Building Your First Apache Apex (Next Gen Big Data/Hadoop) Application

Sep 13, 2016Download as PPTX, PDF1 like621 views

This document provides an overview of building a first Apache Apex application. It describes the main concepts of an Apex application including operators that implement interfaces to process streaming data within windows. The document outlines a "Sorted Word Count" application that uses various operators like LineReader, WordReader, WindowWordCount, and FileWordCount. It also demonstrates wiring these operators together in a directed acyclic graph and running the application to process streaming data.

© 2015 DataTorrent
Munagala V. Ramanath (“Ram”) <ram@datatorrent.com>
Dec 15th, 2015
Building your First Apache Apex
Application

© 2015 DataTorrent
Outline
Main concepts of an Apex Application.
Brief description of the "Sorted Word Count" application.
Hands on demonstration of cloning the source repository and building
Apex source code.
Hands on demonstration of creating a new application.
Running the application.
Code walk-through.
Questions

© 2015 DataTorrent
Main Concepts
Applications are built from Operators which implement the Operator interface;
each operator has input/output ports which are connected by streams to form a
directed acyclic graph (DAG).
A BaseOperator class is provided which provides empty implementations of all the
required methods.
Within an operator, define necessary input and output ports typically using the
DefaultInputPort and DefaultOutputPort classes.
The Application class implements the StreamingApplication interface; need only
implement populateDAG() method which wires the operators together.
Applications process data within time-based windows, typically 0.5s.

© 2015 DataTorrent
The Sorted Word Count Application
The following operators are involved:
LineReader: reads file dropped into input directory and outputs lines (on its
output port).
WordReader: splits each line into words using a regex.
WindowWordCount: compute and emit word frequencies for all words in lines
processed in current window.
FileWordCount: accumulates all word counts for current file and emits final
sorted list when EOF is reached.
WordCountWriter: writes list to output file in output directory.

© 2015 DataTorrent
Resources
6
Apache Apex Community Page
Apache Apex LinkedIn Group

Presenter - Dr Sandeep Deshmukh, Committer Apache Apex, DataTorrent engineer Abstract: Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. Apache Apex Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. Apache Apex Data Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project. In this series of talks, we would cover how Hadoop Ingestion is made easy using Apache Apex. The third talk in this series would focus on ingesting unbounded data from Kafka to JDBC with couple of processing operators -Transform and enrichment.

Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsApache Apex

Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

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.

Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache 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.

Deep Dive into Apache Apex App DevelopmentApache Apex

Intro to Apache Apex @ Women in Big DataApache Apex

Extending The Yahoo Streaming Benchmark to Apache ApexApache Apex

Apex as yarn applicationChinmay Kolhatkar

Introduction to Apache ApexApache Apex

From Batch to Streaming with Apache Apex Dataworks Summit 2017Apache 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.

Java High Level Stream APIApache Apex

Presenter - Siyuan Hua, Apache Apex PMC Member & DataTorrent Engineer Apache Apex provides a DAG construction API that gives the developers full control over the logical plan. Some use cases don't require all of that flexibility, at least so it may appear initially. Also a large part of the audience may be more familiar with an API that exhibits more functional programming flavor, such as the new Java 8 Stream interfaces and the Apache Flink and Spark-Streaming API. Thus, to make Apex beginners to get simple first app running with familiar API, we are now providing the Stream API on top of the existing DAG API. The Stream API is designed to be easy to use yet flexible to extend and compatible with the native Apex API. This means, developers can construct their application in a way similar to Flink, Spark but also have the power to fine tune the DAG at will. Per our roadmap, the Stream API will closely follow Apache Beam (aka Google Data Flow) model. In the future, you should be able to either easily run Beam applications with the Apex Engine or express an existing application in a more declarative style.

