概要

DataWorks Summit Tokyo 2018                                                           国内最大級のビックデータ・ IoTデータ基盤のイベント   Ideas. Insights. Innovation.

DataWorks Summit Singapore is one amazing day of learning and discovery where developers and businesses come together to explore what’s next in AI, machine learning, IoT, cloud and more. Don’t miss your chance to network with top industry peers and pioneers to learn how to apply open source technology to make data work and accelerate your digital transformation.

講演者

Santhosh B Gowda is an Engineering Manager QE at Hortonworks. He takse care of the scale, resiliency and performance aspects for Hortonworks products (i.e. HDP, HDF and Dataplane). He has more than 13 years of IT experience and has focused on Hadoop for the last 2 years. He holds an M.S Software Systems degree from BITS, Pilani.

アジェンダ

アジェンダ一覧

THURSDAY, OCT 11
8:00 AM - 9:00 AM
受付開始
9:00 AM - 10:30 AM
基調講演
11:00 AM - 12:30 PM
トラックセッション
11:00 AM - 1:30 PM
Crash Courses
 
12:30 PM - 2:00 PM
ネットワーキング 昼食
2:00 PM - 4:30 PM
Crash Courses
2:00 PM - 5:30 PM
トラックセッション
5:30 PM - 6:30 PM
Expo会場にてレセプション
セッション

セッション

    • AI・データサイエンス
    • クラウド・ビッグデータアーキテクチャ
    • データウェアハウジング・オペレーショナルデータストア
    • IoT・ストリーミング分析
Artificial intelligence (AI) is transforming every industry. Data science and machine learning are opening new doors in process automation, predictive analytics, and decision optimization. This track offers sessions spanning the entire data science lifecycle: development, test, and production.

You’ll see examples of innovative analytics applications and systems for data visualization, statistics, machine learning, cognitive systems, and deep learning. We’ll show you how to use modern open source workbenches to develop, test, and evaluate advanced AI models before deploying them. You’ll hear from leading researchers, data scientists, analysts, and practitioners who are driving innovation in AI and data science.

Sample technologies: TensorFlow, Keras, Apache Spark, PyTorch, Apache MXNet, Theano, DL4J, R, scikit-learn, DSX, Apache Zeppelin
A modern data architecture enables enterprises to scale along with their data growth, provides flexibility to consume any and all data sources, and provides platforms to drive deep insights from the latest open source analytical tools. Striking the right balance between data strategy and cloud strategy is the first step. For many enterprises a hybrid multi-cloud data architecture that optimizes their information architecture between on-premises and the cloud is critical. It also needs to provide a global and integrated view of all their data with consistent operations, governance, and security.

This track provides the latest best practices on how to build modern data architectures. You’ll learn about key open source projects, including Apache Hadoop and related technologies, and how they integrate with the latest cloud offering to enable these transformative changes. You’ll interact with technical leads, committers, and experts who are driving the roadmaps, key features, and research around what is coming next and the extended open source big data architecture.
Data engineers and architects use multiple engines to process data in the most appropriate way, from batch ETL, to interactive SQL, to low latency NoSQL. Sessions will cover the SQL engines and tools that help users to derive the most from their data on premises and in the cloud and enrich their enterprise data warehouse (EDW).

You’ll learn how NoSQL stores like Apache HBase are adding transactional capabilities that bring traditional operational data store (ODS) workloads to Hadoop and why data preparation is a key workload. You’ll meet Apache community rock stars and learn how these innovators are building the applications of the future.

Sample technologies: Apache Hive, Apache Tez, Apache ORC, Apache Druid, Apache HBase, Apache Phoenix
The rapid proliferation of sensors and connected devices is fueling an explosion of data. Streaming data allows algorithms to dynamically adapt to new patterns in data, which is critical in applications like fraud detection and stock price prediction. Deploying real-time machine learning models in data streams enables insights and interactions not previously possible.

In this track you’ll learn how to apply machine learning to capture perishable insights from streaming data sources and how to interface with devices at the “jagged edge.” Sessions present new strategies and best practices for real-time data ingestion and analysis. Presenters will show how to use these technologies to develop IoT solutions and how to combine historical with streaming data to build dynamic, real-time predictive systems for actionable insights.

Sample technologies: Apache Nifi, Apache Storm, Streams Messaging Manager, Streaming Analytics Manager, Apache Flink, Apache Spark Streaming, Apache Beam, Apache Pulsar and Apache Kafka
スポンサー

スポンサー

Packages & Passes

Packages & Passes

Conference Pass
Standard
Sep 8 - Oct 10, 2018
Onsite
Oct 11, 2018
Alumni
Through Oct 11, 2018
全て閲覧
基調講演、トラックセッション
S$599
S$699
S$399
 
Package
Full Conference*
 
Standard
Sep 8 - Oct 10, 2018
S$599
Onsite
Oct 11, 2018
S$699
Alumni
Through Oct 11, 2018
S$399
*Access to DataWorks Summit keynotes, breakouts, and meals, including crash courses and the sponsor reception.
会場とアクセス

会場とアクセス

ロケーションアイコン
Swissôtel the Stamford

Stamford Road, Swissôtel The Stamford, Singapore

+65 6338 8585

イベント会場のウェブサイトはこちら

Swissôtel the Stamford

Stamford Road, Swissôtel The Stamford, Singapore

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