Accessories Shop Management System on Advance JAVAMd. Mahbub Alam
The company maintains basically 4 registry copies which are accordingly:
• Sales registry book
• Servicing registry book
• Download registry book
• Laser registry book
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Azure Digital Twins is an Azure IoT service that allow you creating comprehensive models of the physical environment the covers your devices and the envronment that surrounds them.
Let's discover what a Twin is and which features this new service offers
In this deck from FOSDEM 2018 in Brussels, Todd Gamblin presents: Binary Packaging for HPC with Spack.
"Spack is a package manager for cluster users, developers, and administrators, rapidly gaining populartiy in the HPC community. Like other HPC package managers, Spack was designed to build packages from source. However, we’ve recently added binary packaging capabilities, which pose unique challenges for HPC environments. Most binary distributions assume a lowest-common-denominator architecture, e.g. x86_64, and do not take advantage of vector instructions or architecture-specific features. Spack supports relocatable binaries for specific OS releases, target architectures, MPI implementations, and other very fine-grained build options.
This talk will introduce binary packaging in Spack and some of the open infrastructure we have planned for distributing packages. We’ll talk about challenges to providing binaries for a combinatorially large package ecosystem, and what we’re doing in Spack to address these problems. We’ll also talk about challenges for implementing relocatable binaries with a multi-compiler system like Spack. Finally, We’ll talk about how Spack integrates with the US exsascale project’s open source software release plan, and how this will help glue together the HPC OSS ecosystem as a whole."
Watch the video: https://wp.me/p3RLHQ-i34
Learn more: https://computation.llnl.gov/projects/spack-hpc-package-manager
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Accessories Shop Management System on Advance JAVAMd. Mahbub Alam
The company maintains basically 4 registry copies which are accordingly:
• Sales registry book
• Servicing registry book
• Download registry book
• Laser registry book
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Azure Digital Twins is an Azure IoT service that allow you creating comprehensive models of the physical environment the covers your devices and the envronment that surrounds them.
Let's discover what a Twin is and which features this new service offers
In this deck from FOSDEM 2018 in Brussels, Todd Gamblin presents: Binary Packaging for HPC with Spack.
"Spack is a package manager for cluster users, developers, and administrators, rapidly gaining populartiy in the HPC community. Like other HPC package managers, Spack was designed to build packages from source. However, we’ve recently added binary packaging capabilities, which pose unique challenges for HPC environments. Most binary distributions assume a lowest-common-denominator architecture, e.g. x86_64, and do not take advantage of vector instructions or architecture-specific features. Spack supports relocatable binaries for specific OS releases, target architectures, MPI implementations, and other very fine-grained build options.
This talk will introduce binary packaging in Spack and some of the open infrastructure we have planned for distributing packages. We’ll talk about challenges to providing binaries for a combinatorially large package ecosystem, and what we’re doing in Spack to address these problems. We’ll also talk about challenges for implementing relocatable binaries with a multi-compiler system like Spack. Finally, We’ll talk about how Spack integrates with the US exsascale project’s open source software release plan, and how this will help glue together the HPC OSS ecosystem as a whole."
Watch the video: https://wp.me/p3RLHQ-i34
Learn more: https://computation.llnl.gov/projects/spack-hpc-package-manager
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
PostgreSQL, performance for queries with groupingAlexey Bashtanov
The talk will cover PostgreSQL grouping and aggregation facilities and best practices of using them in fast and efficient manner.
In 40 minutes the audience will learn several techniques to optimise queries containing GROUP BY, DISTINCT or DISTINCT ON keywords.
DevOps for Applications in Azure Databricks: Creating Continuous Integration ...Databricks
Working with our customers, developers and partners around the world, it's clear DevOps has become increasingly critical to a team's success. Continuous integration (CI) and continuous delivery (CD) which is part of DevOps, embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. In this session, we will cover how you can automate your entire process from code commit to production using CI/CD pipelines in Azure DevOps for Azure Databricks applications. Using CI/CD practices, you can simplify, speed and improve your cloud development to deliver features to your customers as soon as they're ready.
