Agile Data Science: Building Hadoop Analytics Applications

Network analytics are being increasingly utilized to create machine intelligence that automates the world around us. But what is a network, and how do you analyze them? More directly: how do I find and analyze networks in my dataset? This talk will go over a number of examples of practical network analytics to give viewers a playbook for doing applied social network analysis and network analytics.

In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed. To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not. All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient.

Note: Get all workshop content at - https://github.com/h2oai/h2o-meetups/tree/master/2017_02_22_Seattle_STC_Meetup Basic knowledge of R/python and general ML concepts Note: This is bring-your-own-laptop workshop. Make sure you bring your laptop in order to be able to participate in the workshop Level: 200 Time: 2 Hours Agenda: - Introduction to ML, H2O and Sparkling Water - Refresher of data manipulation in R & Python - Supervised learning ---- Understanding liner regression model with an example ---- Understanding binomial classification with an example ---- Understanding multinomial classification with an example - Unsupervised learning ---- Understanding k-means clustering with an example - Using machine learning models in production - Sparkling Water Introduction & Demo

This document discusses best practices for big data analytics. It emphasizes the importance of data curation to ensure semantic consistency and quality across diverse data sources. It warns against simply accumulating large amounts of ungoverned data ("data swamps") without relevant analytics or business applications. Instead, it advocates taking a full stack approach by building incremental decision models and data products to demonstrate value from the beginning. The document also stresses the need for data management layers, appropriate computing frameworks, and real-time and batch analytics capabilities to enable flexible exploration and insights.

This document discusses various heuristics and principles for architecture design. It provides guidelines for creating simplified, evolvable systems using small modular components. Some key points discussed include using open architectures, building in options, and designing structures that are resilient to stress. The document also advocates for pattern-oriented, minimalist designs and evolutionary systems that can adapt over time without disrupting existing information. Overall, the document presents best practices for handling complexity, enabling flexibility, and ensuring architectures can withstand failures.

Data Science with Windows Azure is an introduction to HDInsight and Hadoop offerings from Microsoft Machine Learning and Big Data Cloud based platform. This was presented at Microsoft Data Science Group – Tampa Analytics Professionals.

A keynote presentation for Big Data Spain 2015 in Madrid, 2015-10-15 http://www.bigdataspain.org/program/

Slideset of the training we gave at the Spark Summit East. Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/ Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ

The document discusses using a graph database to store and query graph data stored in a Hadoop data lake more efficiently. It describes the limitations of the typical approach of using Spark/GraphFrames on HDFS for graph queries. A graph database allows for faster ad hoc graph queries by leveraging graph traversals. The document proposes using a multi-model database that combines a document store, graph database, and key-value store with a common query language. It suggests this approach could run on a DC/OS cluster for easy deployment and management of resources. Examples show importing data into ArangoDB and running graph queries.

The document outlines the workflow of a data scientist, from planning experiments and collecting data, to analyzing, visualizing, and publishing results. It emphasizes that data science involves formalizing hypotheses based on observations and testing them using collected data. A suite of open-source tools is presented to help data scientists in managing data and supporting open, reproducible life science research. The goal is to enable integration and sharing of experimental data and results.

This document appears to be a slide deck presentation on empowering others through data exploration. The presentation discusses removing barriers to data, making feedback fast, and removing yourself from blocking others. It emphasizes visualizing data pipelines and augmenting data warehouses with data lakes to handle varying data volumes, varieties, and velocities. The goal is to turn data into insights that create business value.

Interested in Data science but trying to get a handle on all the terms getting you confused? Not sure where to start? This presentation breaks down the concepts and the terminology

This document discusses the potential for self-evolving machine learning models to provide dynamic system accuracy. It notes drawbacks of static models and outlines characteristics of self-evolving models, including their ability to work at scale, sense their environment, judge relevance, discover connections, retain and build upon previous learning, and learn from experience. The document argues that self-evolving models powered by Hadoop streaming and machine learning could fulfill expectations of low latency, high throughput, real-time performance, scalability, accuracy and dynamic context while overcoming human limitations of focus, variants, emotion and reliability.

What was a data product before the world changed and got so complex. Why distributed computing/data science is the solution. What problems does that add? How to solve most of them using the right technologies like spark notebook, spark, scala, mesos and so on in a accompanied framework

Eclipse science group presentation given at Eclipse Converge and Devoxx 2017 in California. These slides give an overview of projects in the Eclipse Science working group in 2017.

With the surge in Big Data, organizations have began to implement Big Data related technologies as a part of their system. This has lead to a huge need to update existing skillsets with Hadoop. Java professionals are one such people who have to update themselves with Hadoop skills.

Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.

Graphs are everywhere, but in a modern data stack, they are not the only tool in the toolbox. With Apache TinkerPop, adding graph capability on top of your existing data platform is not as daunting as it sounds. We will do a deep dive on writing Traversal Strategies to optimize performance of the underlying graph database. We will investigate how various TinkerPop systems offer unique possibilities in a multimodel approach to graph processing. We will discuss how using Gremlin frees you from vendor lock-in and enables you to swap out your graph database as your requirements evolve. Presented at Graph Day Texas, January 14, 2017. http://graphday.com/graph-day-at-data-day-texas/#plurad

See 2020 update: https://derwen.ai/s/h88s SF Python Meetup, 2017-02-08 https://www.meetup.com/sfpython/events/237153246/ PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.

Big SQL, Impala, and Hive were benchmarked on their ability to execute 99 queries from the TPC-DS benchmark at various scale factors. Big SQL was able to express all queries without rewriting, complete the full workload at 10TB and 30TB, and achieved the highest throughput. Impala and Hive required rewriting some queries and could only complete 70-73% of the workload at 10TB. The results indicate that query support, scale, and throughput are important factors to consider for SQL-on-Hadoop implementations.

El documento describe los componentes básicos de los generadores eólicos. Explica que la energía eólica proviene de la energía solar y el calentamiento diferencial del aire por el sol. También menciona que existen diferentes tipos de aerogeneradores según su potencia y número de palas. Luego enumera los principales componentes como el rotor, las palas, el eje de baja velocidad, la caja multiplicadora, el sistema de orientación y el sistema de soporte.

Slide from my Particle Photon workshop @bitraffineriet, Oslo, 8. Feb 2017

1) JSON-LD has seen widespread adoption with over 2 million HTML pages including it and it being a required format for Linked Data platforms. 2) A primary goal of JSON-LD was to allow JSON developers to use it similarly to JSON while also providing mechanisms to reshape JSON documents into a deterministic structure for processing. 3) JSON-LD 1.1 includes additional features like using objects to index into collections, scoped contexts, and framing capabilities.

El documento describe las características clave de un líder efectivo. Un líder debe tener la capacidad de comunicarse claramente, poseer inteligencia emocional para manejar los sentimientos propios y de otros, y establecer metas y objetivos congruentes con las capacidades del grupo. Además, un líder planea estratégicamente, aprovecha sus fortalezas y trabaja para mejorar sus debilidades, y ayuda a su gente a crecer delegando responsabilidades.

Consumers are interested in autonomous cars but still fear letting go of the wheel completely. While traffic is a major issue for city satisfaction, autonomous vehicles may help by freeing up drivers and improving the commute experience. Those most interested in autonomous cars tend to be professionals with children who already use cars to commute. Allowing cars to be shared more easily through technologies like digital keys could change whether people own cars or use them as a service. A variety of companies from traditional automakers to technology firms and public transport providers are seen as potential future providers of autonomous mobility options.

Zipcar is a car sharing service that allows users to rent vehicles by the hour or day. Members pay an annual fee of $70 plus hourly rates of $8.50 per hour or daily rates of $59. Zipcar has over 1 million members across 500 cities in 9 countries, with a fleet of 10,000 vehicles. The document outlines Zipcar's approach, history, competitors, and future outlook which includes increasing their fleet size and adding more hybrid and electric vehicles.

This document provides word of the day definitions for the words "clever", "dainty", "pounce", and "generous" across four sections. Each section defines the word, provides part of speech, examples, and discussion questions related to demonstrating or applying that word. The overall document aims to build vocabulary and comprehension through engaging examples and questions about the different words.

It's all about introduction to a blog which speaks about Destinations, Arts, Culture, People, Cuisines...Everything you would want to know about Kerala Discover Life. Feel Divinity. Find Yourself...........Experience God's Own Country

La carta proporciona información sobre Michell Figueroa, un estudiante de la Universidad Fermín Toro en Barquisimeto. Figueroa está inscrito en la Facultad de Ciencias Jurídicas y Políticas, Escuela de Derecho, sección Saia D. La carta incluye su nombre completo y número de identificación.

An overview of Teraproc cluster-as-a-service offerings for high-performance distributed analytics. This overview presentation includes a step-by-step demonstration of the process of deploying a ready-to-run R Studio cluster environment on Amazon Web Services. More information available at http://teraproc.com

The document discusses creating an HTML page from a template. It breaks the template down into sections like header, main content, and footer. It then provides the HTML code to recreate each section, with explanations. For example, it shows how to code the header section with elements for quick links, logo, search bar, and navigation. It also demonstrates how to code the main content with different article sections. The document is intended to teach how to reconstruct a web page design in HTML.

