Timeline Service Next Gen (YARN-2928): YARN BOF @ Hadoop Summit 2015

Data Warehousing in the Era of Big Data

The mission of Uber is to make transportation as reliable as running water. The business is fundamentally driven by real-time data -- more than half of the employees in Uber, many of whom are non-technical, use SQL on a regular basis to analyze data and power their business decisions. We are building AthenaX, a stream processing platform built on top of Apache Flink to enable our users to write SQL to process real-time data efficiently and reliably at Uber's scale. Using Apache Calcite as query parser, AthenaX compiles the SQL down to Flink jobs. Leveraging Flink's unique streaming capabilities, AthenaX supports (1) consistent computations reliably thanks to at-least-once guarantees, (2) nontrivial analytics (e.g., windowing and joins) on multiple data sources, and (3) efficient and cost-effective executions in production through code generation and elastic scaling.

Flink Forward SF 2017: Bill Liu & Haohui Mai - AthenaX : Uber’s streaming pro...

Flink Forward

Stream processing is rapidly adopted by the enterprise. While in the past, stream processing frameworks mostly provided Java or Scala-based APIs, stream processing with SQL is recently gaining a lot of attention because it makes stream processing accessible to non-programmers and significantly reduces the effort to solve common tasks. About three years ago, the Apache Flink community started adding SQL support to process static and streaming data in a unified fashion. Today, Flink SQL powers production systems at Alibaba, Huawei, Lyft, and Uber. In this talk, I will discuss the current state of Flink’s SQL support and explain the importance of Flink’s unified approach to process static and streaming data. Once the basics are covered, I will present common real-world use cases ranging from low-latency ETL to pattern detection and demonstrate how easily they can be addressed by Flink SQL.

Webinar: Flink SQL in Action - Fabian Hueske

Ververica

Mobius: C# Language Binding For Spark

Spark Summit

Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.

Running Non-MapReduce Big Data Applications on Apache Hadoop

hitesh1892

A Pluggable Autoscaling System @ UCC

Chris Bunch

Performance Tuning RocksDB for Kafka Streams’ State Stores

Kafka Streams and the addition of KSQL has provided opportunities do stateful processing of data. Sometimes, the biggest challenge is determining how you can join that data. Keying and windowing are core concepts that need to be understood in order to properly and efficiently stream data. In this presentation, Neil will utilize geospatial data to showcase non-trivial joining; particularly, but not limited to, distance comparisons. The stream processing will be written in Kafka Streams DSL and in KSQL with the topologies being compared. KSQL 2.0 concepts of User Defined Functions (UDFs), nested AVRO structures, and ‘insert into’ functionality of KSQL will be showcased. The presentation will show a custom OpenSky Connector for obtaining real-time aircraft, a Streams application for processing that data, a D3 topojson application to visualize the data, and an addition KSQL implementation of the streams application for comparison. Expect a deep dive into the Streams DSL and KSQL implementations that will provide the bases into a discussion around Apache Kafka and stream processing.

Confluent Kafka and KSQL: Streaming Data Pipelines Made Easy

Kairo Tavares

Using Location Data to Showcase Keys, Windows, and Joins in Kafka Streams DSL...

Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems. However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes. We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector. eventbrite_kafka_summit_event_logo_v3-035858-edited.png

Kafka Connect by Datio

Datio Big Data

Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...

Whether you are deploying a new application in Microservices or transitioning from a monolithic database application to a cloud-ready architecture, you will inevitably face the decision of either creating a service mesh of API’s – or – using an event bus for better durability, reliability and extensibility of your application. If you choose to go the event bus route, Kafka is an excellent choice for several reasons. One key technology not to overlook is Avro Schemas. They provide a definition for your event payload, just like an API, to ensure all of the event consumers can reliably consume the events. They also handle schema evolution as requirements change and much, much more. In this talk we will discuss all the nuances and considerations around using Avro Schemas for your JSON event payloads. From developer tools, to DevOps approaches, versioning, governance and some “gotchas” we found when working with Avro Schemas and the Confluent Schema Registry.

Kafka for Microservices – You absolutely need Avro Schemas! | Gerardo Gutierr...

HostedbyConfluent

Analytics Beyond RAM Capacity using R

Alex Palamides

KSQL Intro

by Tony Gibbs, Data Warehouse Specialist SA, AWS Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.