Stream Data Deduplication Powered by Kafka Streams | Philipp Schirmer, BakdataHostedbyConfluent
Representations of data, e.g., describing news, persons or places, differ. Therefore, we need to identify duplicates, for example, if we want to stream deduplicated news from different sources into a sentiment classifier.
We built a system that collects data from different sources in a streaming fashion, aligns them to a global schema and then detects duplicates within the data stream without time window constraints. The challenge is not only to process newly published data without significant delay, but also to reprocess hundreds of millions existing messages, for example, after improving the similarity measure.
In this talk, we present our implementation for deduplication of data streams built on top of Kafka Streams. For this, we leverage Kafka APIs, namely state stores, and also use Kubernetes to auto-scale our application from 0 to a defined maximum. This allows us to process live data immediately and also reprocess all data from scratch within a reasonable amount of time.
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
Salesforce enabling real time scenarios at scale using kafkaThomas Alex
Nishant Gupta from Salesforce talked about Ajna, a service for monitoring system health across global data centers in real time, and how Kafka is at the center of this system. The talk covers the scenario, key challenges, learnings and best practices.
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into the architecture of our current data quality solution. We’ll cover what worked, what didn’t work so well, and what we're working on next. We’ll conclude with some tips & lessons learned for ensuring high quality on big data.
This talk was presented at DataWorks/Hadoop Summit 2017 on June 13, 2017.
Location Analytics - Real-Time Geofencing using Apache KafkaGuido Schmutz
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries).
Geofencing lays the foundation for realizing use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others.
GPS tracking tells constantly and in real time where a device is located and forms the stream of events which needs to be analyzed against the much more static set of geo-fences. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play.
This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs). The design of such solution so that it can scale with both an increasing amount of position events as well as geo-fences will be discussed as well.
When implementing IPv6 it can be important to maintain a view of how it is being used. This presentation provides a quick look at using Zabbix with SNMP to monitor IP protocol usage.
在接連勇奪三個產品創新獎項之後,亞洲 Big Data 解決方案領導品牌 Etu 今天舉辦「Etu Solution Day 2012」,與合作夥伴聯手展出了一系列以其核心產品 Etu Appliance 發展出來的 Big Data End-to End 解決方案,並在會中提出 2013 年台灣 Big Data 市場的趨勢預測。Etu 認為,隨著不同行業的 Big Data 首批應用一一成形落地,企業擁抱 Big Data 的力道將在新的一年有明顯加重的趨勢。
首屆的「Etu Solution Day」特別針對電子商務、零售、電信、金融、高科技製造、政府及交通運輸行業,一次匯集具有 Hadoop 經驗的 Big Data 應用軟體開發商,以及可接取 Hadoop 平台的工具廠商,與焦點行業來賓分享經驗成果。Etu 在會中發表對 2013 年台灣 Big Data 市場的五大前瞻性預測,包括:一、本地不同行業的 Big Data 應用案例將一一浮現;二、”Medium” Data 出現在更多企業 Big Data 應用場景;三、Hadoop 相關專業教育訓練課程漸熱;四、從 Quantified Self、Enterprise Data、Open Data、到 Internet-scale Data,資料分析蔚為顯學; 五、Open Data 方興未艾,各級政府、不同部門的開放策略與腳步不一,來自民間的挑戰也不斷。
ESD 2012 Keynote: What Is the next Big Data?Fred Chiang
This is my keynote slides for Etu Solution Day 2012 which was held on Dec, 20, 2012 @Taipei, Taiwan. I had summarized the market status of Big Data in Taiwan and predicted the trend in 2013.
PostgreSQL, performance for queries with groupingAlexey Bashtanov
The talk will cover PostgreSQL grouping and aggregation facilities and best practices of using them in fast and efficient manner.
In 40 minutes the audience will learn several techniques to optimise queries containing GROUP BY, DISTINCT or DISTINCT ON keywords.
DevOps for Applications in Azure Databricks: Creating Continuous Integration ...Databricks
Working with our customers, developers and partners around the world, it's clear DevOps has become increasingly critical to a team's success. Continuous integration (CI) and continuous delivery (CD) which is part of DevOps, embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. In this session, we will cover how you can automate your entire process from code commit to production using CI/CD pipelines in Azure DevOps for Azure Databricks applications. Using CI/CD practices, you can simplify, speed and improve your cloud development to deliver features to your customers as soon as they're ready.
