Real time Analytics Using Storm and Kafka
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This document describes an application architecture that uses Apache Storm to process log files from Apache Kafka. The Storm topology includes a KafkaSpout that reads messages from a single Kafka broker, a LogExtractor bolt that applies rules to extract data from the logs, and DBPersist and HdfsBolt bolts that write the extracted data to an Oracle database and HDFS respectively, running on a single node.
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Replication and rebuild in cStorOpenEBS
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cStor is a resilient storage engine built with proven building blocks of storage components. The storage block layer is derived from the user space ZFS inherited from the proven OpenSolaris stack. The volumes can be accessed via iSCSI Target which is derived from Linux to BSD port. Both of these core components of cStor have been field tested at thousands of installations for many years.Monogo db in-action
Monogo db in-actionChi Lee
?
This document provides an overview and agenda for a MongoDB workshop. It begins with introductions and background on MongoDB. The agenda then covers basic CRUD operations and aggregation. It also discusses replication, sharding, and development uses for MongoDB. Replication in MongoDB uses replica sets for high availability and fault tolerance. Sharding partitions data across multiple servers to support large data volumes and high throughput. The document provides examples of shard key strategies and the production sharding architecture in MongoDB.YDAL Barcelona
YDAL BarcelonaGluster.org
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YADL is a software defined deduplication library that provides a stream-based API for integrating dedupe capabilities into storage systems like file systems, block storage, and object storage. It offers configurable chunking options and can store chunks on disk or in memory. Future plans for YADL include distributed dedupe across multiple sites, locality-sensitive hashing, and wrappers to provide direct data access without the API.Embedded Recipes 2018 - Shared memory / telemetry - Yves-Marie Morgan
Embedded Recipes 2018 - Shared memory / telemetry - Yves-Marie MorganAnne Nicolas
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To exchange data between processes without the overhead of locking/syscall, shared memory can be used. libshdata/telemetry offers a lock free circular buffer with timestamped data chunks to handle some typical uses cases found in drones:
One process for flying stack, and one a more processes for video computing.
Video processing can access drone attitude precisely with the frame
Logging of all the telemetry exchanged in shared memory is easily written in a file or to a remote ground station
Also work in AMP configurationsGluster d2
Gluster d2Gluster.org
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The document discusses GlusterD 2.0, a redesign of the Gluster distributed file system management daemon. Some key points:
- GlusterD 1.0 had scalability and consistency issues that limited it to hundreds of nodes. GlusterD 2.0 was rewritten from scratch in Go for better performance.
- GlusterD 2.0 uses etcd for centralized management and configuration storage. It has REST APIs and plugins for modularity.
- Components include REST interfaces, etcd backend, RPC framework, transaction system, and a flexible volume generator.
- Upgrades from Gluster 3.x to 4.x will be disruptive but provide a migration path. GlusterRuby,no sql and tokyocabinet
Ruby,no sql and tokyocabinetbiaowei zhuang
?
This document discusses the transition from relational databases to NoSQL databases. It provides an overview of key-value databases like Tokyo Cabinet and how they provide high performance and scalability alternatives to SQL databases. It also describes how Tokyo Cabinet can be used with an ActiveRecord adapter to integrate NoSQL style databases into Rails applications, bridging the gap between SQL and NoSQL. Finally, it outlines a vision for further developing Tokyo Cabinet into a cloud database that can provide a unified interface to both SQL and NoSQL databases.openATTIC & Ceph Management @ Suse Monthly Open Source Talks - 2016-06-07
openATTIC & Ceph Management @ Suse Monthly Open Source Talks - 2016-06-07it-novum
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This are the slides from Lenz Grimmer, Team Lead for Product Management and Development talking about openATTIC and Ceph Management at Suse Monthly Open Source Talks in July 2016. Petabyte Scale Object Storage Service Using Ceph in A Private Cloud - Varada ...
Petabyte Scale Object Storage Service Using Ceph in A Private Cloud - Varada ...Ceph Community
?
This document discusses Flipkart's use of Ceph object storage to provide a petabyte-scale object storage service. Key points:
- Flipkart runs two large data centers hosting over 20,000 servers and 60,000 VMs to power its e-commerce marketplace.
- It developed a highly scalable object storage service using Ceph to store over 1.5 billion objects totaling around 2PB of data. This service provides APIs compatible with AWS S3.
