O pequeno alquimista e o elixir da longa vida

Mais conteúdo relacionado

Destaque

Apache Apex (http://apex.apache.org/) is a stream processing platform that helps organizations to build processing pipelines with fault tolerance and strong processing guarantees. It was built to support low processing latency, high throughput, scalability, interoperability, high availability and security. The platform comes with Malhar library - an extensive collection of processing operators and a wide range of input and output connectors for out-of-the-box integration with an existing infrastructure. In the talk I am going to describe how connectors together with the distributed checkpointing (a mechanism used by the Apex to support fault tolerance and high availability) provide exactly-once end-to-end processing guarantees. Speakers: Vlad Rozov is Apache Apex PMC member and back-end engineer at DataTorrent where he focuses on the buffer server, Apex platform network layer, benchmarks and optimizing the core components for low latency and high throughput. Prior to DataTorrent Vlad worked on distributed BI platform at Huawei and on multi-dimensional database (OLAP) at Hyperion Solutions and Oracle.

February 2017 HUG: Exactly-once end-to-end processing with Apache Apex

Yahoo Developer Network

One of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.

Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...

Spark Summit

Session from JFokus 2017 (https://www.jfokus.se/jfokus/talks.jsp#ComparisonofOpenSour) in Stockholm, Sweden. This session shows and compares open source frameworks built to develop very lightweight applications or microservices, which can be deployed on small devices with very low resources and wire together all different kinds of hardware devices, APIs and online services. The focus of this session is the comparison of open source projects such as Node-RED or Flogo, which offer a zero-code environment with web IDE for building and deploying integration and data processing directly onto connected devices using IoT standards such as MQTT, WebSockets or CoaP, but also other interfaces such as Twitter feeds or REST services. The end of the session compares these open source projects to other options such as SaaS offerings like AWS IoT or more powerful streaming analytics platforms.

Comparison of Open Source Frameworks for Integrating the Internet of Things

Kai Wähner

AMIS SIG - Introducing Apache Kafka - Scalable, reliable Event Bus & Message ...

Lucas Jellema

Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms. Apache Apex is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data. Apache Apex is a native Hadoop data-in-motion platform. We will discuss architectural differences between Apache Apex features with Spark Streaming. We will discuss how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion. We will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. We will also discuss how these features affect time to market and total cost of ownership.

Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming

Apache Apex

Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. We implemented a novel entity type recognition system which combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using Spark MLlib. In order to enable real-time recognition of entity types, we utilize Apache Solr to cache the embedding vectors generated by Spark MLlib. This approach enable us to recognize entity types for entities expressed in search queries in less than 60 milliseconds which makes this system applicable for real-time entity type recognition.

How to Integrate Spark MLlib and Apache Solr to Build Real-Time Entity Type R...

Spark Summit

Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. If your data is already in one of Spark Streaming’s well-supported message queuing systems, this is easy. If not, an ad hoc solution to import data may work for a single application, but trying to scale that approach to complex data pipelines integrating dozens of data sources and sinks with multi-stage processing quickly breaks down. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier. This talk will first describe some data pipeline anti-patterns we have observed and motivate the need for a tool designed specifically to bridge the gap between other data systems and stream processing frameworks. We will introduce Kafka Connect, starting with basic usage, its data model, and how a variety of systems can map to this model. Next, we’ll explain how building a tool specifically designed around Kafka allows for stronger guarantees, better scalability, and simpler operationalization compared to other general purpose data copying tools. Finally, we’ll describe how combining Kafka Connect and Spark Streaming, and the resulting separation of concerns, allows you to manage the complexity of building, maintaining, and monitoring large scale data pipelines.

Building Realtime Data Pipelines with Kafka Connect and Spark Streaming: Spar...

Spark Summit

Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle. Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice. In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.

Introducing Kafka's Streams API

confluent

Slides from http://www.meetup.com/Hadoop-User-Group-Munich/events/230313355/ This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.

Apache Apex: Stream Processing Architecture and Applications

Thomas Weise

Honoré de Balzac - O elixir da longa vida

Francis Monteiro da Rocha

Objets connectés : "We are able !"

OCTO Technology

Large-scale data stream processing has come a long way to where it is today. It combines all the essential requirements of modern data analytics: subsecond latency, high throughput and impressively, strong consistency. Apache Flink is a system that serves as a proof-of-concept of these characteristics and it is mainly well-known for its lightweight fault tolerance. Data engineers and analysts can now let the system handle Terabytes of computational state without worrying about failures that can potentially occur. This presentation describes all the fundamental challenges behind exactly-once processing guarantees in large-scale streaming in a simple and intuitive way. Furthermore, it demonstrate the basic and extended versions of Flink's state-of-the-art snapshotting algorithm tailored to the needs of a dataflow graph.

