This talk was given during DevOps Con 2017.
Have you ever spent time digging through various terminals, greping, lessing, awking and trying to find that few log lines that may be important? Have you every done that under time pressure, because mission critical services were not working? Have you every heard from your developers that they can’t tell you anything, because they don’t have access to application logs? Have you ever considered a centralized storage for logs, but time and resources are not on your side?
If you said yes, to any of the above questions, than this talk is for you. During the talk we’ll introduce you to the world of log centralization and analysis, both when it comes to open source, but also commercial tools. We will go from top to bottom and learn how to setup log centralization and analysis for servers, virtualized environments and containers. We will get from log shipping, through centralized buffering to storage and analysis to show you, that having a centralized log analysis tool is not a rocket science.
Finally, you will see how useful is to combine the logs from all your servers in a single place for blazingly fast correlation.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
BI: new of the buzz words that everyone is talking about but what is it? How can it be used to make a impact in my organization? How do I get started? This session was delivered for SharePoint Saturday Reston.
This talk was given during DevOps Con 2017.
Have you ever spent time digging through various terminals, greping, lessing, awking and trying to find that few log lines that may be important? Have you every done that under time pressure, because mission critical services were not working? Have you every heard from your developers that they can’t tell you anything, because they don’t have access to application logs? Have you ever considered a centralized storage for logs, but time and resources are not on your side?
If you said yes, to any of the above questions, than this talk is for you. During the talk we’ll introduce you to the world of log centralization and analysis, both when it comes to open source, but also commercial tools. We will go from top to bottom and learn how to setup log centralization and analysis for servers, virtualized environments and containers. We will get from log shipping, through centralized buffering to storage and analysis to show you, that having a centralized log analysis tool is not a rocket science.
Finally, you will see how useful is to combine the logs from all your servers in a single place for blazingly fast correlation.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
BI: new of the buzz words that everyone is talking about but what is it? How can it be used to make a impact in my organization? How do I get started? This session was delivered for SharePoint Saturday Reston.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
SCS 4120 - Software Engineering IV
BACHELOR OF SCIENCE HONOURS IN COMPUTER SCIENCE
BACHELOR OF SCIENCE HONOURS IN SOFTWARE ENGINEERING
All in One Place Lecture Notes
Distribution Among Friends Only
All copyrights belong to their respective owners
Viraj Brian Wijesuriya
vbw@ucsc.cmb.ac.lk
How to boost your datamanagement with Dremio ?Vincent Terrasi
Works with any source. Relational, non-relational, 3rd party apps. 5 years ago nobody was using Hadoop, MongoDB, and 5 years from now there will be new products. You need a solution that is future proof.
Works with any BI tool. In every company multiple tools are in use. Each department has their favorite. We need to work with all of them.
No ETL, data warehouse, cubes. This would need to give you a really good alternative to these options.
Makes data self-service, collaborative. Probably most important of all, we need to change the dynamic between the business and IT. We need to make it so business users can get the data they want, in the shape they want it, without waiting on IT.
Makes Big Data feels small. It needs to make billions of rows feel like a spreadsheet on your desktop.
Open source. It’s 2017, so we think this has to be open source.
The slide deck from data and analytics workshop for HR professionals. Presented in @hrtechgroup event in Microsoft Vancouver. The workshop was built around the HR sample partner data set
https://docs.microsoft.com/en-us/power-bi/sample-human-resources
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
BigInsights and Text Analytics.
As enterprises seek to gain operational efficiencies and competitive advantage through greater use of analytics, much of the new information they need to analyze is found in text documents and, increasingly, in a wide variety of social media sites and portals. A critical step in gaining insights from this information is extracting core data from huge volumes of text. That data is then available for downstream analytic, mining and machine learning tools. AQL (Annotator Query Language) is a powerful declarative, rule-based language for the extraction of information from text documents.
