Mesos-based Data Infrastructure @ DoubanZhong Bo Tian
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How to build an elastic and efficient platform to support various Big Data and Machine Learning tasks is a challenge for a lot of corporations. In this presentation, Zhongbo Tian will give an overview of the Mesos-based core infrastructure of Douban, and demonstrate how to integrate the platform with state-of-art Big Data/ML technologies.
Can data virtualization uphold performance with complex queries? (Chinese)Denodo
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Watch full webinar here: https://bit.ly/3fQFUEY
There are myths about data virtualization that are based on misconceptions and even falsehoods. These myths can confuse and worry people who - quite rightly - look at data virtualization as a critical technology for a modern, agile data architecture.
We've decided that we need to set the record straight, so we put together this webinar series. It's time to bust a few myths!
In the first webinar of the series, we’ll be busting the 'performance' myth. “What about performance?” is usually the first question that we get when talking to people about data virtualization. After all, the data virtualization layer sits between you and your data, so how does this affect the performance of your queries? Sometimes the myth is perpetuated by people with alternative solutions…the ‘Put all your data in our Cloud and everything will be fine. Data virtualization? Nah, you don’t need that! It can't handle big queries anyway,’ type of thing.
Register this webinar as we explore the basis of the 'performance' myth and examine whether there is any underlying truth to it.
Big Data Taiwan 2014 Track1-3: Big Data, Big Challenge — Splunk 幫你解決 Big Data...Etu Solution
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講者:SYSTEX 數據加值應用發展部產品經理 | 陶靖霖
議題簡介:認清現實吧! Big Data 是個熱門詞彙、熱門議題,但是問題的核心仍然圍繞在資料處理的流程、架構與技術,要踏入 Big Data 的領域,使用者會遭遇哪些挑戰? Splunk 被譽為「全球最佳的 Big Data Company」,究竟在資料處理的流程中擁有什麼獨特的技術優勢,能夠幫助使用者克服這些挑戰?又有哪些成功幫助使用者從資料中萃取出價值的應用案例?歡迎來認識 Splunk 以及全球 Big Data 成功案例。
Mesos-based Data Infrastructure @ DoubanZhong Bo Tian
?
How to build an elastic and efficient platform to support various Big Data and Machine Learning tasks is a challenge for a lot of corporations. In this presentation, Zhongbo Tian will give an overview of the Mesos-based core infrastructure of Douban, and demonstrate how to integrate the platform with state-of-art Big Data/ML technologies.
Can data virtualization uphold performance with complex queries? (Chinese)Denodo
?
Watch full webinar here: https://bit.ly/3fQFUEY
There are myths about data virtualization that are based on misconceptions and even falsehoods. These myths can confuse and worry people who - quite rightly - look at data virtualization as a critical technology for a modern, agile data architecture.
We've decided that we need to set the record straight, so we put together this webinar series. It's time to bust a few myths!
In the first webinar of the series, we’ll be busting the 'performance' myth. “What about performance?” is usually the first question that we get when talking to people about data virtualization. After all, the data virtualization layer sits between you and your data, so how does this affect the performance of your queries? Sometimes the myth is perpetuated by people with alternative solutions…the ‘Put all your data in our Cloud and everything will be fine. Data virtualization? Nah, you don’t need that! It can't handle big queries anyway,’ type of thing.
Register this webinar as we explore the basis of the 'performance' myth and examine whether there is any underlying truth to it.
Big Data Taiwan 2014 Track1-3: Big Data, Big Challenge — Splunk 幫你解決 Big Data...Etu Solution
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講者:SYSTEX 數據加值應用發展部產品經理 | 陶靖霖
議題簡介:認清現實吧! Big Data 是個熱門詞彙、熱門議題,但是問題的核心仍然圍繞在資料處理的流程、架構與技術,要踏入 Big Data 的領域,使用者會遭遇哪些挑戰? Splunk 被譽為「全球最佳的 Big Data Company」,究竟在資料處理的流程中擁有什麼獨特的技術優勢,能夠幫助使用者克服這些挑戰?又有哪些成功幫助使用者從資料中萃取出價值的應用案例?歡迎來認識 Splunk 以及全球 Big Data 成功案例。
The document provides an overview of new features in HDFS in Hadoop 2, including:
- A new appendable write pipeline that allows files to be reopened for append and provides primitives like hflush and hsync.