Not Less, Not More: Exactly Once, Large-Scale Stream Processing in Action

Paris Carbone

Introductory Virtual Reality in Unity3d

Bond University

La tortuga-veloz

Mary Carrillo Dominguez

Kinematika gerak rpp

SMA Negeri 9 KERINCI

Destaque (15)

February 2017 HUG: Exactly-once end-to-end processing with Apache Apex

Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...

Comparison of Open Source Frameworks for Integrating the Internet of Things

AMIS SIG - Introducing Apache Kafka - Scalable, reliable Event Bus & Message ...

Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming

How to Integrate Spark MLlib and Apache Solr to Build Real-Time Entity Type R...

Building Realtime Data Pipelines with Kafka Connect and Spark Streaming: Spar...

Introducing Kafka's Streams API

Apache Apex: Stream Processing Architecture and Applications

Honoré de Balzac - O elixir da longa vida

Objets connectés : "We are able !"

Not Less, Not More: Exactly Once, Large-Scale Stream Processing in Action

Introductory Virtual Reality in Unity3d

La tortuga-veloz

Kinematika gerak rpp

Último

PROJETO DE EXTENSÃO I - TERAPIAS INTEGRATIVAS E COMPLEMENTARES.pdf

HELENO FAVACHO

Seminário Biologia e desenvolvimento da matrinxa.pptx

ReinaldoMuller1

DeClara n.º 75 Abril 2024 - O Jornal digital do Agrupamento de Escolas Clara ...

IsabelPereira2010

19- Pedagogia (60 mapas mentais) - Amostra.pdf

marlene54545

Projeto_de_Extensão_Agronomia_adquira_ja_(91)_98764-0830.pdf

HELENO FAVACHO

About Vila Galé- Cadeia Empresarial de Hotéis

ines09cachapa

Projeto de Extensão - ENGENHARIA DE SOFTWARE - BACHARELADO.pdf

HELENO FAVACHO

TCC_MusicaComoLinguagemNaAlfabetização-ARAUJOfranklin-UFBA.pdf

amarianegodoi

Teoria heterotrófica e autotrófica dos primeiros seres vivos..pptx

TailsonSantos1

PROJETO DE EXTENSÃO I - SERVIÇOS JURÍDICOS, CARTORÁRIOS E NOTARIAIS.pdf

HELENO FAVACHO

aula de bioquímica bioquímica dos carboidratos.ppt

ssuser2b53fe

PROJETO DE EXTENSÃO I - Radiologia Tecnologia

HELENO FAVACHO

Currículo - Ícaro Kleisson - Tutor acadêmico.pdf

Tutor de matemática Ícaro

PROJETO DE EXTENSÃO I - TECNOLOGIA DA INFORMAÇÃO Relatório Final de Atividade...

HELENO FAVACHO

EDUCAÇÃO ESPECIAL NA PERSPECTIVA INCLUSIVA

ssuser2ad38b

PROJETO DE EXTENSÃO - EDUCAÇÃO FÍSICA BACHARELADO.pdf

HELENO FAVACHO

Aula de jornada de trabalho - reforma.ppt

Pedro Luis Moraes

Ler e conhecer as competências específicas referentes à sua área de conhecimento de acordo com a BNCC, bem como a proposta de objetos de conhecimento e habilidades prevista para uma série de sua escolha, conforme segmento de atuação de seu curso de graduação (Por exemplo: Pedagogia, Educação Física e Arte: do 1º ao 5º ano; Letras, Matemática, História, Geografia: do 6º ao 9º ano; Filosofia, Sociologia: 1º ao 3º ano do Ensino Médio). O curso de Educação Especial abrange todos os segmentos mencionados, com foco em estudantes com necessidades educacionais especiais.

PRÁTICAS PEDAGÓGICAS GESTÃO DA APRENDIZAGEM

HELENO FAVACHO

Recomposiçao em matematica 1 ano 2024 - ESTUDANTE 1ª série.pdf

Francisco Márcio Bezerra Oliveira

Nossas concepções estão explicitadas no nosso fazer e agir, envolvendo desde o atendimento que se faz na entrada da escola, aos cartazes informativos espalhados pelos corredores, na relação entre os estudantes e professores, funcionários e equipe gestora. Atendendo jovens, adultos e idosos, pessoas com e sem deficiência uma marca do trabalho do CIEJA, é o de contemplarmos as mais diversas histórias de vida e apostarmos em uma educação que considera essa diversidade.

P P P 2024 - *CIEJA Santana / Tucuruvi*

Viviane Moreiras

O pequeno alquimista e o elixir da longa vida

Recomendados

Recomendados

Mais conteúdo relacionado

Destaque

Destaque (15)

Último

Último (20)