[TALK]@Telematika

Talks & Presentations Collection

Lessons learned building a scalable self-serve, real-time, multitenant monitoring service at Yahoo
October 23, 2019

Lessons learned building a scalable self-serve, real-time, multitenant monitoring service at Yahoo

Building a real-time monitoring service that handles millions of custom events per second while satisfying complex rules, varied throughput requirements, and numerous dimensions simultaneously is a complex endeavor. Sumeet Singh and Mridul Jain explain how Yahoo approached these challenges with Apache Storm Trident, Kafka, HBase, and OpenTSDB and discuss the lessons learned along the way.

Let's get real: Acting on data in real time

Let's get real: Acting on data in real time

October 23, 2019

Big data is not limited to reporting and analysis; increasingly, companies are differentiating themselves by acting on data in real time. But what does "real time" really mean? Jack Norris discusses the challenges of coordinating data flows, analysis, and integration at scale to truly impact business as it happens.
Machine learning for human rights advocacy: Big benefits, serious consequences

Machine learning for human rights advocacy: Big benefits, serious consequences

October 23, 2019

Megan Price demonstrates how machine-learning methods help us determine what we know, and what we don't, about the ongoing conflict in Syria. Megan then explains why these methods can be crucial to better understand patterns of violence, enabling better policy decisions, resource allocation, and ultimately, accountability and justice.
Netflix's big leap from Oracle to Cassandra

Netflix's big leap from Oracle to Cassandra

October 23, 2019

Roopa Tangirala details Netflix's migration from Oracle to Cassandra, covering the problems encountered, what worked and what didn't, and lessons learned along the way.
NoLambda: A new architecture combining streaming, ad hoc, machine-learning, and batch analytics

NoLambda: A new architecture combining streaming, ad hoc, machine-learning, and batch analytics

October 22, 2019

Developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda. Helena Edelson and Evan Chan highlight a much simpler approach using the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka) plus FiloDB, a new entrant to the distributed-database world, which combines streaming and ad hoc analytics.
Nonsense science

Nonsense science

October 22, 2019

Paula Poundstone isnt just any comedian. After years of justly criticizing and questioning the purpose of the many studies used for questions on NPRs #1 show, Wait Wait. . .Dont Tell Me, on which she's a popular panelist, shes here to explore what we can learn about asking the right questions from a unique critique of published behavioral research.
Old industries, sexy data: How machine learning is reshaping the world's backbone industries

Old industries, sexy data: How machine learning is reshaping the world's backbone industries

October 22, 2019

Over the past decade, machine learning has become intertwined with newer, Internet-born businesses. This despite the fact that the vast majority of global GDP turns on larger, less visible industries like energy and construction. David Beyer explores the ways these backbone industries are adopting machine-intelligent applications and the trends underlying this shift.
Open by design, open for data

Open by design, open for data

October 22, 2019

As the volume and variety of data continue to grow, organizations have the opportunity to transform their industries and professions, but companies are grappling with how to deliver innovation. Adam Kocoloski shares his experience around this market shift and challenges attendees to join his mission of contributing to the community and investing in the power of open source and the cloud.
Practical machine learning (Full Day)

Practical machine learning (Full Day)

October 22, 2019

This hands-on tutorial provides a quick start to building intelligent business applications using machine learning. Learn about machine-learning basics, feature engineering, anomaly detection, recommender systems, and deep learning as you are guided through all the steps of prototyping and production: data cleaning, feature engineering, model building and evaluation, and deployment.
Putting Kafka into overdrive

Putting Kafka into overdrive

October 22, 2019

Apache Kafka lies at the heart of the largest data pipelines, handling trillions of messages and petabytes of data every day. Learn the right approach for getting the most out of Kafka from the experts at LinkedIn and Confluent. Todd Palino and Gwen Shapira explore how to monitor, optimize, and troubleshoot performance of your data pipelinesfrom producer to consumer, development to production.
Python scalability: A convenient truth

Python scalability: A convenient truth

October 21, 2019

Despite Python's popularity throughout the data-engineering and data science workflow, the principles behind its performance and scaling behavior are less understood. Travis Oliphant explains best practices and modern tools to scale Python to larger-than-memory and distributed workloads without sacrificing its ease of use or being forced to adopt heavyweight frameworks.
Radical candor: Be a better boss

Radical candor: Be a better boss

October 21, 2019

Bad bosses make people miserable. They kill innovation, stifle growth, increase costs, and create instability. Well-meaning people become bad bosses without even realizing it. Great bosses have relationships with employees and are sources of growth and stability for both individuals and companies. Kim Scott outlines three principles for approaching the relationship between a boss and their team.
Real-time fraud detection using process mining with Spark Streaming

Real-time fraud detection using process mining with Spark Streaming

October 21, 2019

If you consider user click paths a process, you can apply process mining. Process mining models users based on their actual behavior, which allows us to compare new clicks with modeled behavior and report any inconsistencies. Bolke de Bruin and Hylke Hendriksen explain how ING implemented process mining on Spark Streaming, enabling real-time fraud detection.
Real-time Hadoop: What an ideal messaging system should bring to Hadoop