Data Warehousing in the Era of Big Data: Deep Dive into Amazon Redshift

Data Con LA 2020 Description The data warehouse has been an analytics workhorse for decades. Unprecedented volumes of data, new types of data, and the need for advanced analyses like machine learning brought on the age of the data lake. But Hadoop by itself doesn't really live up to the hype. Now, many companies have a data lake, a data warehouse, or a mishmash of both, possibly combined with a mandate to go to the cloud. The end result can be a sprawling mess, a lot of duplicated effort, a lot of missed opportunities, a lot of projects that never made it into production, and a lot of financial investment without return. Technical and spiritual unification of the two opposed camps can make a powerful impact on the effectiveness of analytics for the business overall. Over time, different organizations with massive IoT workloads have found practical ways to bridge the artificial gap between these two data management strategies. Look under the hood at how companies have gotten IoT ML projects working, and how their data architectures have changed over time. Learn about new architectures that successfully supply the needs of both business analysts and data scientists. Get a peek at the future. In this area, no one likes surprises. *Look at successful data architectures from companies like Philips, Anritsu, Uber, *Learn to eliminate duplication of effort between data science and BI data engineering teams *Avoid some of the traps that have caused so many big data analytics implementations to fail *Get AI and ML projects into production where they have real impact, without bogging down essential BI *Study analytics architectures that work, why and how they work, and where they're going from here Speaker Paige Roberts,Vertica, Open Source Relations Manager

Unifying Analytics

Data Con LA

Deep Dive Redshift, with a focus on performance

The next generation of Hadoop MapReduce Arun C. Murthy presented the plans for the next generation of Apache Hadoop MapReduce. The MapReduce framework has hit a scalability limit around 4,000 machines. We are developing the next generation of MapReduce that factors the framework into a generic resource scheduler and a per-job, user-defined component that manages the application execution. Since downtime is more expensive at scale high-availability is built-in from the beginning; as are security and multi-tenancy to support many users on the larger clusters. The new architecture will also increase innovation, agility and hardware utilization. More information and video available at: http://developer.yahoo.com/blogs/hadoop/posts/2011/02/hug-feb-2011-recap/

Failing to Cross the Streams – Lessons Learned the Hard Way | Philip Schmitt,...

HostedbyConfluent

Tendances (20)

Spark and Bloomberg by Sudarshan Kadambi and Partha Nageswaran

So You Want to Write a Connector?

Data Warehousing in the Era of Big Data

Flink Forward SF 2017: Bill Liu & Haohui Mai - AthenaX : Uber’s streaming pro...

Webinar: Flink SQL in Action - Fabian Hueske

Mobius: C# Language Binding For Spark

Running Non-MapReduce Big Data Applications on Apache Hadoop

A Pluggable Autoscaling System @ UCC

Performance Tuning RocksDB for Kafka Streams’ State Stores

Confluent Kafka and KSQL: Streaming Data Pipelines Made Easy

Using Location Data to Showcase Keys, Windows, and Joins in Kafka Streams DSL...

Kafka Connect by Datio

Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...

Kafka for Microservices – You absolutely need Avro Schemas! | Gerardo Gutierr...

Analytics Beyond RAM Capacity using R

KSQL Intro

Data Warehousing in the Era of Big Data: Deep Dive into Amazon Redshift

Unifying Analytics

Deep Dive Redshift, with a focus on performance

Failing to Cross the Streams – Lessons Learned the Hard Way | Philip Schmitt,...

En vedette

OSGi at eBay: JavaOne 2010

Sangjin Lee

MapReduce is History: Deep Integration of Tez with YARN's Timeline Server

DataWorks Summit

Application Timeline Server - Past, Present and Future

VARUN SAXENA

Analyzing Historical Data of Applications on YARN for Fun and Profit

DataWorks Summit

Next Generation Hadoop Operations

Owen O'Malley

Next Generation MapReduce

Owen O'Malley

Bay Area HUG Feb 2011 Intro

Owen O'Malley

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. Upstream data 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 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, whether you're processing data 'off-cluster', in Spark, or in MapReduce. StreamSets software delivers performance management for data flows that feed the next generation of big data applications. Its mission is to bring operational excellence to the management of data in motion, so that data arrives on time and with quality, accelerating analysis and decision making. StreamSets Data Collector is in use at hundreds of companies where it brings unprecedented visibility into and control over data as it moves between an expanding variety of sources and destinations. Speakers: Pat Patterson has been working with Internet technologies since 1997, building software and working with communities at Sun Microsystems, Huawei, Salesforce and StreamSets. At Sun, Pat was the community lead for the OpenSSO open source project, while at Huawei he developed cloud storage infrastructure software. Part of the developer evangelism team at Salesforce, Pat focused on identity, integration and the Internet of Things. Now community champion at StreamSets, Pat is responsible for the care and feeding of the StreamSets open source community.