"You can download this product from SlideTeam.net"
Presenting this set of slides with name - Three Months Infrastructure Roadmap. This is a nine stage process. The stages in this process are Infrastructure Roadmap, Infrastructure Roadmap, Organizational Structures Roadmap. https://bit.ly/3d6hXJf
Simplify and Scale Data Engineering Pipelines with Delta LakeDatabricks
We’re always told to ‘Go for the Gold!,’ but how do we get there? This talk will walk you through the process of moving your data to the finish fine to get that gold metal! A common data engineering pipeline architecture uses tables that correspond to different quality levels, progressively adding structure to the data: data ingestion (‘Bronze’ tables), transformation/feature engineering (‘Silver’ tables), and machine learning training or prediction (‘Gold’ tables). Combined, we refer to these tables as a ‘multi-hop’ architecture. It allows data engineers to build a pipeline that begins with raw data as a ‘single source of truth’ from which everything flows. In this session, we will show how to build a scalable data engineering data pipeline using Delta Lake, so you can be the champion in your organization.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
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.
Azure Machine Learning and its real-world use casesMichaela Murray
Is Machine Learning still a buzz word or can we easily put it to use to gain actionable insights from our data? In this demo-heavy, hands-on session, Ram will explain how to find hidden patterns in the data, find outliers and predict results using real-world datasets.
By the end of this session, you will be familiar with Machine Learning concepts (regression, classification, over-fitting, cross-validation etc.) and you should be able to build, deploy and consume Machine Learning models with ease.
About the Presenter
Based in Brisbane, Ram Katepally is a Microsoft Certified Solutions Expert and Data Analytics consultant at WARDY IT Solutions. As a consultant, Ram has significant experience working with companies of all sizes across Australia, empowering them to make data-driven business decisions. He’s passionate about Machine Learning, Internet of Things, Office 365 and Power BI. In his free time, he is an avid player of chess.
How Nubank is building a customer-obsessed bank - FSV201 - New York AWS SummitAmazon Web Services
Nubank, Latin America’s first (and largest) cloud-native bank, has relied on AWS since day one. Operating in the cloud allows Nubank’s developers to create software that scales and quickly adapts to the changing needs of a complex market and a growing business. Nubank relies on services like Amazon EC2, Amazon DynamoDB, Amazon VPC, Amazon S3, and AWS CloudFormation to let 8.5 million customers make around 2.5 million purchases per day—all without a dedicated infrastructure team. Learn how Nubank’s fully automated, cell-based architecture allows the bank to provide the best customer experience while generating reliable audited financial records for regulators.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
SCS 4120 - Software Engineering IV
BACHELOR OF SCIENCE HONOURS IN COMPUTER SCIENCE
BACHELOR OF SCIENCE HONOURS IN SOFTWARE ENGINEERING
All in One Place Lecture Notes
Distribution Among Friends Only
All copyrights belong to their respective owners
Viraj Brian Wijesuriya
vbw@ucsc.cmb.ac.lk
How to boost your datamanagement with Dremio ?Vincent Terrasi
Works with any source. Relational, non-relational, 3rd party apps. 5 years ago nobody was using Hadoop, MongoDB, and 5 years from now there will be new products. You need a solution that is future proof.
Works with any BI tool. In every company multiple tools are in use. Each department has their favorite. We need to work with all of them.
No ETL, data warehouse, cubes. This would need to give you a really good alternative to these options.
Makes data self-service, collaborative. Probably most important of all, we need to change the dynamic between the business and IT. We need to make it so business users can get the data they want, in the shape they want it, without waiting on IT.
Makes Big Data feels small. It needs to make billions of rows feel like a spreadsheet on your desktop.
Open source. It’s 2017, so we think this has to be open source.
The slide deck from data and analytics workshop for HR professionals. Presented in @hrtechgroup event in Microsoft Vancouver. The workshop was built around the HR sample partner data set
https://docs.microsoft.com/en-us/power-bi/sample-human-resources
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
BigInsights and Text Analytics.
