Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: AI and Data technologies in the cloud sessions

Add to your personal schedule
9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Data Engineering and Architecture
Location: London Suite 3
Jorge Lopez (Amazon Web Services), Nikki Rouda (Amazon Web Services), Damon Cortesi (AWS), Sven Hansen (Amazon Web Services), Manos Samatas (Amazon Web Services), Alket Memushaj (Amazon Web Services)
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join in to learn how to incorporate serverless concepts into your big data architectures. You'll explore design patterns to ingest, store, and analyze your data as you build a big data application using AWS technologies such as S3, Athena, Kinesis, and more Read more.
Add to your personal schedule
9:0012:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15
Holden Karau (Google), Trevor Grant (IBM), Ilan Filonenko (Bloomberg LP), Francesca Lazzeri (Microsoft)
This workshop will quickly introduce what Kubeflow is, and how we can use it to train and serve models across different cloud environments (and on-prem). We’ll have a script to do the initial set up work ready so you can jump (almost) straight into training a model on one cloud, and then look at how to set up serving in another cluster/cloud. We will start with a simple model w/follow up links. Read more.
Add to your personal schedule
9:0012:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 4
Krishnan Saidapet (REAN Cloud, A Hitachi Vantara company)
Krishnan Saidapet offers an overview of the latest big data and machine learning serverless technologies from AWS and leads a deep dive into using them to process and analyze two different datasets: the publicly available Bureau of Labor Statistics dataset and the Chest X-Ray Image Data dataset. Read more.
Add to your personal schedule
9:0012:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 2/3
Melinda King (ROI Training)
Melinda King offers an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you’ll learn machine learning (ML) and TensorFlow concepts, and develop skills in developing, evaluating, and productionizing ML models. Read more.
Add to your personal schedule
9:0012:30 Tuesday, 30 April 2019
Mark Madsen (Think Big Analytics), Todd Walter (Teradata)
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build a multiuse data infrastructure that is not subject to past constraints. Mark Madsen and Todd Walter explore design assumptions and principles and walk you through a reference architecture to use as you work to unify your analytics infrastructure. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 30 April 2019
Data Engineering and Architecture
Location: Capital Suite 4
Colm Moynihan (Cloudera), Jonathan Seidman (Cloudera), Michael Kohs (Cloudera)
Moving to the cloud poses challenges from re-architecting to be cloud-native, to data context consistency across workloads that span multiple clusters on-prem and in the cloud. First, we’ll cover in depth cloud architecture and challenges; second, you’ll use Cloudera Altus to build data warehousing and data engineering clusters and run workloads that share metadata between them using Cloudera SDX. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 30 April 2019
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio)
Many industry segments have been grappling with fast data (high-volume, high-velocity data). In this tutorial we shall lead the audience through a journey of the landscape of state-of-the-art systems for each stage of an end-to-end data processing pipeline - messaging, compute and storage - for real-time data and algorithms to extract insights - e.g., heavy-hitters, quantiles - from data streams. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 11
Melinda King (ROI Training)
Melinda King offers an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you’ll learn machine learning (ML) and TensorFlow concepts and develop skills in developing, evaluating, and productionizing ML models. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 30 April 2019
Data Engineering and Architecture
Location: Capital Suite 15
Matt Fuller (Starburst)
Used by Facebook, Netflix, Airbnb, LinkedIn, Twitter, Uber, and others, Presto has become the ubiquitous open source software for SQL on anything. Presto was built from the ground up for fast interactive SQL analytics against disparate data sources ranging in size from GBs to PBs. Join Matt Fuller to learn how to use Presto and explore use cases and best practices you can implement today. Read more.
Add to your personal schedule
13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 2/3
Francesca Lazzeri (Microsoft), Aashish Bhateja (Microsoft)
Time series modeling and forecasting is fundamentally important to various practical domains; in the past few decades, machine learning model-based forecasting has become very popular in both private and public decision-making processes. Francesca Lazzeri and Aashish Bhateja walk you through using Azure Machine Learning to build and deploy your time series forecasting models. Read more.
Add to your personal schedule
11:1511:55 Wednesday, 1 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Itai Yaffe (Nielsen)
At Nielsen Marketing Cloud, we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. To achieve that, we need to ingest billions of events per day into our big data stores and we need to do it in a scalable yet cost-efficient manner. In this talk, we will discuss how we continuously transform our data infrastructure to support these goals. Read more.
Add to your personal schedule
11:1511:55 Wednesday, 1 May 2019
Wojciech Biela (Starburst), Piotr Findeisen (Starburst)
Presto is a popular open source distributed SQL engine for interactive queries over heterogeneous data sources (Hadoop/HDFS, Amazon S3/Azure ADSL, RDBMS, no-SQL, etc). Recently Starburst has contributed the Cost-Based Optimizer for Presto which brings a great performance boost for Presto. Learn about this CBO’s internals, the motivating use cases and observed improvements. Read more.