- Support for multiple namenode federation to improve scalability and isolate namespaces.
- Namenode high availability using techniques like ZooKeeper and a quorum journal manager to avoid single points of failure.
- A new file system snapshots feature that allows point-in-time recovery through copy-on-write snapshots without data copying.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
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This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and performance evaluation of different hardware platforms for big data workloads.
The document discusses big data visualization and visual analysis, focusing on the challenges and opportunities. It begins with an overview of visualization and then discusses several challenges in big data visualization, including integrating heterogeneous data from different sources and scales, dealing with data and task complexity, limited interaction capabilities for large data, scalability for both data and users, and the need for domain and development libraries/tools. It then provides examples of visualizing taxi GPS data and traffic patterns in Beijing to identify traffic jams.
Cloud computing, big data, and mobile are three major trends that will change the world. Cloud computing provides scalable and elastic IT resources as services over the internet. Big data involves large amounts of both structured and unstructured data that can generate business insights when analyzed. The hadoop ecosystem, including components like HDFS, mapreduce, pig, and hive, provides an architecture for distributed storage and processing of big data across commodity hardware.
This document provides an overview of Capital One's plans to introduce Hadoop and discusses several proof of concepts (POCs) that could be developed. It summarizes the history and practices of using Hadoop at other companies like LinkedIn, Netflix, and Yahoo. It then outlines possible POCs for Hadoop distributions, ETL/analytics frameworks, performance testing, and developing a scaling layer. The goal is to contribute open source code and help with Capital One's transition to using Hadoop in production.
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It consists of Hadoop Distributed File System (HDFS) for storage, and MapReduce for distributed processing. HDFS stores large files across multiple machines, with automatic replication of data for fault tolerance. It has a master/slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks.
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。
Modernising Data Architecture for Data Driven Insights (Chinese)Denodo
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Watch full webinar here: https://bit.ly/3phVEEv
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics, it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But, that scenario is rapidly changing with the increasing adoption of real-time data virtualization - A paradigm shift in the approach that organisations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes center stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission critical.
Register this webinar to learn:
- How you can meet the challenges of delivering data insights with data virtualization
- Why Data Virtualization is increasingly find enterprise-wide adoption
- How customers are reducing costs and delivering faster insight
How Enterprises Leverage Data to Overcome Business Challenges During CoronavirusDenodo
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Watch full webinar here: https://bit.ly/2Jgb1uc
Coronavirus is spreading all over the world and has big impact on all the industries. How to acquire latest virus information from different countries and regions in real time to help organizations strategically plan and take actions accordingly and timely becomes very important.
Attend this webinar to learn:
- How business department acquires trustworthy data, gain deeper insights and fasten decision making
- How IT easily supports dynamic business requirements in real time
Trinity 大幅提昇企業面對大量快速變化資訊潮流時的競爭力。
現今企業 BI 多建於 RDBMS 上,伴隨大量的 ETL 與資料交換作業。在導入 Hadoop Big Data 應用之後, 如何有效地與既有 BI 系統介接,且進一步整合,以發揮整體綜效,將是一項挑戰。
Trinity 藉由優越的架構,在傳統 Structured Data 與 Hadoop Big Data 的應用間,建立無縫的交換作業,讓資訊分析人員直接運用熟悉的方式,以大幅降低導入 Big Data 應用時的學習曲線與後續對系統維運所投入的人力。
Big Data 102 - Crossovers 成長之旅導覽 (Keynote for Big Data Taiwan 2013)Fred Chiang
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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。
Spark is an open source cluster computing framework originally developed at UC Berkeley. Intel has made many contributions to Spark's development through code commits, patches, and collaborating with the Spark community. Spark is widely used by companies like Alibaba, Baidu, and Youku for large-scale data analytics and machine learning tasks. It allows for faster iterative jobs than Hadoop through its in-memory computing model and supports multiple workloads including streaming, SQL, and graph processing.