August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector

Yahoo Developer Network

First part of the talk will describe the anatomy of a typical data pipeline and how Apache Oozie meets the demands of large-scale data pipelines. In particular, we will focus on recent advancements in Oozie for dependency management among pipeline stages, incremental and partial processing, combinatorial, conditional and optional processing, priority processing, late processing and BCP management. Second part of the talk will focus on out of box support for spark jobs. Speakers: Purshotam Shah is a senior software engineer with the Hadoop team at Yahoo, and an Apache Oozie PMC member and committer. Satish Saley is a software engineer at Yahoo!. He contributes to Apache Oozie.

August 2016 HUG: Recent development in Apache Oozie

Yahoo Developer Network

Spark and Ignite are two of the most popular open source projects in the area of high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next generation real-time applications is to use them together? In this session, Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and CPO at GridGain will explain in detail how IgniteRDD — an implementation of native Spark RDD and DataFrame APIs — shares the state of the RDD across other Spark jobs, applications and workers. Dmitriy will also demonstrate how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames. Don't miss this opportunity to learn from one of the experts how to use Spark and Ignite better together in your projects. Speakers: Dmitriy Setrakyan, is a founder and CPO at GridGain Systems. Dmitriy has been working with distributed architectures for over 15 years and has expertise in the development of various middleware platforms, financial trading systems, CRM applications and similar systems. Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of an add-serving system processing several billion hits a day. Currently Dmitriy also acts as PMC chair of Apache Ignite project.

August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...

Yahoo Developer Network

En vedette (10)

OSGi at eBay: JavaOne 2010

MapReduce is History: Deep Integration of Tez with YARN's Timeline Server

Application Timeline Server - Past, Present and Future

Analyzing Historical Data of Applications on YARN for Fun and Profit

Next Generation Hadoop Operations

Next Generation MapReduce

Bay Area HUG Feb 2011 Intro

August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector

August 2016 HUG: Recent development in Apache Oozie

August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...

Similaire à Timeline Service Next Gen (YARN-2928): YARN BOF @ Hadoop Summit 2015

The Language Server Protocol developed by Microsoft for Visual Studio Code is a language and IDE agnostic protocol which clearly separates language semantics from UI presentation. Language developers can implement the protocol and benefit from immediate support in all IDEs, while IDE developers, who implement the protocol get automatic support for all these languages without having to write any language-specific code. This session will let you learn more about the innards of the LSP. We will also have an overview of the current implementations in Eclipse, and outside Eclipse as well.

Language Server Protocol - Why the Hype?

mikaelbarbero

Full Stack Web Development

SWAGATHCHOWDARY1

JSF 2.3: Integration with Front-End Frameworks

Ian Hlavats

How do you deploy and manage containerized applications at scale? Amazon ECS is a new AWS service that makes it easy to run and manage Docker-enabled applications across a cluster of Amazon EC2 instances. This webinar will familiarize you with the benefits of containers, introduce Amazon EC2 Container Service (ECS), and demonstrate how to use Amazon ECS for your applications. You will learn how to define, schedule, and stop sets of containers. You will also learn how to access the state of your resources to view running tasks and EC2 instance utilization in your cluster. Learning Objectives: • Understand the benefits of containers • Define and deploy containers on Amazon ECS • Access cluster state information to track utilization and unning tasks • Integrate Amazon ECS into your existing software release process or CI/CD (Continuous Integration / Continuous Delivery) pipeline Who Should Attend: • Developers, system administrators, Docker users, container users

AWS April Webinar Series - Getting Started with Amazon EC2 Container Service

The Larch Environment is a visual interactive programming environment for Jython/Python, that makes programming more visual. Its is designed for the creation of visual interactive programs, and programs that operate as interactive technical literature. To this end, protocols for presenting objects visually have been devised. An active document based programming environment builds on the edit-run-debug cycle of a standard console, allowing a programmer to experiment with ideas, and develop visual programs at the same time. Additionally, a way of embellishing source code with visual content is presented. http://sites.google.com/site/larchenv

The Larch - a visual interactive programming environment

Python Ireland

IBM Application Delivery Foundation for z Systems (ADFz) is the perfect starting solution for embarking on a DevOps transformation journey. The solution includes the market-leading Integrated Development Environment (IBM Developer for z Systems), as well as a set of essential problem analysis tools. Come and learn about the latest enhancements of ADFz and how customers are benefiting from this solution. visit http://www-03.ibm.com/software/products/en/ibm-application-delivery-foundation-for-z-systems

InterConnect 2017 : Do You Have the Right Solution for z/OS Application Devel...

