Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

•Download as PPTX, PDF•

2 likes•929 views

Senthil Pandurangan

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop presented at Grafanacon in New York.

Engineering

Monitoring at Scale over diverse data
sources @ PayPal – Druid, TSDB, Hadoop

Agenda
©2016 PayPal Inc. Confidential and proprietary.
• Introduction
• Monitoring Platform Architecture
• Grafana Druid Plugin
• Q & A

PayPal Command Center
Powered by Monitoring Team
3

PayPal Monitoring Ecosystem - Sherlock
4
APIs
Logs Processing
Metric Processing Insights Engine
Alerts
Visualization
Events
Stream
Metrics
Stream
Topology
Logs
Stream
Pools & Hosts
PaaS
IaaS
Network
Databases
CAL (Logging)
Remediation Orchestration
Configuration
Sherlock is a unified monitoring, alerting and logging platform that exposes real time data and
insights, enabled by an extensible visualization platform.
Developers
Analysts
Command Center
SRE

Tools & Technologies
©2016 PayPal Inc. Confidential and proprietary.
Storage
Processing
Visualization

Ingest Queue Process Store Query
PayPal Monitoring Platform - Architecture
6
MessageBus
App
Logs
AppApp
S
e
r
v
i
c
e
s
DBLB
NO
SQ
L
Metrics
Stream
processing
MetricsLogs
Log Views
Hadoop
Reports
Custom
Visualizations
Alerts

Grafana Druid Plugin
https://grafana.net/plugins/abhisant-druid-datasource/installation
©2016 PayPal Inc. Confidential and proprietary.

Grafana - Adding Druid Data source
©2016 PayPal Inc. Confidential and proprietary.

Grafana Druid Plugin – Query Editor
©2016 PayPal Inc. Confidential and proprietary.

Druid Query Types
©2016 PayPal Inc. Confidential and proprietary.
Time Series
Top N
Group by
Select

Multiple Data sources
©2016 PayPal Inc. Confidential and proprietary.
Time Series
Top N
Group by
Select
TSDB
Druid
Hadoop Reports

Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark. Speaker: Yaron Ekshtein

Delta Lake OSS: Create reliable and performant Data Lake by Quentin Ambard

Paris Data Engineers !

Case Study: Realtime Analytics with Druid

Salil Kalia

The case study is about ViralGains - a US based video marketing platform. The presentation was delivered by me (Salil Kalia) at Great Indian Developer Summit (GIDS) 2016. This is a piece of a great work that we have done at TO THE NEW Digital with our customer, ViralGains. Here, I show-cased Druid (http://druid.io) and the supporting technologies (Kafka/Zookeeper) to demonstrate how it helped us in building a stable realtime analytics system, in capturing hundreds of millions of analytics events per day. When it comes to Ad industry - it becomes very important to be precise or close to precision because money is involved at every step (even for a single ad impression). The case study included a demo and a short talk on their journey of moving from Redis to Cassandra and finally ending up on Druid with an outstanding performance.

Whoops, The Numbers Are Wrong! Scaling Data Quality @ Netflix

DataWorks Summit

Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies? In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data.

Druid at SF Big Analytics 2015-12-01

gianmerlino

Near real-time analytics has become a common requirement for many data teams as the technology has caught up to the demand. One of the hardest aspects of enabling near-realtime analytics is making sure the source data is ingested and deduplicated often enough to be useful to analysts while writing the data in a format that is usable by your analytics query engine. This is usually the domain of many tools since there are three different aspects of the problem: streaming ingestion of data, deduplication using an ETL process, and interactive analytics. With Spark, this can be done with one tool. This talk with walk you through how to use Spark Streaming to ingest change-log data, use Spark batch jobs to perform major and minor compaction, and query the results with Spark.SQL. At the end of this talk you will know what is required to setup near-realtime analytics at your organization, the common gotchas including file formats and distributed file systems, and how to handle data the unique data integrity issues that arise from near-realtime analytics.

Headaches and Breakthroughs in Building Continuous Applications

Databricks

At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.

The Evolution of Big Data Pipelines at Intuit

DataWorks Summit/Hadoop Summit

"Who Moved my Data? - Why tracking changes and sources of data is critical to...

Cask Data

Speaker: Russ Savage, from Cask Big Data Applications Meetup, 09/14/2016 Palo Alto, CA More info here: http://www.meetup.com/BigDataApps/ Link to talk: https://youtu.be/4j78g3WvC4Y About the talk: As data lake sizes grow, and more users begin exploring and including that data in their everyday analysis, keeping track of the sources for data becomes critical. Understanding how a dataset was generated and who is using it allows users and companies to ensure their analysis is leveraging the most accurate and up to date information. In this talk, we will explore the different techniques available to keep track of your data in your data lake and demonstrate how we at Cask approached and attempted to mitigate this issue.

