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These days it’s easy for companies to say, "We measure everything!” The problem is, most popular metrics may not be appropriate or relevant for your business. Measurement isn’t free and should be done strategically. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal explain how to align measurement with your product strategy so you can measure what matters for your business.

Using Customer 360 and the IoT as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Flink, Kudu, Spark Streaming, and Spark SQL and modern storage engines to enable new forms of data processing and analytics.

Vinithra Varadharajan, Jason Wang, Eugene Fratkin, and Mael Ropars detail new paradigms to effectively run production-level pipelines with minimal operational overhead. Join in to learn how to remove barriers to data discovery, metadata sharing, and access control.

Natural language processing is a key component in many data science systems. David Talby and Claudiu Branzan lead a hands-on tutorial on scalable NLP using spaCy for building annotation pipelines, Spark NLP for building distributed natural language machine-learned pipelines, and Spark ML and TensorFlow for using deep learning to build and apply word embeddings.

Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build 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.

The honeymoon era of data science is ending, and accountability is coming. Not content to wait for results that may or may not arrive, successful data science leaders deliver measurable impact on an increasing share of an enterprise's KPIs. Dan Enthoven outlines a holistic approach to people, process, and technology to build a sustainable competitive advantage.

Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.

From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry.

The need for instant data-driven insights has led the proliferation of messaging and streaming frameworks. Karthik Ramasamy, Arun Kejariwal, and Ivan Kelly walk you through state-of-the-art streaming frameworks, algorithms, and architectures, covering the typical challenges in modern real-time big data platforms and offering insights on how to address them.

Dean Wampler and Boris Lublinsky walk you through building streaming apps as microservices using Akka Streams and Kafka Streams. Along the way, Dean and Boris discuss the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to chose them instead.

Aurélie Pols walks you through a "5+5 pillars" framework for GDPR readiness, explaining what the GDPR means to data-fueled businesses. You'll learn how to attribute responsibility to assure compliance and build toward ethical data practices, minimizing risk for your company while fostering trust with your clients.

Danyel Fisher and Miriah Meyer explore the human side of data analysis and visualization, covering operationalization, the process of reducing vague problems to specific tasks, and how to choose a visual representation that addresses those tasks. Along the way, they also discuss single views and explain how to link them into multiple views.

Natural language processing techniques help address tasks like text classification, information extraction, and content generation. Barbara Fusinska offers an overview of natural language processing and walks you through building a bag-of-words representation, using Python and its machine learning libraries, and then using it for text classification.

Carl Osipov walks you through building a complete machine learning pipeline from ingest, exploration, training, and evaluation to deployment and prediction.

Vartika Singh, Marton Balassi, Steven Totman, and Juan Yu outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.

Hybrid big data deployments present significant new security risks. Security admins must ensure a consistently secured and governed experience for end users and administrators across multiple workloads. Mark Donsky, Steffen Maerkl, and André Araujo share best practices for meeting these challenges as they walk you through securing a Hadoop cluster.

Join us after tutorials on Tuesday in the Expo Hall. Grab a drink and mingle with fellow Strata attendees while you check out all of the exhibitors.

Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine London has to offer.