Market Research SHARE I had the pleasure of attending the SaaStr Annual 2016 Conference in San Francisco earlier this month and wanted to share some of the insights I gathered from that event with you here. The findings below are arranged by functional area with attribution. I tried to compress the content as much as possible, but there was A TON of great information at the conference so would highly recommend spending the time to read through.

On Swedish-German translation culture during the eighteenth century

This document summarizes Rachel Andrew's presentation on CSS Grid Layout. Some key points: - CSS Grid Layout provides a new two-dimensional layout system for CSS that solves many of the problems of previous methods like floats and flexbox. - Grid uses line-based placement, with grid lines that can be explicit or implicit, to position items on the page. Properties like grid-column and grid-row position items within the grid. - The grid template establishes the structure of rows and columns. Items can span multiple tracks. Fraction units like fr distribute space proportionally. - Common layouts like Holy Grail are easily achieved with Grid. The structure can also adapt at breakpoints by redefining

Detailed report of IBM's 30TB Hadoop-DS report showing that IBM InfoSphere BigInsights (SQL-on-Hadoop) is able to execute all 99 TPC-DS queries at scale over native Hadoop data formats. Written by Simon Harris, Abhayan Sundararajan, John Poelman and Matthew Emmerton.

La motivación laboral se refiere a la capacidad de las empresas para mantener el estímulo positivo de sus empleados y su desempeño en el trabajo. Existen cuatro tipos de motivación: extrínseca, intrínseca, transitiva y trascendente. La motivación es importante para las empresas porque mejora la productividad individual y grupal de los empleados. Algunos factores que motivan incluyen tener responsabilidades, autonomía y objetivos claros, mientras que problemas interpersonales, falta de confianza y exceso de control desmotivan.

This document discusses setting up an environment for agile data science and analytics applications. It recommends: - Publishing atomic records like emails or logs to a "database" like MongoDB in order to make the data accessible to designers, developers and product managers. - Wrapping the records with tools like Pig, Avro and Bootstrap to enable viewing, sorting and linking the records in a browser. - Taking an iterative approach of refining the data model and publishing insights to gradually build up an application that discovers insights from exploring the data, rather than designing insights upfront. - Emphasizing simplicity, self-service tools, and minimizing impedance between layers to facilitate rapid iteration and collaboration across roles.

This document provides an overview of steps to build an agile analytics application, beginning with raw event data and ending with a web application to explore and visualize that data. The steps include: 1) Serializing raw event data (emails, logs, etc.) into a document format like Avro or JSON 2) Loading the serialized data into Pig for exploration and transformation 3) Publishing the data to a "database" like MongoDB 4) Building a web interface with tools like Sinatra, Bootstrap, and JavaScript to display and link individual records The overall approach emphasizes rapid iteration, with the goal of creating an application that allows continuous discovery of insights from the source data.

cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.

"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision

This document discusses how Cloudera Enterprise Data Hub (EDH) can be used for advanced analytics. EDH allows users to perform diverse concurrent analytics on large datasets without moving the data. It includes tools for machine learning, graph analytics, search, and statistical analysis. EDH protects data through security features and system change tracking. The document argues that EDH is the only platform that can support all these analytics capabilities in a single, integrated system. It provides several examples of how advanced analytics on EDH have helped organizations like the government address important problems.

http://www.bigdataspain.org/2014/conference/state-of-play-data-science-on-hadoop-in-2015-keynote Machine Learning is not new. Big Machine Learning is qualitatively different: More data beats algorithm improvement, scale trumps noise and sample size effects, can brute-force manual tasks. Session presented at Big Data Spain 2014 Conference 18th Nov 2014 Kinépolis Madrid http://www.bigdataspain.org Event promoted by: http://www.paradigmatecnologico.com Slides: https://speakerdeck.com/bigdataspain/state-of-play-data-science-on-hadoop-in-2015-by-sean-owen-at-big-data-spain-2014

Tomáš Červenka will discuss Hive, an open-source data warehousing system built on Hadoop that provides SQL-like queries over large datasets. He will explain what Hive is useful for (big data analytics and processing), and not useful for (real-time queries and algorithms difficult to parallelize). He will demonstrate how to get started with Hive using Amazon EMR and provide a sample query, and discuss how VisualDNA uses Hive for analytics, reporting pipelines, and machine learning inference. Tips provided include using fast instance types, compression, and partitioning data.

