas, NumPy, SciPy, Matplotlib and Seaborn. It describes Anaconda distribution and the key features of Pandas including data loading, structures like DataFrames and Series, and core operations like filtering, mapping, joining, sorting, cleaning and grouping. It also demonstrates data visualization using Seaborn and a machine learning example of linear regression.
Apache Hive provides a SQL-like interface to query and manipulate data stored in Hadoop, while Apache Pig provides a scripting language to define data flows and transformations. Hive is better suited for business intelligence analysis and ad-hoc queries through its familiar SQL interface, while Pig is more appropriate for data pipelines, iterative data processing, and research through its scripting capabilities. Both can perform similar functions but may differ in performance depending on the use case.
Making Machine Learning Scale: Single Machine and DistributedTuri, Inc.
This document summarizes machine learning scalability from single machine to distributed systems. It discusses how true scalability is about how long it takes to reach a target accuracy level using any available hardware resources. It introduces GraphLab Create and SFrame/SGraph for scalable machine learning and graph processing. Key points include distributed optimization techniques, graph partitioning strategies, and benchmarks showing GraphLab Create can solve problems faster than other systems by using fewer machines.
• What is MapReduce?
• What are MapReduce implementations?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Beyond Kaggle: Solving Data Science Challenges at ScaleTuri, Inc.
This document summarizes a presentation on entity resolution and data deduplication using Dato toolkits. It discusses key concepts like entity resolution, challenges in entity resolution like missing data and data integration from multiple sources, and provides an example dataset of matching Amazon and Google products. It also outlines the preprocessing steps, describes using a nearest neighbors algorithm to find duplicate records, and shares some resources on entity resolution.
Tech Talk - Underutilized Resources in Distributed SystemRishabh Dugar
This document discusses using underutilized distributed computing resources and the Chord protocol. It first introduces the problem of processing growing data and costs of hardware. It then defines distributed systems and describes MapReduce for parallel processing. The document outlines the Chord protocol for distributed lookup, including the finger table, successor list, consistent hashing, and Chord ring. It notes that Chord lookup scales as O(log n). Finally, it mentions comparing Chord to Hadoop with and without node churn.
Open source databases like MySQL, PostgreSQL, and Berkeley DB are flexible alternatives to proprietary databases. PostGIS extends PostgreSQL with spatial database capabilities for storing and querying geographic data and objects. It implements OpenGIS standards and provides functions for spatial indexing, analysis, and data access. PostGIS allows integrating geographic data into web and desktop applications and is used successfully in various real-world GIS projects and systems.
The document discusses the core components of Hadoop, including storage, transformation, and analysis using components like HDFS, MapReduce, Tez and Spark. It describes Generation 1 core components as HDFS for storage and MapReduce for processing. HDFS uses a master-slave architecture with the NameNode tracking metadata and DataNodes storing replicated blocks. MapReduce uses mappers to create key-value pairs, a shuffle to group related pairs, and reducers to aggregate pairs for output. Sample MapReduce jobs for word counting and tracking smart phones are provided.
DBPedia past, present and future - Dimitris Kontokostas. Reveals recent developments in the Linked Data and knowledge graphs field and how DBPedia progress with wikipedia data.
The document discusses Dremel, an interactive query system for analyzing large-scale datasets. Dremel uses a columnar data storage format and a multi-level query execution tree to enable fast querying. It evaluates Dremel's performance on interactive queries, showing it can count terms in a field within seconds using 3000 workers, while MapReduce takes hours. Dremel also scales linearly and handles stragglers well. Today, similar systems like Google BigQuery and Apache Drill use Dremel-like techniques for interactive analysis of web-scale data.