Stream Data Deduplication Powered by Kafka Streams | Philipp Schirmer, BakdataHostedbyConfluent
Representations of data, e.g., describing news, persons or places, differ. Therefore, we need to identify duplicates, for example, if we want to stream deduplicated news from different sources into a sentiment classifier.
We built a system that collects data from different sources in a streaming fashion, aligns them to a global schema and then detects duplicates within the data stream without time window constraints. The challenge is not only to process newly published data without significant delay, but also to reprocess hundreds of millions existing messages, for example, after improving the similarity measure.
In this talk, we present our implementation for deduplication of data streams built on top of Kafka Streams. For this, we leverage Kafka APIs, namely state stores, and also use Kubernetes to auto-scale our application from 0 to a defined maximum. This allows us to process live data immediately and also reprocess all data from scratch within a reasonable amount of time.
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
Salesforce enabling real time scenarios at scale using kafkaThomas Alex
Nishant Gupta from Salesforce talked about Ajna, a service for monitoring system health across global data centers in real time, and how Kafka is at the center of this system. The talk covers the scenario, key challenges, learnings and best practices.
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into the architecture of our current data quality solution. We’ll cover what worked, what didn’t work so well, and what we're working on next. We’ll conclude with some tips & lessons learned for ensuring high quality on big data.
This talk was presented at DataWorks/Hadoop Summit 2017 on June 13, 2017.
Location Analytics - Real-Time Geofencing using Apache KafkaGuido Schmutz
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries).
Geofencing lays the foundation for realizing use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others.
GPS tracking tells constantly and in real time where a device is located and forms the stream of events which needs to be analyzed against the much more static set of geo-fences. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play.
This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs). The design of such solution so that it can scale with both an increasing amount of position events as well as geo-fences will be discussed as well.
When implementing IPv6 it can be important to maintain a view of how it is being used. This presentation provides a quick look at using Zabbix with SNMP to monitor IP protocol usage.
在接連勇奪三個產品創新獎項之後,亞洲 Big Data 解決方案領導品牌 Etu 今天舉辦「Etu Solution Day 2012」,與合作夥伴聯手展出了一系列以其核心產品 Etu Appliance 發展出來的 Big Data End-to End 解決方案,並在會中提出 2013 年台灣 Big Data 市場的趨勢預測。Etu 認為,隨著不同行業的 Big Data 首批應用一一成形落地,企業擁抱 Big Data 的力道將在新的一年有明顯加重的趨勢。
首屆的「Etu Solution Day」特別針對電子商務、零售、電信、金融、高科技製造、政府及交通運輸行業,一次匯集具有 Hadoop 經驗的 Big Data 應用軟體開發商,以及可接取 Hadoop 平台的工具廠商,與焦點行業來賓分享經驗成果。Etu 在會中發表對 2013 年台灣 Big Data 市場的五大前瞻性預測,包括:一、本地不同行業的 Big Data 應用案例將一一浮現;二、”Medium” Data 出現在更多企業 Big Data 應用場景;三、Hadoop 相關專業教育訓練課程漸熱;四、從 Quantified Self、Enterprise Data、Open Data、到 Internet-scale Data,資料分析蔚為顯學; 五、Open Data 方興未艾,各級政府、不同部門的開放策略與腳步不一,來自民間的挑戰也不斷。
ESD 2012 Keynote: What Is the next Big Data?Fred Chiang
This is my keynote slides for Etu Solution Day 2012 which was held on Dec, 20, 2012 @Taipei, Taiwan. I had summarized the market status of Big Data in Taiwan and predicted the trend in 2013.