LinkedIn is a large professional social network with 50 million users from around the world. It faces big data challenges at scale, such as caching a user's third degree network of up to 20 million connections and performing searches across 50 million user profiles. LinkedIn uses Hadoop and other scalable architectures like distributed search engines and custom graph engines to solve these problems. Hadoop provides a scalable framework to process massive amounts of user data across thousands of nodes through its MapReduce programming model and HDFS distributed file system.

War stories from building the Global Patent Search Network, and why Data folks need to think more about UX and Discovery, and UX folks need to think more about Data.

ORM or Stored Procedures? Code First or Database First? Ad-Hoc Queries? Impedance Mismatch? If you're a developer or you are a DBA working with developers you have heard all this terms at least once in your life…and usually in the middle of a strong discussion, debating about one or the other. Well, thanks to StackOverflow's Dapper, all these fights are finished. Dapper is a blazing fast microORM that allows developers to map SQL queries to classes automatically, leaving (and encouraging) the usage of stored procedures, parameterized statements and all the good stuff that SQL Server offers (JSON and TVP are supported too!) In this session I'll show how to use Dapper in your projects from the very basis to some more complex usages that will help you to create *really fast* applications without the burden of huge and complex ORMs. The days of Impedance Mismatch are finally over!

This document summarizes a presentation about query-time nonparametric regression and time routed aliases in Solr. It discusses how nonparametric multiplicative regression was used to continuously predict user interests for an online career coaching system based on click-through data. It also describes how time routed aliases in Solr provide a built-in way to implement time-partitioned indexing of timestamped data across multiple collections while automatically adding and removing collections over time.

This document provides an overview of architecting a first big data implementation. It defines key concepts like Hadoop, NoSQL databases, and real-time processing. It recommends asking questions about data, technology stack, and skills before starting a project. Distributed file systems, batch tools, and streaming systems like Kafka are important technologies for big data architectures. The document emphasizes moving from batch to real-time processing as a major opportunity.

The four Linked Data publishing principles established in 2006 seem to be quite clear and well understood by people inside and outside the core Linked Data and Semantic Web community. However, not only when discussing with outsiders about the goodness of Linked Data but also when reviewing papers for the COLD workshop series, I find myself, in many occasions, going back again to the principles in order to see whether some approach for Web data publication and consumption is actually Linked Data or not. In this talk we will review some of the current approaches that we have for publishing data on the Web, and we will reflect on why it is sometimes so difficult to get into an agreement on what we understand by Linked Data. Furthermore, we will take the opportunity to describe yet another approach that we have been working on recently at the Center for Open Middleware, a joint technology center between Banco Santander and Universidad Politécnica de Madrid, in order to facilitate Linked Data consumption.

The document discusses how traditional analytics processes involve siloed data and platforms, long timelines for data discovery, and difficulties accessing and sharing data. It proposes that an Enterprise Data Hub (EDH) using Cloudera can help address these issues by providing unified storage for all types of data, shorter analytics lifecycles, and the ability to do more with data by using 100x more data and more types of data. The EDH allows organizations to use all of their data and gain insights sooner.

5 Things that Make Hadoop a Game Changer Webinar by Elliott Cordo, Caserta Concepts There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop. To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029 For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/

The document discusses building a lightweight discovery interface for Chinese patents using Solr/Lucene. It describes parsing various patent file formats using Tika and building custom parsers. It also emphasizes the importance of making the search solution accessible by allowing users to export data and share results.

This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)

This talk is introduce by Xiangdong Huang, who is a PPMC of Apache IoTDB (incubating) project, at Apache Event at Tsinghua University in China. About the Event: The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world. The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.

This document provides an introduction and agenda for a presentation on Spark. It discusses how Spark is a fast engine for large-scale data processing and how it improves on MapReduce. Spark stores data in memory across clusters to allow for faster iterative computations versus writing to disk with MapReduce. The presentation will demonstrate Spark concepts through word count and log analysis examples and provide an overview of Spark's Resilient Distributed Datasets (RDDs) and directed acyclic graph (DAG) execution model.

The DataHub Project: Collaborative Data Science and Dataset Version Management; talk given at CIDR 2015

原版一模一样【微信：741003700 】【阳光海岸大学毕业证成绩单】【微信：741003700 】学位证，留信学历认证（真实可查，永久存档）原件一模一样纸张工艺/offer、在读证明、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。 本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】 一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！ 二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查） 四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 办理阳光海岸大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。 办理阳光海岸大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分： 校徽：象征着学校的荣誉和传承。 校名:学校英文全称 授予学位：本部分将注明获得的具体学位名称。 毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。 颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。 其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。 办理阳光海岸大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。 综上所述，办理阳光海岸大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

간단해 보이지만 실제로는 복잡한 몇 가지 Amazon DynamoDB 디자인 퍼즐을 함께 해결하며 DynamoDB가 대규모로 작동하는 방식에 대해 자세히 알아봅니다. DynamoDB의 작동 방식을 이해함으로써 더 효과적이고 확장 가능한 솔루션을 찾는 방법을 알아보세요.