This document discusses large-scale data processing using Apache Hadoop at SARA and BiG Grid. It provides an introduction to Hadoop and MapReduce, noting that data is easier to collect, store, and analyze in large quantities. Examples are given of projects using Hadoop at SARA, including analyzing Wikipedia data and structural health monitoring. The talk outlines the Hadoop ecosystem and timeline of its adoption at SARA. It discusses how scientists are using Hadoop for tasks like information retrieval, machine learning, and bioinformatics.
Hive is used at Facebook for data warehousing and analytics tasks on a large Hadoop cluster. It allows SQL-like queries on structured data stored in HDFS files. Key features include schema definitions, data summarization and filtering, extensibility through custom scripts and functions. Hive provides scalability for Facebook's rapidly growing data needs through its ability to distribute queries across thousands of nodes.
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...Cloudera, Inc.
Michael Sun presented on CBS Interactive's use of Hadoop for web analytics processing. Some key points:
- CBS Interactive processes over 1 billion web logs daily from hundreds of websites on a Hadoop cluster with over 1PB of storage.
- They developed an ETL framework called Lumberjack in Python for extracting, transforming, and loading data from web logs into Hadoop and databases.
- Lumberjack uses streaming, filters, and schemas to parse, clean, lookup dimensions, and sessionize web logs before loading into a data warehouse for reporting and analytics.
- Migrating to Hadoop provided significant benefits including reduced processing time, fault tolerance, scalability, and cost effectiveness compared to their
This document provides an overview of big data and Hadoop. It introduces big data concepts and architectures, describes the Hadoop ecosystem including its core components of HDFS and MapReduce. It also provides an example of how MapReduce works for a word count problem, splitting the documents, mapping to count word frequencies, and reducing to sum the counts. The document aims to give the reader an understanding of big data and how Hadoop is used for distributed storage and processing of large datasets.
This document discusses how to deal with nested lists in R using the purrr, furrr, and future packages. It summarizes working with nested list data from a JSON response, including adding custom IDs to nested data frames and parallelizing the process using future_map to speed it up. Anonymous functions are also discussed as they are used with the apply functions in purrr, and examples are provided of their syntax.
CityLABS Workshop: Working with large tablesEnrico Daga
This document discusses working with large tables and big data processing. It introduces distributed computing as an approach to process large datasets by distributing data across multiple nodes and parallelizing operations. The document then outlines using Apache Hadoop and the MK Data Hub cluster to distribute data storage and processing. It demonstrates how to use tools like Hue, Hive, and Pig to analyze tabular data in a distributed manner at scale. Finally, hands-on examples are provided for computing TF-IDF statistics on the large Gutenberg text corpus.
Analysis of historical movie data by BHADRABhadra Gowdra
Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Data engineering and analytics using pythonPurna Chander
This document provides an overview of data engineering and analytics using Python. It discusses Jupyter notebooks and commonly used Python modules for data science like Pandas, NumPy, SciPy, Matplotlib and Seaborn. It describes Anaconda distribution and the key features of Pandas including data loading, structures like DataFrames and Series, and core operations like filtering, mapping, joining, sorting, cleaning and grouping. It also demonstrates data visualization using Seaborn and a machine learning example of linear regression.
Apache Hive provides a SQL-like interface to query and manipulate data stored in Hadoop, while Apache Pig provides a scripting language to define data flows and transformations. Hive is better suited for business intelligence analysis and ad-hoc queries through its familiar SQL interface, while Pig is more appropriate for data pipelines, iterative data processing, and research through its scripting capabilities. Both can perform similar functions but may differ in performance depending on the use case.
Making Machine Learning Scale: Single Machine and DistributedTuri, Inc.
This document summarizes machine learning scalability from single machine to distributed systems. It discusses how true scalability is about how long it takes to reach a target accuracy level using any available hardware resources. It introduces GraphLab Create and SFrame/SGraph for scalable machine learning and graph processing. Key points include distributed optimization techniques, graph partitioning strategies, and benchmarks showing GraphLab Create can solve problems faster than other systems by using fewer machines.
• What is MapReduce?