IoT Ingestion & Analytics using Apache Apex - A Native Hadoop PlatformApache Apex

Internet of Things (IoT) devices are becoming more ubiquitous in consumer, business and industrial landscapes. They are being widely used in applications ranging from home automation to the industrial internet. They pose a unique challenge in terms of the volume of data they produce, and the velocity with which they produce it, and the variety of sources they need to handle. The challenge is to ingest and process this data at the speed at which it is being produced in a real-time and fault tolerant fashion. Apache Apex is an industrial grade, scalable and fault tolerant big data processing platform that runs natively on Hadoop. In this deck, you will see how Apex is being used in IoT applications and also see how the enterprise features such as dimensional analytics, real-time dashboards and monitoring play a key role. Presented by Pramod Immaneni, Principal Architect at DataTorrent and PPMC member Apache Apex, on BrightTALK webinar on Apr 6th, 2016

Developing streaming applications with apache apex (strata + hadoop world)Apache Apex

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.

Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex

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.

University program - writing an apache apex applicationAkshay Gore

Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

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.

Intro to Apache Apex - Next Gen Native Hadoop Platform - HackacApache Apex

Apache Apex is a platform and runtime engine that enables development of scalable and fault-tolerant distributed applications on Hadoop in a native fashion. It processes streaming or batch big data with high throughput and low latency. Applications are built from operators that run distributed across a cluster and can scale up or down dynamically. Apex provides automatic recovery from failures without reprocessing and preserves state. It includes a library of common operators to simplify application development.

Apache Apex Meetup at CaskApache Apex

This document provides an overview of Apache Apex, an open source unified streaming and fast batching platform. It discusses key aspects of Apex including its application programming model using operators and directed acyclic graphs, native Hadoop integration using YARN and HDFS, partitioning and scaling operators for high throughput, windowing support, fault tolerance, and data locality features. Examples of building a data processing pipeline and its logical and physical plans are also presented.

Apache Apex: Stream Processing Architecture and ApplicationsThomas Weise

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 Big Data 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

Apache Apex is a next gen big data analytics platform. Originally developed at DataTorrent 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 about the Apex architecture, including its unique features for scalability, fault tolerance and processing guarantees, programming model and use cases. http://apachebigdata2016.sched.org/event/6M0L/next-gen-big-data-analytics-with-apache-apex-thomas-weise-datatorrent

Building your first aplication using Apache ApexYogi Devendra Vyavahare

This document provides an overview of building an Apache Apex application, including key concepts like DAGs, operators, and ports. It also includes an example "word count" application and demonstrates how to define the application and operators, and build Apache Apex from source code. The document outlines the sample application workflow and includes information on resources for learning more about Apache Apex.

Apache Apex Fault Tolerance and Processing SemanticsApache Apex

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

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.

DataTorrent Presentation @ Big Data Application MeetupThomas Weise

The document introduces Apache Apex, an open source unified streaming and batch processing framework. It discusses how Apex integrates with native Hadoop components like YARN and HDFS. It then describes Apex's programming model using directed acyclic graphs of operators and streams to process data. The document outlines Apex's support for scaling applications through partitioning, windowing, fault tolerance, and guarantees on processing semantics. It provides an example of building an application pipeline and shows the logical and physical plans. In closing, it directs the reader to Apache Apex community resources for more information.

Smart Partitioning with Apache Apex (Webinar)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.

Introduction to Apache ApexApache Apex

Architectual Comparison of Apache Apex and Spark StreamingApache Apex

This presentation discusses architectural differences between Apache Apex features with Spark Streaming. It discusses 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. Also, it will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. Further, it will discuss how these features affect time to market and total cost of ownership.

HDFS InternalsApache Apex

HDFS stores files as blocks that are by default 64 MB in size to minimize disk seek times. The namenode manages the file system namespace and metadata, tracking which datanodes store each block. When writing a file, HDFS breaks it into blocks and replicates each block across multiple datanodes. The secondary namenode periodically merges namespace and edit log changes to prevent the log from growing too large. Small files are inefficient in HDFS due to each file requiring namespace metadata regardless of size.