As enterprises seek to gain operational efficiencies and competitive advantage through greater use of analytics, much of the new information they need to analyze is found in text documents and, increasingly, in a wide variety of social media sites and portals. A critical step in gaining insights from this information is extracting core data from huge volumes of text. That data is then available for downstream analytic, mining and machine learning tools. AQL (Annotator Query Language) is a powerful declarative, rule-based language for the extraction of information from text documents.
"You can download this product from SlideTeam.net"
Presenting this set of slides with name - Three Months Infrastructure Roadmap. This is a nine stage process. The stages in this process are Infrastructure Roadmap, Infrastructure Roadmap, Organizational Structures Roadmap. https://bit.ly/3d6hXJf
Simplify and Scale Data Engineering Pipelines with Delta LakeDatabricks
We’re always told to ‘Go for the Gold!,’ but how do we get there? This talk will walk you through the process of moving your data to the finish fine to get that gold metal! A common data engineering pipeline architecture uses tables that correspond to different quality levels, progressively adding structure to the data: data ingestion (‘Bronze’ tables), transformation/feature engineering (‘Silver’ tables), and machine learning training or prediction (‘Gold’ tables). Combined, we refer to these tables as a ‘multi-hop’ architecture. It allows data engineers to build a pipeline that begins with raw data as a ‘single source of truth’ from which everything flows. In this session, we will show how to build a scalable data engineering data pipeline using Delta Lake, so you can be the champion in your organization.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
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.
Azure Machine Learning and its real-world use casesMichaela Murray
Is Machine Learning still a buzz word or can we easily put it to use to gain actionable insights from our data? In this demo-heavy, hands-on session, Ram will explain how to find hidden patterns in the data, find outliers and predict results using real-world datasets.
By the end of this session, you will be familiar with Machine Learning concepts (regression, classification, over-fitting, cross-validation etc.) and you should be able to build, deploy and consume Machine Learning models with ease.
About the Presenter
Based in Brisbane, Ram Katepally is a Microsoft Certified Solutions Expert and Data Analytics consultant at WARDY IT Solutions. As a consultant, Ram has significant experience working with companies of all sizes across Australia, empowering them to make data-driven business decisions. He’s passionate about Machine Learning, Internet of Things, Office 365 and Power BI. In his free time, he is an avid player of chess.
How Nubank is building a customer-obsessed bank - FSV201 - New York AWS SummitAmazon Web Services
Nubank, Latin America’s first (and largest) cloud-native bank, has relied on AWS since day one. Operating in the cloud allows Nubank’s developers to create software that scales and quickly adapts to the changing needs of a complex market and a growing business. Nubank relies on services like Amazon EC2, Amazon DynamoDB, Amazon VPC, Amazon S3, and AWS CloudFormation to let 8.5 million customers make around 2.5 million purchases per day—all without a dedicated infrastructure team. Learn how Nubank’s fully automated, cell-based architecture allows the bank to provide the best customer experience while generating reliable audited financial records for regulators.
Getting Agile Right - Rebooting an Agile Organization in 100 days - Agile Tou...Maurizio Mancini
Presentation by Senior Consultant Maurizio Mancini of Exempio.com about an Agile Reboot of one Agile organization that was accomplished in just 100 business days!
When Management Asks You: “Do You Accept Agile as Your Lord and Savior?"admford
So you’ve been told that your organization is going to implement Agile methodologies across ALL of IT, and not just in development. And you’ve been given the responsibility to implement it in Security Operations, and without a clear plan or measurable objectives other than “make the team more efficient”. While one can complain that someone in the C-Suite heard of the book “Scrum: The Art of Doing Twice the Work in Half the Time”, you still have a job to do. So the basics of Project Management, Agile, Scrum & Kanban are covered and how one can shoehorn these concepts into working in an operations context. Oh, and there will also be some finagling of where DevOps stands regarding Agile and Operations.