• What are MapReduce implementations?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Beyond Kaggle: Solving Data Science Challenges at ScaleTuri, Inc.
This document summarizes a presentation on entity resolution and data deduplication using Dato toolkits. It discusses key concepts like entity resolution, challenges in entity resolution like missing data and data integration from multiple sources, and provides an example dataset of matching Amazon and Google products. It also outlines the preprocessing steps, describes using a nearest neighbors algorithm to find duplicate records, and shares some resources on entity resolution.
Tech Talk - Underutilized Resources in Distributed SystemRishabh Dugar
This document discusses using underutilized distributed computing resources and the Chord protocol. It first introduces the problem of processing growing data and costs of hardware. It then defines distributed systems and describes MapReduce for parallel processing. The document outlines the Chord protocol for distributed lookup, including the finger table, successor list, consistent hashing, and Chord ring. It notes that Chord lookup scales as O(log n). Finally, it mentions comparing Chord to Hadoop with and without node churn.
Open source databases like MySQL, PostgreSQL, and Berkeley DB are flexible alternatives to proprietary databases. PostGIS extends PostgreSQL with spatial database capabilities for storing and querying geographic data and objects. It implements OpenGIS standards and provides functions for spatial indexing, analysis, and data access. PostGIS allows integrating geographic data into web and desktop applications and is used successfully in various real-world GIS projects and systems.
The document discusses the core components of Hadoop, including storage, transformation, and analysis using components like HDFS, MapReduce, Tez and Spark. It describes Generation 1 core components as HDFS for storage and MapReduce for processing. HDFS uses a master-slave architecture with the NameNode tracking metadata and DataNodes storing replicated blocks. MapReduce uses mappers to create key-value pairs, a shuffle to group related pairs, and reducers to aggregate pairs for output. Sample MapReduce jobs for word counting and tracking smart phones are provided.
DBPedia past, present and future - Dimitris Kontokostas. Reveals recent developments in the Linked Data and knowledge graphs field and how DBPedia progress with wikipedia data.
The document discusses Dremel, an interactive query system for analyzing large-scale datasets. Dremel uses a columnar data storage format and a multi-level query execution tree to enable fast querying. It evaluates Dremel's performance on interactive queries, showing it can count terms in a field within seconds using 3000 workers, while MapReduce takes hours. Dremel also scales linearly and handles stragglers well. Today, similar systems like Google BigQuery and Apache Drill use Dremel-like techniques for interactive analysis of web-scale data.
This document discusses large-scale data processing using Apache Hadoop at SARA and BiG Grid. It provides an introduction to Hadoop and MapReduce, noting that data is easier to collect, store, and analyze in large quantities. Examples are given of projects using Hadoop at SARA, including analyzing Wikipedia data and structural health monitoring. The talk outlines the Hadoop ecosystem and timeline of its adoption at SARA. It discusses how scientists are using Hadoop for tasks like information retrieval, machine learning, and bioinformatics.
Hive is used at Facebook for data warehousing and analytics tasks on a large Hadoop cluster. It allows SQL-like queries on structured data stored in HDFS files. Key features include schema definitions, data summarization and filtering, extensibility through custom scripts and functions. Hive provides scalability for Facebook's rapidly growing data needs through its ability to distribute queries across thousands of nodes.
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...Cloudera, Inc.
Michael Sun presented on CBS Interactive's use of Hadoop for web analytics processing. Some key points:
- CBS Interactive processes over 1 billion web logs daily from hundreds of websites on a Hadoop cluster with over 1PB of storage.
- They developed an ETL framework called Lumberjack in Python for extracting, transforming, and loading data from web logs into Hadoop and databases.
- Lumberjack uses streaming, filters, and schemas to parse, clean, lookup dimensions, and sessionize web logs before loading into a data warehouse for reporting and analytics.