Druid Overview by Rachel Pedreschi

Brian Olsen

Real-time Distributed Stream Processing@ Scale

Jerome Boulon

Visual Mapping of Clickstream DataDataWorks Summit

Realtime streaming architecture in INFINARIO

Jozo Kovac

Conquering the Lambda architecture in LinkedIn metrics platform with Apache C...

Khai Tran

Metrics play an important role in data-driven companies like LinkedIn, where we leverage them extensively for reporting, experimentation, and in-product applications. We built an offline platform to help people define and produce metrics driven through their transformation code, mostly in Pig or Hive, and metadata-rich configurations. Many of our users would like to look at these metrics in a real-time fashion. To support this, we recently built an extension to the platform that auto-generates Samza real-time flow from existing offline transformation code with just a single command. Combining with the existing offline platform, we delivered Lambda architecture without maintaining multiple code bases. In this talk, we will describe how we use Apache Calcite to translate our offline logic, served as the single source of truth, into both Samza code and configuration for real-time execution.

The Stream is the Database - Revolutionizing Healthcare Data Architecture

DataWorks Summit/Hadoop Summit

Building Data Pipelines with Spark and StreamSets

Pat Patterson

Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.

Lambda architecture for real time big data

Trieu Nguyen

Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...

HostedbyConfluent

Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance. This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.

Make streaming processing towards ANSI SQL

DataWorks Summit

SQL is the most widely used language for data processing. It allows users to concisely and easily declare their business logic. Data analysts usually do not have complex software programing backgrounds, but they can program SQL and use it on a regular basis to analyze data and power the business decisions. Apache Flink is one of streaming engines that supports SQL. Besides Flink, some other stream processing frameworks, like Kafka and Spark structured streaming, have SQL-like DSL, but they do not have the same semantics as Flink. Flink’s SQL implementation follows ANSI SQL standard while others do not. In this talk, we will present why following ANSI SQL standard is essential characteristic of Flink SQL and how we achieved this. The core business of Alibaba is now fully driven by the data processing engine: Blink, a project based on Flink with Alibaba’s improvements. About 90% of blink jobs are written by Flink SQL. We will show the use cases and the experience of running large scale Flink SQL jobs at Alibaba in the talk. Speakers Shaoxuan Wang, Senior Engineering Manager, Alibaba Xiaowei Jiang, Senior Director, Alibaba

Introduction to Apache Apex by Thomas Weise

Big Data Spain

Building Robust Production Data Pipelines with Databricks Delta

Databricks

"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets. Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain. This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT YOU’LL LEARN: – Understand the key data reliability and performance data pipelines challenges – How Databricks Delta helps build robust pipelines at scale – Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements – How to deliver performance gains using Delta PREREQUISITES: – A fully-charged laptop (8-16GB memory) with Chrome or Firefox – Pre-register for Databricks Community Edition" Speakers: Steven Yu, Burak Yavuz

Money Heist - A Stream Processing Original! | Meha Pandey and Shengze Yu, Net...

HostedbyConfluent

Netflix will spend roughly 19B+ on content in 2021 over 100 countries to entertain over 200M subscribers. With this scale of investment in internationalized productions, it is essential to intelligently invest and track the movement of cash, in near-real-time, from pre-production to launch. This involves complex near real time financial calculations, data movement, consistent data views of transactional data of the present and the cash predictions of the future. It needs exactly once processing semantics and idempotency while guaranteeing a low latency serving layer. We leverage kafka to build a scalable and fault tolerance event-based processing engine to provide the right SLA guarantees. We also embrace event-driven data materialization to provide low latency lookups at scale. The aim of this talk is to provide an insight into how we built such a scalable system while embracing a full blown Kappa architecture, with Kafka at the heart of it all.

Regulatory Reporting of Asset Trading Using Apache Spark-(Sudipto Shankar Das...

Spark Summit

Cloud Connect 2012, Big Data @ Netflix

Jerome Boulon

Big Data Day LA 2016/ Use Case Driven track - Hydrator: Open Source, Code-Fre...

Data Con LA

xGem Data Stream Processing

Jorge Hirtz

What's hot

ASPgems - kappa architecture

Juantomás García Molina

Near Real-Time Analytics with Apache Spark: Ingestion, ETL, and Interactive Q...

Databricks

Headaches and Breakthroughs in Building Continuous Applications

Databricks

The Evolution of Big Data Pipelines at Intuit

DataWorks Summit/Hadoop Summit

"Who Moved my Data? - Why tracking changes and sources of data is critical to...

