BigDataSpain 2016: Introduction to Apache Apex

Processing big data often requires running the same computations parallelly in multiple processes or threads, called partitions, with each partition handling a subset of the data. This becomes all the more necessary when processing live data streams where maintaining SLA is paramount. Furthermore, multiple different computations make up an application and each of them may have different partitioning needs. Partitioning also needs to adapt to changing data rates, input sources and other application requirements like SLA. In this talk, we will introduce how Apache Apex, a distributed stream processing platform on Hadoop, handles partitioning. We will look at different partitioning schemes provided by Apex some of which are unique in this space. We will also look at how Apex does dynamic partitioning, a feature unique to and pioneered by Apex to handle varying data needs with examples. We will also talk about the different utilities and libraries that Apex provides for users to be able to affect their own custom partitioning.

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.

Introduction to Apache Apex - The next generation native Hadoop platform. This talk will cover details about how Apache Apex can be used as a powerful and versatile platform for big data processing. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch alerts, real-time actions, threat detection, etc. Bio: Pramod Immaneni is Apache Apex PMC member and senior architect at DataTorrent, where he works on Apache Apex and specializes in big data platform and applications. Prior to DataTorrent, he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs.

Apache Apex - Fault Tolerance and Processing Semantics Webinar on demand: https://www.brighttalk.com/webcast/13685/194115

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.

This document discusses transitioning from batch to streaming data processing using Apache Apex. It provides an overview of Apex and how it can be used to build real-time streaming applications. Examples are given of how to build an application that processes Twitter data streams and visualizes results. The document also outlines Apex's capabilities for scalable stream processing, queryable state, and its growing library of connectors and transformations.

- Apache Apex is a platform and framework for building highly scalable and fault-tolerant distributed applications on Hadoop. - It allows developers to build any custom logic as distributed applications and ensures fault tolerance, scalability and data flow. Applications can process streaming or batch data with high throughput and low latency. - Apex applications are composed of operators that perform processing on streams of data tuples. Operators can run in a distributed fashion across a cluster and automatically recover from failures without reprocessing data from the beginning.

This document discusses challenges in building low-latency machine learning applications and how Apache Apex can help address them. It introduces Apache Apex as a distributed streaming engine and describes how it allows embedding models from frameworks like R, Python, H2O through custom operators. It provides various data and model scoring patterns in Apex like dynamic resource allocation, checkpointing, exactly-once processing to meet SLAs. The document also demonstrates techniques like canary deployment, dormant models, model ensembles through logical overlays on the Apex DAG.

Stream data processing is becoming increasingly important to support business needs for faster time to insight and action with growing volume of information from more sources. Apache Apex (http://apex.apache.org/) is a unified big data in motion processing platform for the Apache Hadoop ecosystem. Apex supports demanding use cases with: * Architecture for high throughput, low latency and exactly-once processing semantics. * Comprehensive library of building blocks including connectors for Kafka, Files, Cassandra, HBase and many more * Java based with unobtrusive API to build real-time and batch applications and implement custom business logic. * Advanced engine features for auto-scaling, dynamic changes, compute locality. Apex was developed since 2012 and is used in production in various industries like online advertising, Internet of Things (IoT) and financial services.

This document discusses using Apache Apex for real-time decision making within 2 milliseconds. It provides performance benchmarks for Apex, showing average latency of 0.25ms for over 54 million events with 600GB of RAM. It compares Apex favorably to other streaming technologies like Storm and Flink, noting Apex's self-healing capabilities, independence of operators, and ability to meet latency and throughput requirements even during failures. The document recommends Apex for its maturity, fault tolerance, and ability to meet the goals of latency under 16ms, 99.999% availability, and scalability.

The presentation covers how Apache Apex is used to deliver actionable insights in real-time for Ad-tech. It includes a reference architecture to provide dimensional aggregates on TB scale for billions of events per day. The reference architecture covers concepts around Apache Apex, with Kafka as source and dimensional compute. Slides from Devendra Tagare at Apache Big Data North America in Miami 2017.