Hadoop Interacting with HDFSApache Apex

More Related Content

What's hot (20)

Apex as yarn applicationChinmay Kolhatkar

Introduction to Apache ApexApache Apex

From Batch to Streaming with Apache Apex Dataworks Summit 2017Apache Apex

Java High Level Stream APIApache Apex

IoT Ingestion & Analytics using Apache Apex - A Native Hadoop PlatformApache Apex

Developing streaming applications with apache apex (strata + hadoop world)Apache Apex

Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex

University program - writing an apache apex applicationAkshay Gore

Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

Intro to Apache Apex - Next Gen Native Hadoop Platform - HackacApache Apex

Apache Apex Meetup at CaskApache Apex

Apache Apex: Stream Processing Architecture and ApplicationsThomas Weise

Apache Big Data 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

Building your first aplication using Apache ApexYogi Devendra Vyavahare

Apache Apex Fault Tolerance and Processing SemanticsApache Apex

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

DataTorrent Presentation @ Big Data Application MeetupThomas Weise

Smart Partitioning with Apache Apex (Webinar)Apache Apex

Introduction to Apache ApexApache Apex

Architectual Comparison of Apache Apex and Spark StreamingApache Apex

Apex as yarn applicationChinmay Kolhatkar

Introduction to Apache ApexApache Apex

From Batch to Streaming with Apache Apex Dataworks Summit 2017Apache Apex

Java High Level Stream APIApache Apex

IoT Ingestion & Analytics using Apache Apex - A Native Hadoop PlatformApache Apex

Developing streaming applications with apache apex (strata + hadoop world)Apache Apex

Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex

University program - writing an apache apex applicationAkshay Gore

Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

Intro to Apache Apex - Next Gen Native Hadoop Platform - HackacApache Apex

Apache Apex Meetup at CaskApache Apex

Apache Apex: Stream Processing Architecture and ApplicationsThomas Weise

Apache Big Data 2016: Next Gen Big Data Analytics with Apache ApexApache Apex

Building your first aplication using Apache ApexYogi Devendra Vyavahare

Apache Apex Fault Tolerance and Processing SemanticsApache Apex

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

DataTorrent Presentation @ Big Data Application MeetupThomas Weise

Smart Partitioning with Apache Apex (Webinar)Apache Apex

Introduction to Apache ApexApache Apex

Architectual Comparison of Apache Apex and Spark StreamingApache Apex

Viewers also liked (20)

HDFS InternalsApache Apex

Hadoop Interacting with HDFSApache Apex

Introduction to Real-Time Data ProcessingApache Apex

Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex

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.

Цветочные легендыNinel Kek

Римский корсаков снегурочкаNinel Kek

High Performance Distributed Systems with CQRSJonathan Oliver

This document discusses the architectural pattern of Command Query Responsibility Segregation (CQRS). It summarizes that CQRS separates read (query) and write (command) operations into different models to allow for more scalability and performance. Queries use a read-only data store optimized for reading, while commands express user intentions and are validated before being asynchronously processed to update data. The pattern allows for eventual consistency by keeping query data slightly stale, and improves scalability by allowing separate optimization of queries and commands.

правописание приставок урок№4HomichAlla

бсп (обоб. урок)HomichAlla

Troubleshooting mysql-tutorialjames tong

Towards True Elasticity of Spark-(Michael Le and Min Li, IBM)Spark Summit

Scaling Spark workloads on YARN and Mesos can provide significant performance improvements but the benefits vary across different workloads. Adding resources alone may not fully utilize the new nodes due to delay in scheduling tasks locally on the new nodes. Tuning the locality wait time parameter in Spark to quickly change task placement preference can help make better use of new resources. Dynamic executor allocation in Spark can also be enhanced to dynamically adjust configuration settings like locality wait time during auto-scaling.

Windowing in Apache ApexApache Apex

Windowing in Apache Apex divides unbounded streaming data into finite time slices called windows to allow for computation. It uses time as a reference to break streams into windows, addressing issues like failure recovery and providing frames of reference. Operators can perform window-level processing by implementing callbacks for window start and end. Windows provide rolling statistics by accumulating results over multiple windows and emitting periodically. Windowing has lower latency than micro-batch systems as records are processed immediately rather than waiting for batch boundaries.

The 5 People in your Organization that grow Legacy CodeRoberto Cortez

Have you ever looked at a random piece of code and wanted to rewrite it so badly? It’s natural to have legacy code in your application at some point. It’s something that you need to accept and learn to live with. So is this a lost cause? Should we just throw in the towel and give up? Hell no! Over the years, I learned to identify 5 main creators/enablers of legacy code on the engineering side, which I’m sharing here with you using real development stories (with a little humour in the mix). Learn to keep them in line and your code will live longer!

