The document discusses real-time fraud detection patterns and architectures. It provides an overview of key technologies like Kafka, Flume, and Spark Streaming used for real-time event processing. It then describes a high-level architecture involving ingesting events through Flume and Kafka into Spark Streaming for real-time processing, with results stored in HBase, HDFS, and Solr. The document also covers partitioning strategies, micro-batching, complex topologies, and ingestion of real-time and batch data.
The document discusses real-time fraud detection patterns and architectures. It provides an overview of key technologies like Kafka, Flume, and Spark Streaming used for real-time event processing. It then describes a high-level architecture involving ingesting events through Flume and Kafka into Spark Streaming for real-time processing, with results stored in HBase, HDFS, and Solr. The document also covers partitioning strategies, micro-batching, complex topologies, and ingestion of real-time and batch data.
Hadoop Application Architectures tutorial at Big DataService 2015hadooparchbook
This document outlines a presentation on architectural considerations for Hadoop applications. It introduces the presenters who are experts from Cloudera and contributors to Apache Hadoop projects. It then discusses a case study on clickstream analysis, how this was challenging before Hadoop due to data storage limitations, and how Hadoop provides a better solution by enabling active archiving of large volumes and varieties of data at scale. Finally, it covers some of the challenges in implementing Hadoop, such as choices around storage managers, data modeling and file formats, data movement workflows, metadata management, and data access and processing frameworks.
Application architectures with Hadoop – Big Data TechCon 2014hadooparchbook
Building applications using Apache Hadoop with a use-case of clickstream analysis. Presented by Mark Grover and Jonathan Seidman at Big Data TechCon, Boston in April 2014
This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.
This document discusses a presentation on fraud detection application architectures using Hadoop. It provides an overview of different fraud use cases and challenges in implementing Hadoop-based solutions. Requirements for the applications include handling high volumes, velocities and varieties of data, generating real-time alerts with low latency, and performing both stream and batch processing. A high-level architecture is proposed using Hadoop, HBase, HDFS, Kafka and Spark to meet the requirements. Storage layer choices and considerations are also discussed.
Building a fraud detection application using the tools in the Hadoop ecosystem. Presentation given by authors of O'Reilly's Hadoop Application Architectures book at Strata + Hadoop World in San Jose, CA 2016.
The document discusses best practices for streaming applications. It covers common streaming use cases like ingestion, transformations, and counting. It also discusses advanced streaming use cases that involve machine learning. The document provides an overview of streaming architectures and compares different streaming engines like Spark Streaming, Flink, Storm, and Kafka Streams. It discusses when to use different storage systems and message brokers like Kafka for ingestion pipelines. The goal is to understand common streaming use cases and their architectures.
Top 5 mistakes when writing Spark applicationshadooparchbook
This document discusses common mistakes made when writing Spark applications and provides recommendations to address them. It covers issues like having executors that are too small or large, shuffle blocks exceeding size limits, data skew slowing jobs, and excessive stages. The key recommendations are to optimize executor and partition sizes, increase partitions to reduce skew, use techniques like salting to address skew, and favor transformations like ReduceByKey over GroupByKey to minimize shuffles and memory usage.
This document discusses a case study on fraud detection using Hadoop. It begins with an overview of fraud detection requirements, including the need for real-time and near real-time processing of large volumes and varieties of data. It then covers considerations for the system architecture, including using HDFS and HBase for storage, Kafka for ingestion, and Spark and Storm for stream and batch processing. Data modeling with HBase and caching options are also discussed.
Hadoop application architectures - using Customer 360 as an examplehadooparchbook
Hadoop application architectures - using Customer 360 (more generally, Entity 360) as an example. By Ted Malaska, Jonathan Seidman and Mark Grover at Strata + Hadoop World 2016 in NYC.
Architecting next generation big data platformhadooparchbook
A tutorial on architecting next generation big data platform by the authors of O'Reilly's Hadoop Application Architectures book. This tutorial discusses how to build a customer 360 (or entity 360) big data application.
Audience: Technical.
Top 5 mistakes when writing Spark applicationshadooparchbook
This document discusses common mistakes people make when writing Spark applications and provides recommendations to address them. It covers issues related to executor configuration, application failures due to shuffle block sizes exceeding limits, slow jobs caused by data skew, and managing the DAG to avoid excessive shuffles and stages. Recommendations include using smaller executors, increasing the number of partitions, addressing skew through techniques like salting, and preferring ReduceByKey over GroupByKey and TreeReduce over Reduce to improve performance and resource usage.
Μια εργασία για το μάθημα: "Κείμενα Νεοελληνικής Λογοτεχνίας" Γ΄ Γυμνασίου από τις μαθήτριες: Τουλκαρίδου Νικολέτα, Τσόπρα Μαρία, Μίχου Κυριακή.
Υπεύθυνη καθηγήτρια: Αρετή Κάρκου
Η ΑΓΙΑ ΣΚΕΠΗ ΤΗΣ ΥΠΕΡΑΓΙΑΣ ΘΕΟΤΟΚΟΥ ΚΑΙ ΤΟ ΕΠΟΣ ΤΟΥ ΄40ΠΑΖΛ ΕΠΙΛΟΓΕΣ
ΣΧΟΛΙΑ : Η Αγία Σκέπη της Υπεραγίας Θεοτόκου εν Βλαχερνώ εορτάζει την 1η Οκτωβρίου. Σύμφωνα με το συναξάρι, «Τῇ Α' τοῦ αὐτοῦ μηνός, τὴν ἀνάμνησιν ἑορτάζομεν τῆς ἁγίας Σκέπης τῆς Ὑπεραγίας Δεσποίνης ἡμῶν Θεοτόκου, καὶ Ἀειπαρθένου Μαρίας, ἤτοι τοῦ ἱεροῦ αὐτῆς Μαφορίου τοῦ ἐν τῇ σορῷ τοῦ Ἱεροῦ Ναοῦ τῶν Βλαχερνῶν, ὅτε ὁ ὅσιος Ἀνδρέας, ὁ διὰ Χριστὸν σαλός, κατεῖδε ἐφηπλωμένην αὐτὴν ἄνωθεν, καὶ πάντας εὐσεβεῖς περισκέπουσαν».
Η Εκκλησία της Ελλάδος όμως, την έχει μεταθέσει στις 28 Οκτωβρίου, όπου η Ελλάδα γιορτάζει το μεγάλο γεγονός της διασώσεως και απελευθερώσεως της από τον Ιταλογερμανικό ζυγό. Την Ακολουθία που ψάλλεται αυτή την ημέρα την έγραψε ο Αγιορείτης Μοναχός Γεράσιμος Μικραγιαννανίτης και εγκρίθηκε από την Ιερά Σύνοδο της Εκκλησίας της Ελλάδος στις 21 Οκτωβρίου 1952 μ.Χ. όπου και αποφασίστηκε ο συνεορτασμός της εορτής της Αγίας Σκέπης και της Εθνικής επετείου του «ΟΧΙ» (Συνοδικές Εγκύκλιοι, Τόμος Β', Αθήνα 1956, σελ. 649).
26 Οκτωβρίου 2016
Εὐάγγελος ὁ Σάμιος
Ἁγίου Δημητρίου τοῦ Μυροβλήτη.
Ἒκδοση 1η