David Yan offers an overview of Apache Apex, a stream processing engine used in production by several large companies for real-time data analytics. Apache Apex uses a programming paradigm based on a directed acyclic graph (DAG). Each node in the DAG represents an operator, which can be data input, data output, or data transformation. Each directed edge in the DAG represents a stream, which is the flow of data from one operator to another. As part of Apex, the Malhar library provides a suite of connector operators so that Apex applications can read from or write to various data sources. It also includes utility operators that are commonly used in streaming applications, such as parsers, deduplicators and join, and generic building blocks that facilitate scalable state management and checkpointing. In addition to processing based on ingression time and processing time, Apex supports event-time windows and session windows. It also supports windowing, watermarks, allowed lateness, accumulation mode, triggering, and retraction detailed by Apache Beam as well as feedback loops in the DAG for iterative processing and at-least-once and “end-to-end” exactly-once processing guarantees. Apex provides various ways to fine-tune applications, such as operator partitioning, locality, and affinity. Apex is integrated with several open source projects, including Apache Beam, Apache Samoa (distributed machine learning), and Apache Calcite (SQL-based application specification). Users can choose Apex as the backend engine when running their application model based on these projects. David explains how to develop fault-tolerant streaming applications with low latency and high throughput using Apex, presenting the programming model with examples and demonstrating how custom business logic can be integrated using both the declarative high-level API and the compositional DAG-level API.

Deep Dive into Apache Apex app development presented by Chaitanya Chebolu: Committer for Apache Apex and Software Engineer at DataTorrent

Apache Apex is a stream processing framework that provides high performance, scalability, and fault tolerance. It uses YARN for resource management, can achieve single digit millisecond latency, and automatically recovers from failures without data loss through checkpointing. Apex applications are modeled as directed acyclic graphs of operators and can be partitioned for scalability. It has a large community of committers and is in the process of becoming a top-level Apache project.

Deep dive into how operators reads and writes from/to files in an idempotent manner. This will cover file input operator, file splitter, block reader on the input side and file output operator on the output side. We will present how these operators are made scalable and fault tolerant with the hooks provided by Apache Apex platform.

This talk was presented at the Apache Big Data 2016, North America conference that was held in Vancouver, CA (http://events.linuxfoundation.org/events/archive/2016/apache-big-data-north-america/program/schedule)

Extending Yahoo Streaming computation Benchmark to Apache Apex - Application topology - Comparison of results between Storm, Flink and Apex - Variation of the Apex Benchmarking App with event time and 'results query' support

This brochure summarizes the KKCL Juniors English language and activities program for students aged 11-17. The program focuses on "Go Create! English" lessons that integrate creative workshops like filmmaking, music, web design and interviewing to teach English in a fun way. Students take 20 hours per week of lessons, participate in cultural excursions in London, and can take exam preparation courses. The school aims to help students improve their English while discovering new talents through the creative lessons and activities.

Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.

Jennifer Mintzer Journalism Writing Samples from the Temple University School of Communications and Theater Alumni Magazine

O documento discute estratégias para empreendedores digitais, incluindo 10 nichos de mercado promissores e princípios para estruturar ações mentais e práticas de sucesso. É fornecido um guia detalhado sobre como identificar necessidades de mercado pouco atendidas, focar no valor entregue ao cliente, e usar estratégias comprovadas de marketing digital como geração de tráfego e construção de listas de contatos.

Este documento presenta el acuerdo de la Sala Sexta del Tribunal de Casación Penal de la provincia de Buenos Aires sobre el recurso de casación interpuesto por la defensa de R.M.B., condenada a prisión perpetua por homicidio agravado. El tribunal analiza los motivos de agravio planteados y el contexto sociocultural de R.M.B. como indígena quechua boliviana, concluyendo que se debe tener en cuenta su perspectiva cultural diversa al dictar sentencia.

La demanda contra el Estado o la República puede iniciarse cuando la actividad de los órganos estatales ocasiona daños a los particulares. Existen diferentes tipos de responsabilidad del Estado dependiendo si el daño fue causado por una función administrativa, un acto legislativo o judicial. Para que exista responsabilidad del Estado se requiere que exista un daño cierto a un interés particular, una relación de causalidad entre el daño y la conducta estatal, y que el administrado no tenga el deber jurídico de soportar el daño.

El documento describe las fuentes del Derecho Tributario y Derecho Financiero en Venezuela. El Derecho Tributario se basa principalmente en la Constitución venezolana y el Código Orgánico Tributario. El Derecho Financiero regula el uso de fondos públicos y se fundamenta en la Constitución, leyes y decretos. Ambas ramas se relacionan con otras como el Derecho Constitucional, Administrativo y Penal.

путеводитель по памятным местам, связанным с пребыванием святителя Феофана Вышенского в Орле

Apanhado básico de exemplo de como delinear um projeto digital http://cazulli.com.br/Blog_Como_Aprender_Fazendo/

El documento contiene varias noticias breves de Ituzaingó. Se anuncia que los municipios de Morón, Pilar y Tres de Febrero subsidiarán a clubes y sociedades de fomento en un 50% del aumento de las tarifas de gas y electricidad por 90 días. También se informa sobre la asamblea pública del Consejo Económico y Social de Ituzaingó contra el aumento de tarifas y su petitorio al gobierno nacional. Por otro lado, se celebra el 50 aniversario de la Escuela Primaria