Add to your personal schedule
11:1511:55 Wednesday, 1 May 2019
Felipe Hoffa (Google)
Before releasing a public dataset, practitioners need to thread the needle between utility and protection of individuals. We will explore massive public datasets, taking you from theory to real life showcasing newly available tools that help with PII detection and brings concepts like k-anonymity and l-diversity to the practical realm (with options such as removing, masking, and coarsening). Read more.
Add to your personal schedule
11:1511:55 Wednesday, 1 May 2019
Mike Olson (Cloudera)
Managing your data securely is difficult, as are choosing the right machine learning tools and managing models and applications in compliance with regulation and law. Mike Olson covers the risks and the issues that matter most and explains how to address them with an enterprise data cloud and by embracing your data center and the public cloud in combination. Read more.
Add to your personal schedule
11:1511:55 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 10/11
Avner Braverman (Binaris)
What is serverless, and how can it be utilized for data analysis and AI? Avner Braverman outlines the benefits and limitations of serverless with respect to data transformation (ETL), AI inference and training, and real-time streaming. This is a technical talk, so expect demos and code. Read more.
Add to your personal schedule
12:0512:45 Wednesday, 1 May 2019
Jacques Nadeau (Dremio)
Performance and cost are two important considerations in determining optimized solutions for SQL workloads in the cloud. Jacques Nadeau explains how to accelerate TPC workloads, invisible to client apps, and how to use Apache Arrow, Parquet, and Calcite to provide a scalable, high-performance solution optimized for cloud deployments while significantly reducing operational costs. Read more.
Add to your personal schedule
14:0514:45 Wednesday, 1 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Simona Meriam (Nielsen)
Ingesting billions of events per day into our big data stores we need to do it in a scalable, cost-efficient and consistent way. When working with Spark and Kafka the way you manage your consumer offsets has a major implication on data consistency. We will go in depths of the solution we ended up implementing and discuss the working process, the dos and don'ts that led us to its final design. Read more.
Add to your personal schedule
14:5515:35 Wednesday, 1 May 2019
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio)
In this talk, we shall walk the audience through an architecture whereby models are served in real-time and the models are updated, using Apache Pulsar, without restarting the application at hand. Further, we will describe how Pulsar functions can be applied to support two example use cases, viz., sampling and filtering. We shall lead the audience through a concrete case study of the same. Read more.
Add to your personal schedule
14:5515:35 Wednesday, 1 May 2019
Data Engineering and Architecture, Expo Hall, Streaming and IoT
Location: Expo Hall 2 (Capital Hall N24)
Geir Engdahl (Cognite), Daniel Bergqvist (Google)
Geir Engdahl and Daniel Bergqvist explain how Cognite is developing IIoT smart maintenance systems that can process 10M samples a second from thousands of sensors. You'll explore an architecture designed for high performance, robust streaming sensor data ingest, and cost-effective storage of large volumes of time series data as well as best practices learned along the way. Read more.
Add to your personal schedule
14:5515:35 Wednesday, 1 May 2019
Holden Karau (Google), Mikayla Konst (Google), Ben Sidhom (Google)
As more workloads move to severless-like environments, the importance of properly handling downscaling increases. Holden Karau, Mikayla Konst, and Ben Sidhom explore approaches for improving the scale-down experience on open source cluster managers—everything from how to schedule jobs to location of blocks and their impact. Read more.
Add to your personal schedule
16:3517:15 Wednesday, 1 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Constantin Muraru (Adobe), Dan Popescu (Adobe)
Obtaining servers to run your realtime application has never been easier. Cloud providers have removed the cumbersome process of provisioning new hardware, to suite your needs. What happens though when you wish to deploy your (web) applications frequently, on hundreds or even thousands of servers in a fast and reliable way with minimal human intervention? This session addresses this precise topic. Read more.
Add to your personal schedule
16:3517:15 Wednesday, 1 May 2019
Anirudha Beria (Qubole), Rohit Karlupia (Qubole)
Autoscaling of resources aims to achieve low latency for a big data application while reducing resource costs at the same time. Scalability-aware autoscaling uses historical information to make better scaling decisions. Anirudha Beria and Rohit Karlupia explain how to measure the efficiency of autoscaling policies and discuss more efficient autoscaling policies, in terms of latency and costs. Read more.
Add to your personal schedule
17:2518:05 Wednesday, 1 May 2019
Mark Samson (Cloudera), Phillip Radley (BT)
It is now possible to build a modern data platform capable of storing, processing and analysing a wide variety of data across multiple public and private Cloud platforms and on-premise data centres. This session will outline an information architecture for such a platform, informed by working with multiple large organisations who have built such platforms over the last 5 years. Read more.