- Migrating to Hadoop provided significant benefits including reduced processing time, fault tolerance, scalability, and cost effectiveness compared to their
This document provides an overview of the Apache Hadoop ecosystem. It discusses key components like HDFS, MapReduce, YARN, Pig Latin, and performance tuning for MapReduce jobs. HDFS is introduced as the distributed file system that provides high throughput and scalability. MapReduce is described as the framework for distributed processing of large datasets across clusters. YARN is presented as an improvement over the static resource allocation in Hadoop 0.1.x. Pig Latin is demonstrated as a high-level language for expressing data analysis jobs. The document concludes by discussing extensions beyond MapReduce, like iterative processing and indexing approaches.
Experience SQL Server 2017: The Modern Data PlatformBob Ward
This is an overview of SQL Server 2017 and its features and capabilities. You can get the recording at https://youtu.be/qgSEwpaRul0
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.
Hive Training -- Motivations and Real World Use Casesnzhang
Hive is an open source data warehouse systems based on Hadoop, a MapReduce implementation.
This presentation introduces the motivations of developing Hive and how Hive is used in the real world situation, particularly in Facebook.
PASS Summit - SQL Server 2017 Deep DiveTravis Wright
Deep dive into SQL Server 2017 covering SQL Server on Linux, containers, HA improvements, SQL graph, machine learning, python, adaptive query processing, and much much more.
The document provides an overview of distributed computing and related technologies. It discusses the history of distributed computing including local, parallel, grid and distributed computing. It then discusses applications of distributed computing like web indexing and recommendations. The document introduces Hadoop and its core components HDFS and MapReduce. It also discusses related technologies like HBase, Mahout and challenges in designing distributed systems. It provides examples of using Mahout for machine learning tasks like classification, clustering and recommendations.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
What it takes to run Hadoop at Scale: Yahoo! PerspectivesDataWorks Summit
This document discusses considerations for scaling Hadoop platforms at Yahoo. It covers topics such as deployment models (on-premise vs. public cloud), total cost of ownership, hardware configuration, networking, software stack, security, data lifecycle management, metering and governance, and debunking myths. The key takeaways are that utilization matters for cost analysis, hardware becomes increasingly heterogeneous over time, advanced networking designs are needed to avoid bottlenecks, security and access management must be flexible, and data lifecycles require policy-based management.
The document discusses using Hadoop and Hive at Zing for log collecting, analyzing, and reporting. It provides an overview of Hadoop and Hive and how they are used at Zing to store and analyze large amounts of log and user data in a scalable, fault-tolerant manner. A case study is presented that describes how Zing evolved its log analysis system from using MySQL to using Scribe, Hadoop, and Hive to more efficiently collect, transform, analyze and report on log data.
Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者:Informatica 資深產品顧問 | 尹寒柏
議題簡介:Big Data 時代,比的不是數據數量,而是了解數據的深度。現在,因為 Big Data 技術的成熟,讓非資訊背景的 CXO 們,可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞,從 BI 進入 CI,更連結消費者經濟的脈動,洞悉顧客的意圖。不過,有個 Big Data 時代要 注意的思維,那就是競爭到最後,不單只是看數據量的增長,還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度,Informatica 也有能力提供更快速彙集數據技術,從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案,是精誠集團在 Big Data 時代的最佳工具。
This document discusses big data and Hadoop. It defines big data as large datasets that are difficult to process using traditional methods due to their volume, variety, and velocity. Hadoop is presented as an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage and MapReduce as a programming model for distributed processing. A number of other technologies in Hadoop's ecosystem are also described such as HBase, Avro, Pig, Hive, Sqoop, Zookeeper and Mahout. The document concludes that Hadoop provides solutions for efficiently processing and analyzing big data.
Big Data Essentials meetup @ IBM Ljubljana 23.06.2015Andrey Vykhodtsev
The document discusses big data concepts and Hadoop technologies. It provides an overview of massive parallel processing and the Hadoop architecture. It describes common processing engines like MapReduce, Spark, Hive, Pig and BigSQL. It also discusses Hadoop distributions from Hortonworks, Cloudera and IBM along with stream processing and advanced analytics on Hadoop platforms.