Cask Data

Druid Overview by Rachel Pedreschi

Brian Olsen

Real-time Distributed Stream Processing@ Scale

Jerome Boulon

Visual Mapping of Clickstream DataDataWorks Summit

Realtime streaming architecture in INFINARIO

Jozo Kovac

Conquering the Lambda architecture in LinkedIn metrics platform with Apache C...

Khai Tran

The Stream is the Database - Revolutionizing Healthcare Data Architecture

DataWorks Summit/Hadoop Summit

Building Data Pipelines with Spark and StreamSets

Pat Patterson

Lambda architecture for real time big data

Trieu Nguyen

Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...

HostedbyConfluent

Make streaming processing towards ANSI SQL

DataWorks Summit

Introduction to Apache Apex by Thomas Weise

Big Data Spain

Building Robust Production Data Pipelines with Databricks Delta

Databricks

Money Heist - A Stream Processing Original! | Meha Pandey and Shengze Yu, Net...

HostedbyConfluent

Regulatory Reporting of Asset Trading Using Apache Spark-(Sudipto Shankar Das...

Spark Summit

Cloud Connect 2012, Big Data @ Netflix

Jerome Boulon

What's hot (20)

ASPgems - kappa architecture

Near Real-Time Analytics with Apache Spark: Ingestion, ETL, and Interactive Q...

Headaches and Breakthroughs in Building Continuous Applications

The Evolution of Big Data Pipelines at Intuit

"Who Moved my Data? - Why tracking changes and sources of data is critical to...

Druid Overview by Rachel Pedreschi

Real-time Distributed Stream Processing@ Scale

Visual Mapping of Clickstream Data

Realtime streaming architecture in INFINARIO

Conquering the Lambda architecture in LinkedIn metrics platform with Apache C...

The Stream is the Database - Revolutionizing Healthcare Data Architecture

Building Data Pipelines with Spark and StreamSets

Lambda architecture for real time big data

Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...

Make streaming processing towards ANSI SQL

Introduction to Apache Apex by Thomas Weise

Building Robust Production Data Pipelines with Databricks Delta

Money Heist - A Stream Processing Original! | Meha Pandey and Shengze Yu, Net...

Regulatory Reporting of Asset Trading Using Apache Spark-(Sudipto Shankar Das...

Cloud Connect 2012, Big Data @ Netflix

Similar to Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Big Data Day LA 2016/ Use Case Driven track - Hydrator: Open Source, Code-Fre...

Data Con LA

xGem Data Stream Processing

Jorge Hirtz

API and Big Data Solution Patterns WSO2

What’s New in Syncsort Integrate? New User Experience for Fast Data Onboarding

Precisely

We are excited to announce the new general availability of the intuitive graphical interface for DataFunnel™. This browser-based point-and-click interface gives you the ability to move hundreds of relational tables to a different RSBMS – or to Hadoop – in just minutes! Select the schema of tables you’d like to move, filter out any tables, columns or rows you’d like to exclude, and invoke – all with the click of a mouse – in a user-friendly wizard interface. View this webinar on-demand, where we discussed the newest features in Syncsort DMX/DMX-h, DMX CDC and DataFunnel™. During this webinar, you will see a special sneak peek of some of the new exciting additions coming soon to the Syncsort data integration product family! Webinar key takeaways: • Learn about the newest features in the Syncsort Integrate product family • Get a sneak preview of interesting Integrate features coming soon • See the new intuitive independent DataFunnel™ platform interface

SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)

Surendar S

Kafka: Journey from Just Another Software to Being a Critical Part of PayPal ...

confluent

PayPal currently processes tens of billions of signals per day from different sources in batch and streaming mode. The data processing platform is the one powering these different analytical needs and use cases, not just at PayPal but our adjacencies like Venmo, Hyperwallet and iZettle. End users of this platform demand access to data insights with as much flexibility as possible to explore it with low processing latency. One such use case is where our Switchboard(data de-multiplexer) platform where we process approximately 20 billion events daily and provide data to different teams and platforms with-in PayPal and also to platform outside PayPal for more insights. When we started building this platform Kafka was just another asynchronous message processing platform for us but we have seen it evolving to a place where its adds value not just in terms of event processing but also for platform resiliency and scalability. Takeaway for the audience: Most people work with and have knowledge about data. With this talk I want to present information which is relevant and meaningful to the audience. Information and examples which will make it easier for attendees to understand our complex system and hopefully have some practical takeaways to use Kafka for similar problems on their hand.