Add to your personal schedule
11:1511:55 Thursday, 2 May 2019
Jian Zhang (Intel), Chendi Xue (Intel), Yuan Zhou (Intel)
Introduce the challenges of migrating bigdata analytics workloads to public cloud - like performance lost, and missing features. Show case how to the new in memory data accelerator leveraging persistent memory and RDMA NICs can resolve this issues and enables new opportunities for bigdata workloads on the cloud. Read more.
Add to your personal schedule
11:1511:55 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 10/11
Eoin O'Flanagan (NewDay), Darragh McConville (Kainos)
Eoin O'Flanagan and Darragh McConville explain how NewDay built a high-performance contemporary data processing platform, from the ground up, on AWS. Join in to explore the company's journey from a traditional legacy onsite data estate to an entirely cloud-based PCI DSS-compliant platform. Read more.
Add to your personal schedule
12:0512:45 Thursday, 2 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Kai Wähner (Confluent)
How can you leverage the flexibility and extreme scale in public cloud combined with Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud? Join this talk to learn how to apply technologies such as TensorFlow with Kafka’s open source ecosystem for machine learning infrastructures Read more.
Add to your personal schedule
12:0512:45 Thursday, 2 May 2019
David Josephsen (Sparkpost)
This is the story of how Sparkpost Reliability Engineering abandoned ELK for a DIY Schema-On-Read logging infrastructure. We share architectural details and tribulations from our _Internal Event Hose_ data ingestion pipeline project, which uses Fluentd, Kinesis, Parquet and AWS Athena to make logging sane. Read more.
Add to your personal schedule
12:0512:45 Thursday, 2 May 2019
Pradeep Bhadani (Hotels.com), Elliot West (Hotels.com)
Expedia Group is a travel platform with an extensive portfolio including Expedia.com and Hotels.com. We like to give our data teams flexibility and autonomy to work with different technologies. However, this approach generates challenges that cannot be solved by existing tools. We'll explain how we built a unified virtual data lake on top of our many heterogeneous and distributed data platforms. Read more.
Add to your personal schedule
14:0514:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Willem Pienaar (GO-JEK), Zhi Ling Chen (GO-JEK)
Features are key to driving impact with AI at all scales. By democratizing the creation, discovery, and access of features through a unified platform, organizations are able to dramatically accelerate innovation and time to market. Find out how GOJEK, Indonesia's first billion-dollar startup, unlocked insights in AI by building a feature store called Feast, and the lessons they learned along the.. Read more.
Add to your personal schedule
14:0514:45 Thursday, 2 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Holden Karau (Google), Kris Nova (VMware)
In the Kubernetes world, where declarative resources are a first-class citizen, running complicated workloads across distributed infrastructure is easy, and processing big data workloads using Spark is common practice, we can finally look at constructing a hybrid system of running Spark in a distributed cloud native way. Join respective experts Kris Nova and Holden Karau for a fun adventure. Read more.
Add to your personal schedule
14:5515:35 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Jane McConnell (Teradata), Sun Maria Lehmann (Equinor)
In Upstream Oil and Gas, a vast amount of the data requested for analytics projects is “scientific data” - physical measurements about the real world. Historically this data has been managed “library-style” in files - but to provide this data to analytics projects, we need to do something different. Sun and Jane discuss architectural best practices learned from their work with subsurface data. Read more.
Add to your personal schedule
14:5515:35 Thursday, 2 May 2019
Nikki Rouda (Amazon Web Services)
Nikki Rouda shares key trends in data lakes and analytics and explains how they shape the services offered by AWS. Specific topics include the rise of machine-generated data and semistructured and unstructured data as dominant sources of new data, the move toward serverless, SPI-centric computing, and the growing need for local access to data from users around the world. Read more.
Add to your personal schedule
14:5515:35 Thursday, 2 May 2019
Greg Rahn (Cloudera)
Data warehouses have traditionally run in the data center, and in recent years, they've been adapted to be more cloud native. Greg Rahn discusses a number of emerging trends and technologies that will impact how data warehouses are run both in the cloud and on-premises and explains what that means for architects, administrators, and end users. Read more.
Add to your personal schedule
16:3517:15 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Max Schultze (Zalando SE)
Data Lake implementation at a large scale company, raw data collection, standardized data preparation (e.g. binary conversion, partitioning), user driven analytics and machine learning. Read more.
Add to your personal schedule
16:3517:15 Thursday, 2 May 2019
Thomas Phelan (BlueData)
Organizations need to keep ahead of their competition by using the latest AI, ML, and DL technologies such as Spark, TensorFlow, and H2O. The challenge is in how to deploy these tools and keep them running in a consistent manner while maximizing the use of scarce hardware resources, such as GPUs. Thomas Phelan discusses the effective deployment of such applications in a container environment. Read more.