Hadoop File System Shell Commands,Hadoop online training

Hadoop basic commandsbispsolutions

This document provides an overview of basic Hadoop commands for interacting with the Hadoop Distributed File System (HDFS). It lists commands for creating directories, listing files, copying data between local and HDFS, copying within HDFS, viewing file contents, deleting files, getting help for commands, and viewing HDFS through a web browser. Contact information is provided at the end for additional support.

Introduction to Apache Apex and writing a big data streaming application Apache Apex

Introduction to Apache Apex - The next generation native Hadoop platform, and writing a native Hadoop big data Apache Apex streaming application. This talk will cover details about how Apex can be used as a powerful and versatile platform for big data. Apache apex is being used in production by customers for both streaming and batch use cases. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch. alerts, real-time actions, threat detection, etc. Presenter : <b>Pramod Immaneni</b> Apache Apex PPMC member and senior architect at DataTorrent Inc, where he works on Apex and specializes in big data 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. Before that he was a technical co-founder of a mobile startup where he was an architect of a dynamic content rendering engine for mobile devices. This is a video of the webcast of an Apache Apex meetup event organized by Guru Virtues at 267 Boston Rd no. 9, North Billerica, MA, on <b>May 7th 2016</b> and broadcasted from San Jose, CA. If you are interested in helping organize i.e., hosting, presenting, community leadership Apache Apex community, please email [email protected]

Build your shiny new pc, with PangolyPangoly

Hadoop Internals (2.3.0 or later)Emilio Coppa

Introduction to UNIX Command-Lines with examplesNoé Fernández-Pozo

Introduction to Map ReduceApache Apex

Here is how you can solve this problem using MapReduce and Unix commands: Map step: grep -o 'Blue\|Green' input.txt | wc -l > output This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches). Reduce step: cat output This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green. So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map

HDFS InternalsApache Apex

Hadoop Interacting with HDFSApache Apex

Introduction to Real-Time Data ProcessingApache Apex

Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex

Цветочные легендыNinel Kek

Римский корсаков снегурочкаNinel Kek

High Performance Distributed Systems with CQRSJonathan Oliver

правописание приставок урок№4HomichAlla

бсп (обоб. урок)HomichAlla

Troubleshooting mysql-tutorialjames tong

Towards True Elasticity of Spark-(Michael Le and Min Li, IBM)Spark Summit

Windowing in Apache ApexApache Apex

The 5 People in your Organization that grow Legacy CodeRoberto Cortez

Hadoop File System Shell Commands,Hadoop online training

Hadoop basic commandsbispsolutions

Introduction to Apache Apex and writing a big data streaming application Apache Apex

Build your shiny new pc, with PangolyPangoly

Hadoop Internals (2.3.0 or later)Emilio Coppa

Introduction to UNIX Command-Lines with examplesNoé Fernández-Pozo

Introduction to Map ReduceApache Apex

Similar to Building Your First Apache Apex (Next Gen Big Data/Hadoop) Application (20)

Overview of MVC Framework - by software outsourcing company indiaJignesh Aakoliya

Building Your First Apache Apex ApplicationApache Apex

PLNOG15: The Power of the Open Standards SDN API’s - Mikael Holmberg PROIDEA

The document discusses using software defined networking (SDN) and open application programming interfaces (APIs) to automate network operations. It provides examples of using IFTTT-style conditional statements to trigger network actions like prioritizing Skype calls or redirecting vulnerable devices. The SDN controller OpenDaylight is examined, along with its northbound and southbound APIs. Service abstraction layers like MD-SAL and AD-SAL are compared for interfacing with network elements and applications. Case studies demonstrate provisioning MPLS networks on-demand and using SDN for Skype call quality of service.