Karol Bagh @ℂall @Girls ꧁❤ 9873777170 ❤꧂VIP Jya Khan Top Model Safe

Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados. QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo. Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.

Analytics use cases for telco

LLM powered contract compliance application which uses Advanced RAG method Self-RAG and Knowledge Graph together for the first time. It provides highest accuracy for contract compliance recorded so far for Oil and Gas Industry.

### Data Description and Analysis Summary for Presentation #### 1. **Importing Libraries** Libraries used: - `pandas`, `numpy`: Data manipulation - `matplotlib`, `seaborn`: Data visualization - `scikit-learn`: Machine learning utilities - `statsmodels`, `pmdarima`: Statistical modeling - `keras`: Deep learning models #### 2. **Loading and Exploring the Dataset** **Dataset Overview:** - **Source:** CSV file (`mumbai-monthly-rains.csv`) - **Columns:** - `Year`: The year of the recorded data. - `Jan` to `Dec`: Monthly rainfall data. - `Total`: Total annual rainfall. **Initial Data Checks:** - Displayed first few rows. - Summary statistics (mean, standard deviation, min, max). - Checked for missing values. - Verified data types. **Visualizations:** - **Annual Rainfall Time Series:** Trends in annual rainfall over the years. - **Monthly Rainfall Over Years:** Patterns and variations in monthly rainfall. - **Yearly Total Rainfall Distribution:** Distribution and frequency of annual rainfall. - **Box Plots for Monthly Data:** Spread and outliers in monthly rainfall. - **Correlation Matrix of Monthly Rainfall:** Relationships between different months' rainfall. #### 3. **Data Transformation** **Steps:** - Ensured 'Year' column is of integer type. - Created a datetime index. - Converted monthly data to a time series format. - Created lag features to capture past values. - Generated rolling statistics (mean, standard deviation) for different window sizes. - Added seasonal indicators (dummy variables for months). - Dropped rows with NaN values. **Result:** - Transformed dataset with additional features ready for time series analysis. #### 4. **Data Splitting** **Procedure:** - Split the data into features (`X`) and target (`y`). - Further split into training (80%) and testing (20%) sets without shuffling to preserve time series order. **Result:** - Training set: `(X_train, y_train)` - Testing set: `(X_test, y_test)` #### 5. **Automated Hyperparameter Tuning** **Tool Used:** `pmdarima` - Automatically selected the best parameters for the SARIMA model. - Evaluated using metrics such as AIC and BIC. **Output:** - Best SARIMA model parameters and statistical summary. #### 6. **SARIMA Model** **Steps:** - Fit the SARIMA model using the training data. - Evaluated on both training and testing sets using MAE and RMSE. **Output:** - **Train MAE:** Indicates accuracy on training data. - **Test MAE:** Indicates accuracy on unseen data. - **Train RMSE:** Measures average error magnitude on training data. - **Test RMSE:** Measures average error magnitude on testing data. #### 7. **LSTM Model** **Preparation:** - Reshaped data for LSTM input. - Converted data to `float32`. **Model Building and Training:** - Built an LSTM model with one LSTM layer and one Dense layer. - Trained the model on the training data. **Evaluation:** - Evaluated on both training and testing sets using MAE and RMSE. **Output:** - **Train MAE:** Accuracy on training data. - **T

Rohini @ℂall @Girls ꧁❤ 9873940964 ❤꧂VIP Vishakha Singla Top Model Safe

Rohini @ℂall @Girls ꧁❤ 9873940964 ❤꧂VIP Megha Singla Top Model Safe

Aurora PostgreSQL에서 가장 일반적인 performance use case 들에 대해 Aurora PostreSQL의 모니터링 Tool들을 통해 어떤게 문제를 식별하고 분석하는지 그리고 이 문제를 해결해나가는 절차와 방법에 대한 Deep Dive입니다.

Sin Involves More Than You Might Think (We'll Explain)

Lajpat Nagar @ℂall @Girls ꧁❤ 9873777170 ❤꧂VIP Ginni Singh Top Model Safe

Mahipalpur @ℂall @Girls ꧁❤ 9873777170 ❤꧂VIP Yogita Mehra Top Model Safe