Real-time Hadoop: What an ideal messaging system should bring to Hadoop

October 21, 2019

Application messaging isnt newsolutions include IBM MQ, RabbitMQ, and ActiveMQ. Apache Kafka is a high-performance, high-scalability alternative that integrates well with Hadoop. Can modern distributed messaging systems like Kafka be considered a legacy replacement or is it purely complementary? Ted Dunning outlines Kafka's architectural benefits and tradeoffs to find the answer.
Scala and the JVM as a big data platform: Lessons from Apache Spark

Scala and the JVM as a big data platform: Lessons from Apache Spark

October 21, 2019

The success of Apache Spark is bringing developers to Scala. For big data, the JVM uses memory inefficiently, causing significant GC challenges. Spark's Project Tungsten fixes these problems with custom data layouts and code generation. Dean Wampler gives an overview of Spark, explaining ongoing improvements and what we should do to improve Scala and the JVM for big data.
Scalable schema management for Hadoop and Spark applications

Scalable schema management for Hadoop and Spark applications

October 21, 2019

Schema plays a key role in the Hadoop architecture at Uber. Kelvin Chu and Evan Richards explain why schema is important and how it can make your Hadoop and Spark application more reliable and efficient.
Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks

Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks

October 20, 2019

Celtra provides a platform for customers like Porsche and Fox to create, track, and analyze digital display advertising. Celtra's platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Grega Kepret outlines Celtras data-pipeline challenges and explains how it solved them by combining Snowflake's cloud data warehouse with Spark.
Self-service, interactive analytics at multipetabyte scale in capital markets regulation on the cloud

Self-service, interactive analytics at multipetabyte scale in capital markets regulation on the cloud

October 20, 2019

Scott Donaldson and Matt Cardillo detail the security measures and system architecture needed to bring alive a multipetabyte data warehouse via interactive analytics and directed graphs from several trillions of market events, using HBase, EMR, Hive, Redshift, and S3 technologies in a cost-efficient manner.
Successful enterprise data hub design patterns at BT

Successful enterprise data hub design patterns at BT

October 20, 2019

Phillip Radley explores how to use an accumulation of marginal gains approach to achieve success with an Apache Hadoop-based enterprise data hub (EDH), drawing on a set of design patterns built up over five years establishing BTs EDH.
Summoning the demon: My perspective from the belly of the beast of AI

Summoning the demon: My perspective from the belly of the beast of AI

October 20, 2019

We hear about AI almost every day now. Opinions seem split between impending doom side and "superintelligence will save the human race." Jana Eggers offers the real deal on AI, explaining what's hype and what isn't and what we can do about it.
Tame that beast: How to bring operations, governance, and reliability to Hadoop

Tame that beast: How to bring operations, governance, and reliability to Hadoop

October 20, 2019

Many companies have created extremely powerful Hadoop use cases with highly valuable outcomes. The diverse adoption and application of Hadoop is producing an extremely robust ecosystem. However, teams often create silos around their Hadoop, forgetting some of the hard-learned lessons IT has gained over the years. Keith Manthey discusses one such often overlooked featuregovernance.
TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)

TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)

October 20, 2019

Kazunori Sato and Amy Unruh explore how you can use TensorFlow to drive large-scale distributed machine learning against your analytic data sitting in Google BigQuery, with data preprocessing driven by Dataflow (now Apache Beam). Kazunori and Amy dive into practical examples of how these technologies can work together to enable a powerful workflow for distributed machine learning.
TensorFlow: Machine learning for everyone

TensorFlow: Machine learning for everyone

October 20, 2019

TensorFlow is an open source software library for numerical computation with a focus on machine learning. Rajat Monga offers an introduction to TensorFlow and explains how to use it to train and deploy machine-learning models to make your next application smarter.
The Internet of Things: How to do it. Seriously!

The Internet of Things: How to do it. Seriously!

October 20, 2019

The Internet of Things (IoT) continues to provide value and hold promise for both the consumer and enterprise alike. To succeed, an IoT project must concern itself with how to ingest data, build actionable models, and react in real time. Chris Rawles describes approaches to addressing these concerns through a deep dive into an interactive demo centered around classification of human activities.
The state of visualization: Application and practice

The state of visualization: Application and practice

October 20, 2019

Noah Iliinsky surveys the state of visualization, outlines the major trends in the field, and explores the directions that visualization is headed. Noah also dives into the assorted tool domainsfrom enterprise to desktop to code-basedand discusses the pros and cons and use cases of each.
Thinking like a Bayesian

Thinking like a Bayesian

October 19, 2019

Julia Galef explores why Bayesian thinking is so different from what we do by default and outlines the most important principles of thinking like a Bayesian.
Three principles for a data-centric security architecture on Hadoop to simplify your life

Three principles for a data-centric security architecture on Hadoop to simplify your life

October 19, 2019

Pratik Verma and Paulo Pereira share three security architecture principles for Hadoop to protect sensitive data without disrupting users: modifying requests to filter content makes security transparent to users; centralizing data-access decisions and distributing enforcement makes security scalable; and using metadata instead of files or tables ensures systematic protection of sensitive data.
Toppling the mainframe: Enterprise-grade streaming under 2 ms on Hadoop

Toppling the mainframe: Enterprise-grade streaming under 2 ms on Hadoop

October 19, 2019

What if we have reached the point where open source can handle massively difficult streaming problems with enterprise-grade durability? Ilya Ganelin presents Capital Ones novel solution for real-time decisioning on Apache Apex. Ilya shows how Apex provides unique capabilities that ensure less than 2 ms latency in an enterprise-grade solution on Hadoop.