6° Sessione - Ambiti applicativi nella ricerca di tecnologie statistiche avan...Jürgen Ambrosi
In questa sessione vedremo, con il solito approccio pratico di demo hands on, come utilizzare il linguaggio R per effettuare analisi a valore aggiunto,
Toccheremo con mano le performance di parallelizzazione degli algoritmi, aspetto fondamentale per aiutare il ricercatore nel raggiungimento dei suoi obbiettivi.
In questa sessione avremo la partecipazione di Lorenzo Casucci, Data Platform Solution Architect di Microsoft.
Introduction to Hadoop.
What are Hadoop, MapReeduce, and Hadoop Distributed File System.
Who uses Hadoop?
How to run Hadoop?
What are Pig, Hive, Mahout?
Hive is a data warehouse system built on top of Hadoop that allows users to query large datasets using SQL. It is used at Facebook to manage over 15TB of new data added daily across a 300+ node Hadoop cluster. Key features include using SQL for queries, extensibility through custom functions and file formats, and optimizations for performance like predicate pushdown and partition pruning.
This document provides a high-level overview of Hadoop and big data concepts for DBAs with SQL experience. It introduces key big data terminology like the four V's of big data, and discusses how Hadoop uses HDFS for distributed storage and MapReduce for distributed processing at massive scales. Example use cases like word counting are demonstrated using both SQL Server and the Pig framework in Hadoop.
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
How to use nRF24L01 module with ArduinoCircuitDigest
Learn how to wirelessly transmit sensor data using nRF24L01 and Arduino Uno. A simple project demonstrating real-time communication with DHT11 and OLED display.
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
Raish Khanji GTU 8th sem Internship Report.pdfRaishKhanji
This report details the practical experiences gained during an internship at Indo German Tool
Room, Ahmedabad. The internship provided hands-on training in various manufacturing technologies, encompassing both conventional and advanced techniques. Significant emphasis was placed on machining processes, including operation and fundamental
understanding of lathe and milling machines. Furthermore, the internship incorporated
modern welding technology, notably through the application of an Augmented Reality (AR)
simulator, offering a safe and effective environment for skill development. Exposure to
industrial automation was achieved through practical exercises in Programmable Logic Controllers (PLCs) using Siemens TIA software and direct operation of industrial robots
utilizing teach pendants. The principles and practical aspects of Computer Numerical Control
(CNC) technology were also explored. Complementing these manufacturing processes, the
internship included extensive application of SolidWorks software for design and modeling tasks. This comprehensive practical training has provided a foundational understanding of
key aspects of modern manufacturing and design, enhancing the technical proficiency and readiness for future engineering endeavors.
Sorting Order and Stability in Sorting.
Concept of Internal and External Sorting.
Bubble Sort,
Insertion Sort,
Selection Sort,
Quick Sort and
Merge Sort,
Radix Sort, and
Shell Sort,
External Sorting, Time complexity analysis of Sorting Algorithms.
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek
This presentation provides detailed guidance and tools for conducting Current State and Future State Value Stream Mapping workshops for Intelligent Continuous Security.
3. What is cool?
big data
distributed systems
libs (algorithms, collections, network, multithreading, serialization, ...)
patterns, methodologies, best practices
trends
13. Upcoming presentations...
Distributed caching with HazelCast
Storm - real time stream processing
TDD - myth or good practice.
Handling failures in distributed systems
Serialization for everybody
Test your code. Always.
SQL Server Reporting Services - make your users happy and your
life easier
14. Upcoming presentations...
Reading (un)real-time feeds in Event Platform
Distributed computing and clustering done right
ActiveMQ usage in a SEM's Live Transcript process.
33 things we did wrong. EP lesson learned.