When Management Asks You: “Do You Accept Agile as Your Lord and Savior?” - Ci...admford
Updated version of my original Cyphercon talk. With more useful information regarding how to enact change and better visual representation of certain concepts. This talk was given at CircleCityCon 10 in 2023
Applying TQM and the Toyota Production System in Development of Software Arti...Dave Litwiller
Adapting TPS Tools and Techniques for Enterprise TQM to Software, Artificial Intelligence, Machine Learning and Deep Learning Development Organizations
The Agile Learning Organization - Dave Litwiller - Sept 17 2020 - PublicDave Litwiller
Adapting Organizational Capabilities in Scale-up Technology Businesses to Thrive in the Strategic Environment using the Principles of TQM
- Enhance organizational learning capacity and agility
- Build connective capacity across functions and time horizons, to counter tendencies toward silos
- Develop leadership bandwidth at all levels to expand institutional capability for productive change
Creating change from within - Agile Practitioners 2012Dror Helper
Faced with management that do not care about "being agile" what can a single developer do? Quite a lot!
Every developer has the power to improve the organization he works in in small iterative steps – and I can show you how.
If you want to make the change and don't know where to start – look no further, in this session I'll share my experience and show a few tips and tricks I learnt. As well as discuss the do and don'ts that can make all the.
- How to be agile developer in a waterfall company.
- Influencing people without formal authority.
- Using the right practices that makes the difference
- How to avoid alienating people
- Discovering your allies
- Know when to fight and when to "retreat" and cut your losses
- Making a change without disrupting the daily routine
- What being an agile evangelist is all about
this presentation contains agile engineering practices which are used by software community.
These practices provides agility in the software development. Applying agile software development without these practices is not easy for software developers.
The Importance of Culture: Building and Sustaining Effective Engineering Org...Randy Shoup
Randy is a 25-year veteran of Silicon Valley, having led engineering organizations at eBay, Google, Oracle, and a number of other companies. Through the lens of his personal experience from hands-on engineer to architect to CTO, at organizations ranging from tiny startups to global giants, Randy will discuss several important aspects of engineering cultures, which both support and hinder the ability to innovate: hiring and retention, ownership and collaboration, quality and discipline, and learning and experimentation.
Randy will suggest some learnings about what has worked well -- and what has not -- in creating and sustaining an effective engineering culture. He will further offer some concrete suggestions on how other organizations -- both large and small -- can evolve their cultures as well.
We’re all doing Agile nowadays, aren’t we? We’ll all delivering software in an Agile way. But what does that mean? Does it mean sprints and stand-ups? Kanban even? But what about Extreme Programming? If as a development team we’re not using pair programming, test driven development, continuous integration, and other XP practices, then we’re not really doing Agile software development and we may be on a march to frustration, or even failure.
I’m going to look at why the current trend of companies and projects adopting Scrum, calling themselves Agile, but not transitioning their development to XP, is a recipe for disaster. I’d like to cover the main practices of XP as well as other good practices that can really help a team deliver quality software, whether they’re doing two-week sprints, Kanban, or even Waterfall.
https://www.youtube.com/watch?v=aZgnY9fAHOA
Getting Agile Right - Rebooting an Agile organization in 100 days - Agile Tou...Maurizio Mancini
Presentation at Agile Tour Montreal 2018 by Maurizio Mancini of Exempio and Paul T. Ryan CTO of OpenX.
Many organizations think they are Agile when they are not. Here is how to recognize when you need an Agile reboot and how to reboot your organization to become a true Agile organization.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
3. HOW TO BE MORE EFFECTIVE AS AN ENGINEER?
• Adopt the right mindsets
• Focus on high-leverage activities
• Optimize for learning
• Prioritize regularly
• Execution
• Improve the iteration speed
• Measure what you want to improve
• Validate your ideas early and often
• Improve project estimation skills
• Build long-term value
• Balance quality with pragmatism
• Minimize operational burden
• Invest in the team’s growth
4. FOCUS ON HIGH-LEVERAGE ACTIVITIES
𝑳𝒆𝒗𝒆𝒓𝒂𝒈𝒆 =
𝑰𝒎𝒑𝒂𝒄𝒕 𝑷𝒓𝒐𝒅𝒖𝒄𝒆𝒅
𝑻𝒊𝒎𝒆 𝑰𝒏𝒗𝒆𝒔𝒕𝒆𝒅
• Reduce the time to complete a certain activity
• Increase the output of a particular activity
• Shift to higher-leverage activities
6. OPTIMIZE FOR LEARNING
• Seek work environments which are conducive for learning
• Dedicate the time on the work for learning
7. PRIORITIZE REGULARLY
• Track To-Dos list in a single easily accessible list
How do you keep track of To-Dos?