- The Ceph clusters are deployed across SSDs and HDDs to provide different performance and cost tiers for shared active, backup, and archival workloads with different SLAs around latency, throughput, and durabilityStor4NFV: Exploration of Cloud native Storage in OPNFV - Ren Qiaowei, Wang Hui
Stor4NFV: Exploration of Cloud native Storage in OPNFV - Ren Qiaowei, Wang HuiCeph Community
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Cephalocon APAC 2018
March 22-23, 2018 - Beijing, China
Ren Qiaowei, Intel Software Engineer
Wang Hui, Huawe Software EngineeropenATTIC Technology Overview - Ceph Management
openATTIC Technology Overview - Ceph Managementit-novum
?
An overview about the openATTIC open source storage management system and the state of the Ceph support. Presented by Lenz Grimmer at the Vault Conference 2016 in Raleigh, NC.Developing apps and_integrating_with_gluster_fs_-_libgfapi
Developing apps and_integrating_with_gluster_fs_-_libgfapiGluster.org
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This document discusses developing applications and integrating with GlusterFS. It describes using the libgfapi library to access GlusterFS volumes, providing improved speed over FUSE. The document outlines the Python and C APIs for libgfapi and provides an example of using the Python API. It also discusses rapidly prototyping GlusterFS translators using the Python Glupy library and provides a simple "hello world" translator example.20160401 guster-roadmap
20160401 guster-roadmapGluster.org
?
The document outlines Gluster's roadmap, including recent improvements to versions 3.5-3.7 like bitrot detection and sharding, and plans for upcoming releases 3.8 and 4.0 such as tiering support, REST APIs, new style replication, and improving the distributed hashing translator. It also provides an overview of Gluster's architecture and quick start instructions.Integrating gluster fs,_qemu_and_ovirt-vijay_bellur-linuxcon_eu_2013
Integrating gluster fs,_qemu_and_ovirt-vijay_bellur-linuxcon_eu_2013Gluster.org
?
This document discusses integrating the open source GlusterFS distributed file system with QEMU virtualization and the oVirt virtualization management platform. It provides an overview of GlusterFS and how it can be used as a virtual machine image store. It describes integrating GlusterFS with QEMU using different methods and shows performance results. It then outlines the architecture and features for managing GlusterFS volumes from within oVirt, including a REST API and upcoming integration plans. The goal is to provide an open alternative for large scale centralized management of server and desktop virtualization.Gluster fs hadoop_fifth-elephant
Gluster fs hadoop_fifth-elephantGluster.org
?
This document discusses using GlusterFS for Hadoop. GlusterFS is an open source distributed file system that aggregates storage and provides a unified global namespace. It can be used with Hadoop as the underlying storage system instead of HDFS. Using GlusterFS offers advantages like no need for a metadata server and ability to use the same storage for both MapReduce jobs and application data. It also supports features like geo-replication and erasure coding that are useful for big data workloads.Tokyo Cabinet
Tokyo Cabinetehuard
?
Tokyo Cabinet is a high-performance embedded database that provides key-value, B-tree, and table data storage. It has APIs for Perl, Java, Lua, and Ruby. Tokyo Cabinet Hash and Btree databases allow fast storage and retrieval of data. Tokyo Tyrant adds network functionality, allowing remote access via its own protocol or HTTP. It also enables features like replication and Lua scripting. Tokyo Dystopia provides full-text search capabilities. Ruby libraries exist to interface with Tokyo Cabinet and Tokyo Tyrant from Ruby applications.Gluster intro-tdose
Gluster intro-tdoseGluster.org
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This document provides an introduction and overview of Gluster, an open source scale-out network-attached storage file system. It discusses what Gluster is, its architecture using distributed and replicated volumes, and provides a quick start guide for setting up a basic Gluster volume. The document also outlines several use cases for Gluster and recently added features. It concludes by describing how readers can get involved in the Gluster community.Kkeithley ufonfs-gluster summit
Kkeithley ufonfs-gluster summitGluster.org
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The document discusses the status and history of NFS and UFO protocols. It notes that many bugs have been fixed in NFS and details UFO's origins as being based on Swift 1.4.8, its packaging in Fedora 18, and plans for future performance enhancements and integration with Keystone authentication. The document contains several sections written by Kaleb KEITHLEY covering NFS status, UFO history, and its future.Gluster for sysadmins
Gluster for sysadminsGluster.org
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GlusterFS is a POSIX-compliant distributed file system that aggregates various storage bricks across commodity servers into a single global namespace. It has no single point of failure or performance bottleneck. Red Hat Storage is an enterprise implementation of GlusterFS. It uses a elastic hashing algorithm to distribute files across bricks without a centralized metadata server. Various translators and volumes types provide features like replication, distribution, striping, and geo-replication. Administration involves adding peers, creating and managing distributed volumes, and manipulating bricks.Recently uploaded (20)
