Bas Geerdink explains why and how ING is becoming more and more data-driven, sharing use cases, architecture, and technology choices along the way.

While other industries have embraced the digital era, healthcare is still playing catch-up. Kaiser Permanente has been a leader in healthcare technology and first started using computing to improve healthcare results in the 1960s. Taposh Roy, Rajiv Synghal, and Sabrina Dahlgren offer an overview of Kaisers big data strategy and explain how other organizations can adopt similar strategies.

Narasimhan Sampath and Avinash Ramineni share how Choice Hotels International used Spark Streaming, Kafka, Spark, and Spark SQL to create an advanced analytics platform that enables business users to be self-reliant by accessing the data they need from a variety of sources to generate customer insights and property dashboards and enable data-driven decisions with minimal IT engagement.

Jeff Carpenter describes how data modeling can be a key enabler of microservice architectures for transactional and analytics systems, including service identification, schema design, and event streaming.

Netflix is exploring new avenues for data processing where traditional approaches fail to scale. Ryan Blue explains how Netflix is building on Parquet to enhance its 40+ petabyte warehouse, combining Parquet's features with Presto and Spark to boost ETL and interactive queries. Information about tuning Parquet is hard to find. Ryan shares what he's learned, creating the missing guide you need.

Google is no stranger to big data, pioneering several big data technologies grown and tested internallyincluding MapReduce, BigTable, and most recently Dataflow and TensorFlow, as well as one of the most heavily used tools at Google, BigQueryand making them available to everyone. Jordan Tigani shares what big data means for Google and announces several new BigQuery features.

The traditional data warehouse of the 1990s was quaintly called the single source of truth. Joe Hellerstein explains why today we take a far more relativistic view: the meaning of data depends on the context in which it is used.

It is fashionable today to declare doom and gloom for the data lake. Alex Gorelik discusses best practices for Hadoop data lake success and provides real-world examples of successful data lake implementations in a non-vendor-specific talk.

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.

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.