DevOps for Enterprise Systems

Step talk

ESUG

AWS Webcast - Build Mobile Apps with a Secure, Scalable Back End on DynamoDB

Rancher and Kubernetes Best Practices

Avinash Patil

Introduction to .NET with C# @ university of wayamba

Prageeth Sandakalum

TDC2018FLN | Trilha Containers - Kubernetes para usuarios Docker.

tdc-globalcode

Introduction to ASP.NET

Joni

Introduction to NoSQL Databases

Derek Stainer

Getting Started with Docker on AWS

By Rafael Benevides and Edson Yanaga Yes, Docker is great. We are all very aware of that, but now it’s time to take the next step: wrapping it all and deploying to a production environment. For this scenario, we need something more. For that “more,” we have Kubernetes by Google, a container platform based on the same technology used to deploy billions of containers per month on Google’s infrastructure. Ready to leverage your Docker skills and package your current Java app (WAR, EAR, or JAR)? Come to this session to see how your current Docker skill set can be easily mapped to Kubernetes concepts and commands. And get ready to deploy your containers in production.

JavaOne 2016: Kubernetes introduction for Java Developers

Rafael Benevides

Kubernetes for Java Developers

Red Hat Developers

Framework určený pre tvorbu ambicioznych webových aplikácií. Alebo o tom, ako som spokojný s knižnicou, ktorá zlepšuje developer experience (DevX). Dozviete sa v čom spočívajú silné stránky embru, čím je unikátna architektúra a prečo je výhodné použiť ho pri multiplatformných riešeniach. Uvediem základné možnosti nasadenia a popíšem benefity a zistenia z praxe (wow's and dont's, deployment, lessons I've learned).

Viliam Elischer - Ember.js - Jak zatopit a neshořet!

Develcz

Ember.js - Jak zatopit a neshořet!

Viliam Elischer

Ever wondered how IDE’s are built? In this talk, we’ll skip the marketing bit and dive into the architecture and implementation of JetBrains Rider. We’ll look at how and why we have built (and open sourced) a reactive protocol, and how the IDE uses a “microservices” architecture to communicate with the debugger, Roslyn, a WPF renderer and even other tools like Unity3D. We’ll explore how things are wired together, both in-process and across those microservices. Let’s geek out!

NDC Sydney 2019 - Microservices for building an IDE – The innards of JetBrain...

Maarten Balliauw

.NET 4 Demystified - Sandeep Joshi

Spiffy

Similaire à Timeline Service Next Gen (YARN-2928): YARN BOF @ Hadoop Summit 2015 (20)

Language Server Protocol - Why the Hype?

Full Stack Web Development

JSF 2.3: Integration with Front-End Frameworks

AWS April Webinar Series - Getting Started with Amazon EC2 Container Service

The Larch - a visual interactive programming environment

InterConnect 2017 : Do You Have the Right Solution for z/OS Application Devel...

Step talk

AWS Webcast - Build Mobile Apps with a Secure, Scalable Back End on DynamoDB

Rancher and Kubernetes Best Practices

Introduction to .NET with C# @ university of wayamba

TDC2018FLN | Trilha Containers - Kubernetes para usuarios Docker.

Introduction to ASP.NET

Introduction to NoSQL Databases

Getting Started with Docker on AWS

JavaOne 2016: Kubernetes introduction for Java Developers

Kubernetes for Java Developers

Viliam Elischer - Ember.js - Jak zatopit a neshořet!

Ember.js - Jak zatopit a neshořet!

NDC Sydney 2019 - Microservices for building an IDE – The innards of JetBrain...

.NET 4 Demystified - Sandeep Joshi

Dernier

Devoxx UK 2024 - Going serverless with Quarkus, GraalVM native images and AWS...

Bert Jan Schrijver

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Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid

Philip Schwarz

Announcing Codolex 2.0 from GDK Software

Jim McKeeth

ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

Architecture decision records - How not to get lost in the past

Papp Krisztián

WSO2CON 2024 - Navigating API Complexity: REST, GraphQL, gRPC, Websocket, Web...

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WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 - Cloud Native Middleware: Domain-Driven Design, Cell-Based Arch...

This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

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