Hot Lead Prediction Analytics Use Case - Smarten
Hot Lead Prediction Analytics Use Case - SmartenSmarten Augmented Analytics
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To conserve resources and optimize investment, a business must determine which potential opportunities are most likely to result in conversions and evolve into successful deals and determine which opportunities are at risk. This Hot Lead predictive analytics use case describes the value of predictive analytics to prioritize high-value leads and capitalize on an opportunity to convert a lead into a relationship by identifying key patterns that contribute to successful deal closures. Use these tools to identify the leads that are most likely to result in conversion and provide the most benefit to the enterprise. This technique can be used in many industries, including Financial Services, B2C and B2B. For more info https://www.smarten.com/augmented-analytics-learn-explore/use-cases.html加拿大成绩单购买原版(鲍颁毕业证书)卡尔加里大学毕业证文凭
加拿大成绩单购买原版(鲍颁毕业证书)卡尔加里大学毕业证文凭taqyed
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CloudMonitor - Architecture Audit Review February 2025.pdf
CloudMonitor - Architecture Audit Review February 2025.pdfRodney Joyce
?
CloudMonitor FinOps is now a Microsoft Certified solution in the Azure Marketplace. This little badge means that we passed a 3rd-party Technical Audit as well as met various sales KPIs and milestones over the last 12 months.
We used our existing Architecture docs for CISOs and Cloud Architects to craft an Audit Response - I've shared it below to help others obtain their cert.
Interestingly, 90% of our customers are in the USA, with very few in Australia. This is odd as the first thing I hear in every meetup and conference, from partners, customers and Microsoft, is that they want to optimise their cloud spend! But very few Australian companies are using the FinOps Framework to lower Azure costs.
![[aon_presentation EN] global_job_leveling .pdf](https://cdn.slidesharecdn.com/ss_thumbnails/englobaljobleveling-250309105035-96521a43-thumbnail.jpg?width=560&fit=bounds)
Introduction Lecture 01 Data Science.pdf
Introduction Lecture 01 Data Science.pdfmessagetome133
?
Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures Data Science Lectures 
MLecture 1 Introduction to AI . The basics.pptx
MLecture 1 Introduction to AI . The basics.pptxFaizaKhan720183
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Machine learning and artificial intelligence introduction. Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.The Role of Christopher Campos Orlando in Sustainability Analytics
The Role of Christopher Campos Orlando in Sustainability Analyticschristophercamposus1
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Christopher Campos Orlando specializes in leveraging data to promote sustainability and environmental responsibility. With expertise in carbon footprint analysis, regulatory compliance, and green business strategies, he helps organizations integrate sustainability into their operations. His data-driven approach ensures companies meet ESG standards while achieving long-term sustainability goals.
The truth behind the numbers: spotting statistical misuse.pptx
The truth behind the numbers: spotting statistical misuse.pptxandyprosser3
?
As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us.
For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.Optimizing Common Table Expressions in Apache Hive with Calcite
Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis
?
In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.Design Data Model Objects for Analytics, Activation, and AI
Design Data Model Objects for Analytics, Activation, and AIaaronmwinters
?
Explore using industry-specific data standards to design data model objects in Data Cloud that can consolidate fragmented and multi-format data sources into a single view of the customer.
Design of the data model objects is a critical first step in setting up Data Cloud and will impact aspects of the implementation, including the data harmonization and mappings, as well as downstream automations and AI processing. This session will provide concrete examples of data standards in the education space and how to design a Data Cloud data model that will hold up over the long-term as new source systems and activation targets are added to the landscape. This will help architects and business analysts accelerate adoption of Data Cloud.加拿大成绩单购买原版(顿补濒毕业证书)戴尔豪斯大学毕业证文凭
加拿大成绩单购买原版(顿补濒毕业证书)戴尔豪斯大学毕业证文凭taqyed
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the data analytics process:Real life applications
the data analytics process:Real life applicationsjhanvisaxena30
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the data analytics process: Real life applications
Real time Analytics Using Storm and Kafka
- 1. Application Architecture AppServer Log File TopicMessage * KafkaSpout Log RulesLogExtractor Bolt DBPersist Bolt HdfsBolt Oracle DB HDFS Single Node – Single Kafka Broker Storm Topology * * *