• Which activities to prioritize
Urgent Not Urgent
Important Crisis
Pressing issues
Deadlines
Planning and prevention
Building relationships
New opportunities
Personal Development
Not Important Interruptions
Most meetings
Most emails and calls
Time wasting
8. PRIORITIZE REGULARLY
• Limit Work-In-Progress Tasks
• Depends on person but generally 1 or 2
• Protect maker’s time
9. IMPROVE ITERATION SPEED
• Invest in time-saving tools
• Shorten the debugging and validation loops
• Master your programming environment
• Get familiar with your IDE
• Learn a scripting language
• Get familiar with Unix Shell commands
• Automate your workflows
• Make unit tests fast
• Tackle non-engineering bottlenecks
• Manager’s approval
• Communication with other teams
10. MEASURE WHAT YOU WANT TO IMPROVE
• Pick the right metrics
Bug fixed vs Bug Outstanding?
Short Click-through rates vs long click-through rates?
• Instrument everything
• Healthcare.gov launch disaster
• Ensure data integrity (Do you measure correctly?)
• The only thing worse than having no data is the illusion of having the right
data.
11. VALIDATE YOUR IDEAS EARLY AND OFTEN
• Find the cheapest way to validate your idea
• Prototype
• Mock-up
• A/B testing
• Get feedback early and often
• Commit code early and often
• Code reviews
12. IMPROVE PROJECT ESTIMATION SKILLS
• Estimate based on small tasks
• Think of estimation as probability distribution not as best-case
scenario
• Beware of anchoring bias
• Validate estimates based on historical data
• Let the person who do the tasks estimate
• Allow others to challenge the estimate
• Define specific goals and measurable milestones
• Tackle the riskiest task first
• Be caution of rewrite projects
• Don’t sprint in the middle of marathon
13. BALANCE QUALITY WITH PRAGMATISM
• Standard code convention, styles?
• Establish code review process
• Catching bugs early
• Increase accountability
• Sharing good practices
• Sharing knowledge of codebase
• Increase long-term value
• Good abstraction can significantly improve output
• MapReduce
• Automated testing (unit test, integration test)
14. MINIMIZE OPERATIONAL BURDEN
• Embrace Operational simplicity
• Choose the simplest technologies for the task (Instagram)
• A New Feature, A New Tool = Potentially introduce complexity
• Build System to Fail Fast
• When a problem occur, it should fail immediately and visibly
15. MINIMIZE OPERATIONAL BURDEN
• Automate Mechanical Task
Why don’t we want to automate?
• No time
• Lack of familiarity with automation tools
• Underestimate the future frequency of the task
• Tragedy of the commons
16. RESPONSE AND RECOVER QUICKLY
• Anticipate failures and hone ability to recover
• Ask what if
• Self-healing?
17. INVEST IN THE TEAM’S GROWTH
• Make hiring a priority
• Build a good boarding process
• Ramp up new engineers as fast as possible
• Impart the team’s cultures and values
• Socially integrate new engineers onto the new team
Codelabs? BootCamp?Mentorship? Talks?
• Share ownership of the code
- Mentoring and pairing
- Rotate types of tasks across the team
- Document complex workflows, designs,..
18. INVEST IN TEAM’S GROWTH
• Build collective wisdoms through post-mortems
• Build a great engineering culture
Editor's Notes
Obama’s Campaign: What email subject lines attract the most number of replies?
Cuil’s big bang launch
Story of Instagram: scale to serve 40 million users with 13 engineers acquired by Facebook in 2012
Example: MemCached Expiration Time
Netflix build Chaos Monkey which randomly kills services in their infrastructure