This document describes an interactive batch query system for game analytics based on Apache Drill. It addresses the problem of answering common ad-hoc queries over large volumes of log data by using a columnar data model and optimizing query plans. The system utilizes Drill's schema-free data model and vectorized query processing. It further improves performance by merging similar queries, reusing intermediate results, and pushing execution downwards to utilize multi-core CPUs. This provides a unified solution for both ad-hoc and scheduled batch analytics workloads at large scale.
刘诚忠:Running cloudera impala on postgre sqlhdhappy001
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This document summarizes a presentation about running Cloudera Impala on PostgreSQL to enable SQL queries on large datasets. Key points:
- The company processes 3 billion daily ad impressions and 20TB of daily report data, requiring a scalable SQL solution.
- Impala was chosen for its fast performance from in-memory processing and code generation. The architecture runs Impala coordinators and executors across clusters.
- The author hacked Impala to also scan data from PostgreSQL for mixed workloads. This involved adding new scan node types and metadata.
- Tests on a 150 million row dataset showed Impala with PostgreSQL achieving 20 million rows scanned per second per core.
This document discusses big data in the cloud and provides an overview of YARN. It begins with introducing the speaker and their experience with VMware and Apache Hadoop. The rest of the document covers: 1) trends in big data like the rise of YARN, faster query engines, and focus on enterprise capabilities, 2) how YARN addresses limitations of MapReduce by splitting responsibilities, 3) how YARN serves as a hub for various big data applications, and 4) how YARN can integrate with cloud infrastructure for elastic resource management between the two frameworks. The document advocates for open source contribution to help advance big data technologies.
Raghu nambiar:industry standard benchmarkshdhappy001
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Industry standard benchmarks have played a crucial role in advancing the computing industry by enabling healthy competition that drives product improvements and new technologies. Major benchmarking organizations like TPC, SPEC, and SPC have developed numerous benchmarks over time to keep up with industry needs. Looking ahead, new benchmarks are needed to address emerging technologies like cloud, big data, and the internet of things. International conferences and workshops bring together experts to collaborate on developing these new, relevant benchmarks.
Michael stack -the state of apache h basehdhappy001
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The document provides an overview of Apache HBase, an open source, distributed, scalable, big data non-relational database. It discusses that HBase is modeled after Google's Bigtable and built on Hadoop for storage. It also summarizes that HBase is used by many large companies for applications such as messaging, real-time analytics, and search indexing. The project is led by an active community of committers and sees steady improvements and new features with each monthly release.
This document discusses the Stinger initiative to improve the performance of Apache Hive. Stinger aims to speed up Hive queries by 100x, scale queries from terabytes to petabytes of data, and expand SQL support. Key developments include optimizing Hive to run on Apache Tez, the vectorized query execution engine, cost-based optimization using Optiq, and performance improvements from the ORC file format. The goals of Stinger Phase 3 are to deliver interactive query performance for Hive by integrating these technologies.
Bikas saha:the next generation of hadoop– hadoop 2 and yarnhdhappy001
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The document discusses Apache YARN, which is the next-generation resource management platform for Apache Hadoop. YARN was designed to address limitations of the original Hadoop 1 architecture by supporting multiple data processing models (e.g. batch, interactive, streaming) and improving cluster utilization. YARN achieves this by separating resource management from application execution, allowing various data processing engines like MapReduce, HBase and Storm to run natively on Hadoop frames. This provides a flexible, efficient and shared platform for distributed applications.