Building Automated Data Pipelines with Airflow.pdfabhaykm804

Oracle APEX 18.1 New Featuresmsewtz

This document summarizes Marc Sewtz's presentation on new features in Oracle APEX 18.1. Key points include: - Upgrades to Oracle JET, jQuery, and jQuery UI to newer versions to adopt new visualizations and stay supported. Built-in APEX components were updated but custom code may need changes. - A new low code application wizard to simplify creating applications with options for frameworks, themes, and page types. - Enhancements to the interactive grid, page designer, and new REST capabilities including a REST workshop.

App Load Presentation 2009sundu72

Stream Processing with Apache ApexPramod Immaneni

- 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.

T2 Web FrameworkShinpei Ohtani

Eclipse - Single Source;Three RuntimesSuresh Krishna Madhuvarsu

API workshop: Introduction to APIs (TC Camp)Tom Johnson

Oracle Application Express 20.2 New Featuresmsewtz

This document provides a summary of new features in Oracle APEX 20.2, including: - Enhancements to Faceted Search such as conditional facets, compact count displays, and visualization of facet values. - Improved report printing capabilities including built-in PDF printing support for additional languages and export data functionality. - New automation capabilities to trigger conditional actions based on database or remote data source changes. - A new Cards region for presenting varied information in small blocks, enhanced REST data source functionality, and updated developer tools.

Building Bridges: Merging RPA Processes, UiPath Apps, and Data Service to bu...DianaGray10

This session is focused on the art of application architecture, where we unravel the intricacies of creating a standard, yet dynamic application structure. We'll explore: Essential components of a typical application, emphasizing their roles and interactions. Learn how to connect UiPath RPA Processes, UiPath Apps, and Data Service together to build a stronger app. Gain insights into building more efficient, interconnected, and robust applications in the UiPath ecosystem. Speaker: David Kroll, Director, Product Marketing @Ashling Partners and UiPath MVP

ilide.info-talend-open-studio-for-data-integration-pr_f4a743b84c8b04cbebbf4c7...khadijahd2

Talend Open Studio is an open source data integration tool that provides a graphical environment for designing and executing data integration jobs. It generates Java or Perl code behind the scenes. The document discusses Talend Enterprise, which is the commercial version that includes additional features like an administration center, version control, and execution servers. It also describes key concepts in Talend Open Studio like projects, components, connections, and metadata, and shows screenshots of the main interface elements.

Oracle APEX Introduction (release 18.1)Michael Hichwa

Oracle APEX is a low-code application development tool that allows users to easily and quickly build powerful and responsive web apps leveraging their SQL skills. It provides an integrated development environment where users can develop entire apps directly from their web browser. Apps are built on top of the Oracle database for high performance and scalability. Oracle APEX itself is a free and fully supported feature of all editions of the Oracle database.

Streamline - Stream Analytics for EveryoneDataWorks Summit/Hadoop Summit

Stream processing has become the defacto standard for building real-time ETL and Stream Analytics applications. We see batch workloads move into Stream processing to to act on the data and derive insights faster. With the explosion of data with "Perishable Insights" such IoT and machine-generated data, Stream Processing + Predictive Analytics is driving tremendous business value. This is evidenced by the explosion of Stream Processing frameworks like proven and evolving Apache Storm and newer frameworks such as Apache Flink, Apache Apex, and Spark Streaming. Today, users have to choose and try to understand the benefits of each of these frameworks and not only that they have to learn the new APIs and also operationalize their applications. To create value faster, we are introducing new open source tool - Streamline. It is a self-service tool that will ease building streaming application and deploy the streaming application across multiple frameworks/engines that users prefer in a snap. It simplifies integration with Machine Learning models for scoring and classification of data for Predictive Analytics. It provides an elegant way to build Analytics dashboards to derive business insights out of the streaming data and to allow the business users to consume it easily. In this talk, we will outline the fundamentals of real-time stream processing and demonstrate Streamline capabilities to show how it simplifies building real-time streaming analytics applications.

Visualizing Big Data in RealtimeDataWorks Summit

This document provides an overview of Apache Apex and real-time data visualization. Apache Apex is a platform for developing scalable streaming applications that can process billions of events per second with millisecond latency. It uses YARN for resource management and includes connectors, compute operators, and integrations. The document discusses using Apache Apex to build real-time dashboards and widgets using the App Data Framework, which exposes application data sources via topics. It also covers exporting and packaging dashboards to include in Apache Apex application packages.