Big Data 102 - Crossovers 成長之旅導覽 (Keynote for Big Data Taiwan 2013)Fred Chiang
總結阻礙企業導入 Big Data 解決方案的因素,除了大環境的景氣因素,其餘幾乎可歸結為對「價值」與「技術」的不確定與不熟悉。此場將帶領大家預覽 Big Data Taiwan 2013 整天的內容精華,具體說明 Big Data 的「價值」洞見與展現,「技術」養成與發展,配合戰略探討與驅動,以降低企業的不確定感,協助數據價值策略的發展。
2012.05.24 於 「Big Data Taiwan 2012」的 Keynote 講稿。
主講者:Etu 副總經理/ 蔣居裕
《議題簡介》
無論是企業區域網路,還是開放的網際網路,在巨大的結構化與非結構化資料的背後,其實充滿著各種行為意圖,以及人、事、物、時、地的多維度關聯。商業的日益競爭,已經來到了一個除了講求行銷創意,還要擁有巨量資料處理與分析技術,才能出奇制勝的時代。有人形容 Big Data 的價值挖掘,就像是在攪拌混凝土,若在尚未完成前就中斷,將導致前功盡棄,全無可用的窘境。對 Big Data 的意圖與關聯探索,必須是 End-to-End 全程的照料,方得實現。本議程將舉例說明這個有序到永續的過程,讓聽者更能領略意圖與關聯充滿的世界。
Hadoop con 2015 hadoop enables enterprise data lakeJames Chen
Mobile Internet, Social Media 以及 Smart Device 的發展促成資訊的大爆炸,伴隨產生大量的非結構化及半結構化的資料,不但資料的格式多樣,產生的速度極快,對企業的資訊架構帶來了前所未有的挑戰,面對多樣的資料結構及多樣的分析工具,我們應該採用什麼樣的架構互相整合,才能有效的管理資料生命週期,提取資料價值,Hadoop 生態系統,無疑的在這個大架構裡,將扮演最基礎的資料平台的角色,實現企業的 Data Lake。
Greenplum is leading MPP database technology for OLAP and ad-hoc workload. With more than 10 years R&D, Greenplum now become a bigdata platform, using it, you could do OLAP, Mixed workload, advanced analytics, machine learning, Text analysis, GIS/Geospatial analysis, Grapth analysis over various dataset no matter it is managed by Greenplum, Hadoop, S3, Gemfire, Database etc.
4. 什麼是非結構化資訊 ?
Unstructured Data refers to information that either does
not have a pre-defined data model and/or does not fit
well into relational tables. Unstructured information is
typically text-heavy, but may contain data such as dates,
numbers, and facts as well. This results in irregularities
and ambiguities that make it difficult to understand using
traditional computer programs as compared to data
stored in fielded form in databases or annotated
(semantically tagged) in documents
-- from Wikipedia http://en.wikipedia.org/wiki/Unstructured_data
4
12. Hadoop 不只是 Hadoop
Big Data Applications
Pig!
SQL HIVE
Zoo
RAW Keeper
12
13. Hadoop 生態系統
ZooKeeper – distributed coordination service
HBase – distributed column-oriented database for random
read/write
HIVE – SQL like database on top of Hadoop
Pig – high level scripting language for data processing
Mahout – a scalable machine learning library for MapReduce
Sqoop – SQL-to-Hadoop connector
Flume – a distributed streaming data collection framework
13
24. 企業的 Hadoop 應用策略
PowerView Excel with Predictive Embedded
PowerPivot Analytics BI
Familiar End User Tools
S
S
SSAS R
S
BI Platform
Connectors
Hadoop
Web
Sensors Devices Crawlers
Log ERP CRM LOB APPs
非結構化資料來源 結構化資料來源
31. Etu Appliance 簡介
Big Data End-to-End Solution in a Box
儲存與運算一體,簡化與最佳化的優勢機種:
•10 分鐘內可部署 100+ 節點
•資料擷取能力 1U 勝過 8U
•Big Data 運算處理最適化
• 延展:公有雲等級的運算架構
• 可靠:電信等級的系統品質
• 效能:企業等級的創新績效
32. 三種資料溫度的整合: Hot / Warm / Cold
Hot Data
在線結構化資料
在線半 / 非結構化資
料 OLTP OLAP
Warm Data
在線半 / 非結構化資
料 Hadoop-based Solution
Cold Data
離線資料
SAN / NAS / Scale-out NAS