DataStax & O'Reilly Media: Large Scale Data Analytics with Spark and Cassandr...

DataStax Academy

In this in-depth workshop you will gain hands on experience with using Spark and Cassandra inside the DataStax Enterprise Platform. The focus of the workshop will be working through data analytics exercises to understand the major developer developer considerations. You will also gain an understanding of the internals behind the integration that allow for large scale data loading and analysis. It will also review some of the major machine learning libraries in Spark as an example of data analysis. The workshop will start with a review the basics of how Spark and Cassandra are integrated. Then we will work through a series of exercises that will show how to perform large scale Data Analytics with Spark and Cassandra. A major part of the workshop will be to understand effective data modeling techniques in Cassandra that allow for fast parallel loading of the data into Spark to perform large scale analytics on that data. The exercises will also look at how to how to use the open source Spark Notebook to run interactive data analytics with the DataStax Enterprise Platform.

A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)

Spark Summit

Cowboy Dating with Big Data or DWH Evolution in Action, Борис Трофимов

Sigma Software

ACDKOCHI19 - Next Generation Data Analytics Platform on AWS

AWS User Group Kochi

Oow2016 review--paas-microservices-

Getting value from IoT, Integration and Data Analytics

Log Data Analysis Platform by Valentin Kropov

SoftServe

Log Data Analysis Platform

Valentin Kropov

Visualizing Big Data in Realtime

DataWorks Summit

Apache Apex brings you the power to quickly build and run big data batch and stream processing applications. But what about visualizing your data in real time as it flows through the Apache Apex applications? Together, we will review Apache Apex, and how it integrates with Apache Hadoop and Apache Kafka to process your big data with streaming computation. Then we will explore the options available to visualize Apex applications metrics and data, including open-source options like REST and PubSub mechanisms in StrAM, as well as features available in the RTS Console like real-time Dashboards and Widgets. We will also look into ways of packaging dashboards inside your Apache Apex applications.

Architecting Petabyte Scale AI Applications

Yahoo Developer Network

Big Data Analytics Platforms by KTH and RISE SICS

Big Data Value Association

SnappyData Toronto Meetup Nov 2017

SnappyData

SnappyData @ Seattle Spark Meetup

SnappyData

Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Da...

Amazon Web Services

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we first present an end-to-end streaming data solution using Amazon Kinesis Data Streams for data ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose for persistence. We review in detail how to write SQL queries for operational monitoring using Kinesis Data Analytics. Learn how PNNL is building their ingestion flow into their Serverless Data Lake leveraging the Kinesis Platform. At times migrating existing NiFi Processes where applicable to various parts of the Kinesis Platform, replacing complex flows on Nifi to bundle and compress the data with Kinesis Firehose, leveraging Kinesis Streams for their enrichment and transformation pipelines, and using Kinesis Analytics to Filter, Aggregate, and detect anomalies.

Analyzing the World's Largest Security Data Lake!

DataWorks Summit

Similar to Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop (20)

Big Data Day LA 2016/ Use Case Driven track - Hydrator: Open Source, Code-Fre...

xGem Data Stream Processing

API and Big Data Solution Patterns

What’s New in Syncsort Integrate? New User Experience for Fast Data Onboarding

SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)

Kafka: Journey from Just Another Software to Being a Critical Part of PayPal ...

DataStax & O'Reilly Media: Large Scale Data Analytics with Spark and Cassandr...

A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)

Cowboy Dating with Big Data or DWH Evolution in Action, Борис Трофимов

ACDKOCHI19 - Next Generation Data Analytics Platform on AWS

Oow2016 review--paas-microservices-

Log Data Analysis Platform by Valentin Kropov

Log Data Analysis Platform

Visualizing Big Data in Realtime

Architecting Petabyte Scale AI Applications

Big Data Analytics Platforms by KTH and RISE SICS

SnappyData Toronto Meetup Nov 2017

SnappyData @ Seattle Spark Meetup

Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Da...

Analyzing the World's Largest Security Data Lake!

Recently uploaded

digital fundamental by Thomas L.floydl.pdf

drwaing

sieving analysis and results interpretation

ssuser36d3051

AIR POLLUTION lecture EnE203 updated.pdf

RicletoEspinosa1

ACRP 4-09 Risk Assessment Method to Support Modification of Airfield Separat...

Mukeshwaran Balu

basic-wireline-operations-course-mahmoud-f-radwan.pdf

NidhalKahouli2

Understanding Inductive Bias in Machine Learning

SUTEJAS

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.