Who do it better? GitFlow implemented in EP and SEM.
Why Kafka is a standard?
20. NoSQL (often interpreted as Not only SQL[1][2]) database provides a
mechanism for storage and retrieval of data that is modeled in means other
than the tabular relations used in relational databases
30. In 2006, Cutting went to work with Yahoo, which was
equally impressed by the Google File System and
MapReduce papers and wanted to build open source
technologies based on them
31. The transformation into Hadoop being “behind every click”
(or every batch process, technically) at Yahoo was pretty
much complete by 2008
32. By the time Yahoo spun out Hortonworks into a separate,
Hadoop-focused software company in 2011, Yahoo’s
Hadoop infrastructure consisted of 42,000 nodes and
hundreds of petabytes of storage
42. Hive is a data warehousing infrastructure based on
Hadoop. Hadoop provides massive scale out and fault
tolerance capabilities for data storage and processing
43. Example
CREATE TABLE page_view(viewTime INT, userid BIGINT,
page_url STRING, referrer_url STRING,
ip STRING COMMENT 'IP Address of the User')
COMMENT 'This is the page view table'
PARTITIONED BY(dt STRING, country STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '1'
STORED AS SEQUENCEFILE;
44. Example
SELECT pv.*, u.gender, u.age, f.friends
FROM page_view pv JOIN user u ON (pv.userid = u.id) JOIN
friend_list f ON (u.id = f.uid)
WHERE pv.date = '2008-03-03';
47. Pig is a high level scripting language that is used with
Apache Hadoop. Pig excels at describing data analysis
problems as data flows. Pig is complete in that you can do
all the required data manipulations in Apache Hadoop with
Pig
48. Example
players = load 'baseball' as (name:chararray, team:chararray,
position:bag{t:(p:chararray)}, bat:map[]);
noempty = foreach players generate name,
((position is null or IsEmpty(position)) ? {('unknown')} :
position)as position;
pos = foreach noempty generate name, flatten(position) as position;
bypos = group pos by position;
49. Example
players = load 'baseball' as (name:chararray, team:chararray,
position:bag{t:(p:chararray)}, bat:map[]);
noempty = foreach players generate name,
((position is null or IsEmpty(position)) ? {('unknown')} :
position)as position;
pos = foreach noempty generate name, flatten(position) as position;
bypos = group pos by position;
53. When Would I Use Apache HBase?
Use Apache HBase™ when you need random, realtime read/write access to your
Big Data. This project's goal is the hosting of very large tables -- billions of rows X
millions of columns -- atop clusters of commodity hardware
#2: na poczatek troche was zmecze…
odpowiemy sobie na kilka pytan…
wiem, jakbyscie wiedzieli ze beda pytania, byscie nie przyszli…, dlatego dopiero teraz mowie
#5: show ourselves outside the company,
uwazacie ze nie ma nic ciekawego do pokazywania?
no tak jak slysze ze testy nie maja sensu ponizej 10k kodu
#6: jezeli nie to sa dwie mozliwosci:
albo nie macie racji
albo cos generalnie jest nie tak
#8: to moze wynikac z roznych rzeczy:
brak dzielenia sie wiedza - kazdy siedzi w swojej piaskownicy, kopie dolek lopatka, a w pokoju obok maja koparke
#11: 1.wy jestescie naszymi przyszlymi prelegentami… :)
2. mozna sporo skozystac;
-respect
-presentation skills
-przygotowanie prezentacji bywa bardzo ksztalcace
-budowanie wlasnej marki
-miejsce dla osob ktore maja ochote to zrobic na zewnatrz ale nie ma gdzie sprobowac
-
My zapewniamy wsparcie:
-pomoc w przygotowaniu prezentacji
-wybor tematu - chcecie ‘cos’ pokazac ale nie macie tematu, nie wiecie co moze interesowac inne osoby? znajdziemy wam temat