DataWorks Summit: 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.

Agenda At A Glance

THURSDAY, OCT 11
8:00 AM - 9:00 AM
Networking Breakfast
9:00 AM - 10:30 AM
General Session
10:30 AM - 12:30 PM
Track Sessions and Crash Courses
 
12:30 PM - 2:00 PM
Networking Lunch
2:00 PM - 5:30 PM
Track Sessions and Crash Courses
5:30 PM - 6:30 PM
Reception in Expo Hall

Featured Speakers

Sanjay is founder and chief architect at Hortonworks, and
an Apache Hadoop committer and member of the Apache Hadoop PMC.
Prior to co-founding Hortonworks, Sanjay was the chief architect of core-Hadoop at Yahoo and part of the team that created Hadoop. In Hadoop he has contributed to several areas including HDFS, MapReduce schedulers, Yarn's design, high availability, compatibility, etc.
He has also held senior engineering positions at Sun Microsystems and INRIA, where he developed software for distributed systems and grid/utility computing infrastructures.
Sanjay has a PhD in Computer Science from the University of Waterloo in Canada.

Andy LoPresto is a Sr. Member of Technical Staff at Hortonworks working on the Hortonworks DataFlow team. In this role he serves as both a Committer and Project Management Committee Member for Apache NiFi, an open source, robust, secure data routing and delivery system. Andy focuses on security concerns within NiFi including identity management, TLS negotiation, data protection, access control, encryption and hashing. Andy is also involved with the sub-project, Apache MiNiFi, which drives edge data collection, including secure command and control and immediate data provenance and governance. He has presented about NiFi at DataWorks Summit Berlin 2018, DataWorks Summit Sydney 2017, Hadoop Summit San Jose 2016, FOSDEM '17 in Brussels, and the OpenIoT Summit 2017.

Tracks

DataWorks Summit Singapore is your best opportunity to participate in business and technical tracks dedicated to enabling next-generation data platforms. You’ll hear industry experts, architects, and data scientists share success stories, best practices, cautionary tales, and technology insights that provide practical guidance to novices as well as experienced practitioners of modern data infrastructure.

Tracks are divided into four key topic areas, which will cover:

Artificial Intelligence and Data Science

Artificial Intelligence and Data Science

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

Cloud and Big Data Architecture

Cloud and Big Data Architecture

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 Warehousing and Operational Data Stores

Data Warehousing and Operational Data Stores

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

IoT and Streaming Analytics

IoT and Streaming Analytics

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

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

Packages & Passes

Conference Pass
Early Bird
Thru Sep 7, 2018
Standard
Sep 8 - Oct 10
On-Site
Alumni
Thru Sep, 28 2018
Full Conference
Access to DataWorks Summit keynotes, breakouts, and meals, including crash courses and the sponsor reception.
S$399
S$599
S$699
S$399
 

Sponsors

Venue & Travel Info

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Swissôtel the Stamford

Stamford Road, Swissôtel The Stamford, Singapore

+65 6338 8585

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Swissôtel the Stamford

Stamford Road, Swissôtel The Stamford, Singapore

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