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。
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。
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。
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。
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。
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。
How Enterprises Leverage Data to Overcome Business Challenges During CoronavirusDenodo
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
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.
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。
How Enterprises Leverage Data to Overcome Business Challenges During CoronavirusDenodo
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
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.
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。
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.
Modernising Data Architecture for Data Driven Insights (Chinese)Denodo
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
3. 群体和个体之间交互沟通的方式发生了
翻天覆地的变换由此带来的信息量的巨增
+ + = A brand new game
Consumers become increasingly instrumented
Consumers become increasingly interconnected
Consumers become increasingly intelligent
DTCC2012
IBM Institute for Business Value CMO Study
3 2011
4. Big Data 的机遇
从巨大、以无与伦比的速度增长和多样化的数据中
提取远见卓识,而这些是以前无法做到的
DTCC2012
4
13. 基于BigData 平台的预测分析
tokens
documents
topics
documents
words
topics
V ≈ W x H
while (i < max iteration) {
H = H ∗ (WT V / WTWH);
W = W ∗ (V HT / WHHT );
}
i = i + 1;DTCC2012
13
15. IBM Big Data Solutions 客户和合作伙伴方案 规则 / 业务流程管理
iLog & Lombardi
数据仓库
Big Data 加速器 InfoSphere
Warehouse
文本 统计数据 财经 地理信息 音频信息 数据仓库一体机
影像/视频 挖掘 时间序列 数学信息 IBM & non-IBM
连接器 应用 蓝图 主数据管理
INTEGRATION
InfoSphere MDM
Big Data 企业引擎 数据库
DB2 & non-IBM
内容分析
InfoSphere Streams InfoSphere BigInsights ECM
业务分析
提升和优化生产效率
Information Server
Cognos & SPSS
工作负载管理和优化 配置 工作流 工作时间表 作业跟踪 数据摄入 营销
Unica
管理 管理工具 配置管理器 事件监控 身份和访问管理 数据保护
DTCC2012 数据增长管理
InfoSphere Optim
15
16. IBM big data
IBM big data • IBM big data • IBM big data
THINK
• IBM big data
• IBM big data
IBM big data
IBM big data • IBM big data • IBM big data
DTCC2012