[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf

awadeshbabu

在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样

obonagu

学校原件一模一样【微信：741003700 】《(ANU毕业证书)澳洲国立大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、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

14 Template Contractual Notice - EOT Application

SyedAbiiAzazi1

Fundamentals of Electric Drives and its applications.pptx

manasideore6

RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...

thanhdowork

一比一原版(Otago毕业证)奥塔哥大学毕业证成绩单如何办理

dxobcob

Otago毕业证原版定制【微信：176555708】【奥塔哥大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。 ◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆ 【主营项目】一.毕业证【微信：176555708】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【微信：176555708】 ◇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→ 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。 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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！学历顾问：微信：176555708

Water billing management system project report.pdf

Kamal 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.

Swimming pool mechanical components design.pptx

yokeleetan1

Planning Of Procurement o different goods and services

JoytuBarua2

Building Electrical System Design & Installation

symbo111

Literature Review Basics and Understanding Reference Management.pptx

Dr Ramhari Poudyal

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.

一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理

bakpo1

SFU毕业证原版定制【微信：176555708】【西蒙菲莎大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。 ◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆ 【主营项目】一.毕业证【微信：176555708】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【微信：176555708】 ◇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→ 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。 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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！学历顾问：微信：176555708

一比一原版(UofT毕业证)多伦多大学毕业证成绩单如何办理

ydteq

UofT毕业证原版定制【微信：176555708】【多伦多大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。 ◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆ 【主营项目】一.毕业证【微信：176555708】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【微信：176555708】 ◇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→ 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。 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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！学历顾问：微信：176555708

Recently uploaded (20)

digital fundamental by Thomas L.floydl.pdf

sieving analysis and results interpretation

AIR POLLUTION lecture EnE203 updated.pdf

ACRP 4-09 Risk Assessment Method to Support Modification of Airfield Separat...

basic-wireline-operations-course-mahmoud-f-radwan.pdf

Understanding Inductive Bias in Machine Learning

[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf

在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样

14 Template Contractual Notice - EOT Application

Fundamentals of Electric Drives and its applications.pptx

RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...

一比一原版(Otago毕业证)奥塔哥大学毕业证成绩单如何办理

Water billing management system project report.pdf

Swimming pool mechanical components design.pptx

Planning Of Procurement o different goods and services

Building Electrical System Design & Installation

Literature Review Basics and Understanding Reference Management.pptx

NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...

一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理

一比一原版(UofT毕业证)多伦多大学毕业证成绩单如何办理

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

1. Monitoring at Scale over diverse data sources @ PayPal – Druid, TSDB, Hadoop

3. PayPal Command Center Powered by Monitoring Team 3

4. PayPal Monitoring Ecosystem - Sherlock 4 APIs Logs Processing Metric Processing Insights Engine Alerts Visualization Events Stream Metrics Stream Topology Logs Stream Pools & Hosts PaaS IaaS Network Databases CAL (Logging) Remediation Orchestration Configuration Sherlock is a unified monitoring, alerting and logging platform that exposes real time data and insights, enabled by an extensible visualization platform. Developers Analysts Command Center SRE

6. Ingest Queue Process Store Query PayPal Monitoring Platform - Architecture 6 MessageBus App Logs AppApp S e r v i c e s DBLB NO SQ L Metrics Stream processing MetricsLogs Log Views Hadoop Reports Custom Visualizations Alerts

13. Thank You

Editor's Notes

https://www.paypal.com/stories/us/a-behind-the-scenes-tour-of-the-paypal-command-center http://www.mercurynews.com/business/ci_29276113/q-paypals-command-center-guru-sri-shivananda?source=email Paypal processd $8773 in payments every second in Q3 2015 In 2015, 28% of the 4.9 billion payments we processed were made on a mobile device. With our 192 million active customer accounts, PayPal is a truly global payments platform that is available to people in more than 200 markets, allowing customers to get paid in more than 100 currencies, withdraw funds to their bank accounts in 56 currencies and hold balances in their PayPal accounts in 25 currencies. For more information on PayPal, visit https://www.paypal.com/about. For PYPL financial information, visit https://investor.paypal-corp.com. From http://www.businesswire.com/news/home/20161026005468/en/Binge-Watching-Big-Buys-Bed-PayPal-Predicts-Holiday PayPal currently operates in 200 countries around the world, with 192 million people using its platform. Its goal is to help small businesses grow by exposing them to the global consumer
TTD, TTR https://github.com/paypal/squbs - https://www.paypal-engineering.com/2016/05/11/squbs-a-new-reactive-way-for-paypal-to-build-applications/ Actor model (message based system) of computation – Akka and spary as the http library

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Similar to Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop (20)

Recently uploaded

Recently uploaded (20)

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Editor's Notes