APEX – jak vytvořit jednoduše aplikaciMarketingArrowECS_CZ

The document provides an overview of Oracle APEX (Application Express), a low-code development framework for building database-centric web applications. Some key points covered include: - APEX allows developing desktop and mobile web apps using visual page design tools while leveraging SQL skills and database capabilities. - It has a browser-based IDE and stores app definitions as metadata in the database with no client software needed. - APEX applications can be developed locally or in the cloud and benefit from features like built-in security, authentication, validation and more.

Introduction to Apache Camel.pdfKnoldus Inc.

Document_format_for_OData_In_A_Nutshell.pdfdavidjpeace

OData provides a standardized protocol for querying and updating data via RESTful web services. It uses HTTP methods and the Atom Publishing Protocol to perform CRUD operations on data entities. OData services can be accessed through a metadata document that describes available entities and relationships. This allows OData to act similarly to ODBC, enabling applications to access online data sources.

Java files and io streamsRubaNagarajan

The document discusses input and output streams in Java. It notes that streams represent ordered sequences of bytes with a source or destination. There are two types of streams in Java - character streams and byte streams. The Java IO package provides hierarchies for handling character and byte streams, including classes like Reader, Writer, InputStream and OutputStream. It also discusses concepts like file handling, random access files, and the lifecycle methods of applets like init(), start(), paint(), stop() and destroy().

Overview of MVC Framework - by software outsourcing company indiaJignesh Aakoliya

Building Your First Apache Apex ApplicationApache Apex

PLNOG15: The Power of the Open Standards SDN API’s - Mikael Holmberg PROIDEA

Building Automated Data Pipelines with Airflow.pdfabhaykm804

Oracle APEX 18.1 New Featuresmsewtz

App Load Presentation 2009sundu72

Stream Processing with Apache ApexPramod Immaneni

T2 Web FrameworkShinpei Ohtani

Eclipse - Single Source;Three RuntimesSuresh Krishna Madhuvarsu

API workshop: Introduction to APIs (TC Camp)Tom Johnson

Oracle Application Express 20.2 New Featuresmsewtz

Building Bridges: Merging RPA Processes, UiPath Apps, and Data Service to bu...DianaGray10

ilide.info-talend-open-studio-for-data-integration-pr_f4a743b84c8b04cbebbf4c7...khadijahd2

Oracle APEX Introduction (release 18.1)Michael Hichwa

Streamline - Stream Analytics for EveryoneDataWorks Summit/Hadoop Summit

Visualizing Big Data in RealtimeDataWorks Summit

APEX – jak vytvořit jednoduše aplikaciMarketingArrowECS_CZ

Introduction to Apache Camel.pdfKnoldus Inc.

Document_format_for_OData_In_A_Nutshell.pdfdavidjpeace

Java files and io streamsRubaNagarajan

More from Apache Apex (8)

Low Latency Polyglot Model Scoring using Apache ApexApache Apex

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.

Introduction to YarnApache Apex

YARN was introduced as part of Hadoop 2.0 to address limitations in the original MapReduce (MR1) architecture like scalability bottlenecks and underutilization of resources. YARN introduces a global ResourceManager and per-node NodeManagers to allocate cluster resources to distributed applications. It allows various distributed processing frameworks beyond MapReduce to share common cluster resources. Applications request containers for ApplicationMasters that then negotiate resources from YARN to run application components in containers across nodes. Existing MapReduce jobs can also run unchanged on YARN.

Intro to Big Data HadoopApache Apex

Big Data Berlin v8.0 Stream Processing with Apache Apex Apache Apex

This document discusses Apache Apex, an open source stream processing framework. It provides an overview of stream data processing and common use cases. It then describes key Apache Apex capabilities like in-memory distributed processing, scalability, fault tolerance, and state management. The document also highlights several customer use cases from companies like PubMatic, GE, and Silver Spring Networks that use Apache Apex for real-time analytics on data from sources like IoT sensors, ad networks, and smart grids.

Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex

Apache Beam (incubating)Apache Apex

Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member Apache Beam (incubating) is a programming model and library for unified batch & streaming big data processing. This talk will cover the Beam programming model broadly, including its origin story and vision for the future. We will dig into how Beam separates concerns for authors of streaming data processing pipelines, isolating what you want to compute from where your data is distributed in time and when you want to produce output. Time permitting, we might dive deeper into what goes into building a Beam runner, for example atop Apache Apex.

Making sense of Apache Bigtop's role in ODPi and how it matters to Apache ApexApache Apex

Apache Apex & BigtopApache Apex

Chinmay Kolhatkar: Engineer, DataTorrent & Committer, Apache Apex For ease of use and deployment, Apache Apex leverages Apache Bigtop. Apex, being part of bigtop stack, can be easily deployed in both debian and rpm based cluster system and run validation tests for installation. This talk will cover a demo on how to install apex-bigtop and use it. It also covers a test sandbox docker environment, having pre-installed bigtop-hadoop and bigtop-apex, for quickly getting started with apex.

Low Latency Polyglot Model Scoring using Apache ApexApache Apex

Introduction to YarnApache Apex

Intro to Big Data HadoopApache Apex

Big Data Berlin v8.0 Stream Processing with Apache Apex Apache Apex

Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex

Apache Beam (incubating)Apache Apex

Making sense of Apache Bigtop's role in ODPi and how it matters to Apache ApexApache Apex

Apache Apex & BigtopApache Apex

Recently uploaded (20)

Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company

Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.

The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john

Build Your Own Copilot & Agents For DevsBrian McKeiver

DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock

Building 10x Organizations with Modern Productivity Metrics 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’ Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them. But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?

Rusty Waters: Elevating Lakehouses Beyond Sparkcarlyakerly1

Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark? At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍 Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀

Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity

This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots. 📕 Here's what you can expect: - Modeling: Build end-to-end processes using BPMN. - Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes. - Operating: Control process instances with rewind, replay, pause, and stop functions. - Monitoring: Use dashboards and embedded analytics for real-time insights into process instances. This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes. 👨‍🏫 Speaker: Andrei Vintila, Principal Product Manager @UiPath This session streamed live on April 29, 2025, 16:00 CET. Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.

tecnologias de las primeras civilizaciones.pdffjgm517

2025-05-Q4-2024-Investor-Presentation.pptxSamuele Fogagnolo

Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex

Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how: • Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules. • Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance. • Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity. • Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications. • Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market. With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications. Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family

Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo

TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc

Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency. This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data. Attendees will learn: - Consumer awareness around data brokers and what consumers are doing to limit data collection - How businesses assess third-party vendors and their consent management operations - Where business preparedness needs improvement - What these trends mean for the future of privacy governance and public trust This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.

Semantic Cultivators : The Critical Future Role to Enable AIartmondano

Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell

Cyber Awareness overview for 2025 month of securityriccardosl1

Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55

Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul

Artificial intelligence is changing how businesses operate. Companies are using AI agents to automate tasks, reduce time spent on repetitive work, and focus more on high-value activities. Noah Loul, an AI strategist and entrepreneur, has helped dozens of companies streamline their operations using smart automation. He believes AI agents aren't just tools—they're workers that take on repeatable tasks so your human team can focus on what matters. If you want to reduce time waste and increase output, AI agents are the next move.

Into The Box Conference Keynote Day 1 (ITB2025)Ortus Solutions, Corp

Drupalcamp Finland – Measuring Front-end Energy ConsumptionExove

Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan

This is a Quick Research Guide (QRG). QRGs include the following: - A brief, high-level overview of the QRG topic. - A milestone timeline for the QRG topic. - Links to various free online resource materials to provide a deeper dive into the QRG topic. - Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic. QRGs planned for the series: - Artificial Intelligence QRG - Quantum Computing QRG - Big Data Analytics QRG - Spacecraft Guidance, Navigation & Control QRG (coming 2026) - UK Home Computing & The Birth of ARM QRG (coming 2027) Any questions or comments? - Please contact Arthur Morgan at [email protected]. 100% human made.

Cybersecurity Identity and Access Solutions using Azure ADVICTOR MAESTRE RAMIREZ