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Recent advances in deep learning allow us to use the semantic content of items in recommendation systems, addressing a weakness of traditional methods. Shioulin Sam explores the limitations of classical approaches and explains how using the content of items can help solve common recommendation pitfalls, such as the cold start problem, and open up new product possibilities.

James Dreiss discusses the challenges in building a content recommendation system for one of the largest news sites in the world, Reuters.com. The particularities of the system include developing a scrolling newsfeed and the use of document vectors for semantic representation of content.

Online recommender systems often rely heavily on user engagement features. This can cause a bias toward exploitation over exploration, overoptimizing on users' interests. Content diversification is important for user satisfaction, but measuring and evaluating impact is challenging. Ahsan Ashraf outlines techniques used at Pinterest that drove ~2–3% impression gains and a ~1% time-spent gain.

As LinkedIn encouraged members to join conversations, it found itself in danger of creating a "rich get richer" economy in which a few creators got an increasing share of all feedback. Bonnie Barrilleaux explains why you must regularly reevaluate metrics to avoid perverse incentives—situations where efforts to increase the metric cause unintended negative side effects.

Businesses rely on user data to power their sites, products, and sales. Can we give back by sharing those insights with users? Masha Westerlund explains how Investopedia harnessed reader data to build an index that tracks market anxiety and moves with the VIX, a proprietary measure of market volatility. You'll see how thinking outside the box helps turn data into tools for users, not stakeholders.

An understanding of graph-based analytical techniques can be extremely powerful when applied to modern practical problems, and modern frameworks and analytical techniques are making graph analysis methods viable for increasingly large, complex tasks. Zachary Hanif examines three prominent graph analytic methods, including graph convolutional networks, and applies them to concrete use cases.

Daniel Kang offers an overview of exploratory video analytics engine BlazeIt, which offers FrameQL, a declarative SQL-like language for querying video, and a query optimizer for executing these queries. You'll see how FrameQL can capture a large set of real-world queries ranging from aggregation and scrubbing and how BlazeIt can execute certain queries up to 2,000x faster than a naive approach.

Containers are a hot technology for application developers, but they also provide key benefits for data scientists. William Benton details the advantages of containers for data scientists and AI developers, focusing on high-level tools that will enable you to become more productive and collaborate more effectively.

Atul Kale and Xiaohan Zeng offer an overview of Bighead, Airbnb's user-friendly and scalable end-to-end machine learning framework that powers Airbnb's data-driven products. Built on Python, Spark, and Kubernetes, Bighead integrates popular libraries like TensorFlow, XGBoost, and PyTorch and is designed be used in modular pieces.

Chang Liu offers an overview of a common problem faced by many software companies, the cold-start problem, and explains how Georgian Partners has been successful at solving this problem by transferring knowledge from existing data through differentially private data aggregation.

Programming by input-output examples (PBE) is a new frontier in AI, set to revolutionize the programming experience for the masses. It can enable end users—99% of whom are nonprogrammers—to create small scripts and make data scientists 10–100x more productive for many data wrangling tasks. Sumit Gulwani leads a deep dive into this new programming paradigm and explores the science behind it.

Narrative economics studies the impact of popular narratives and stories on economic fluctuations in the context of human interests and emotions. Andreea Kremm and Mohammed Ibraaz Syed describe the use of emotion analysis, entity relationship extraction, and topic modeling in modeling narratives from written human communication.

Olga Cuznetova and Manna Chang demonstrate supervised and unsupervised learning methods to work with claims data and explain how the methods complement each other. The supervised method looks at CKD patients at risk of developing end-stage renal disease (ESRD), while the unsupervised approach looks at the classification of patients that tend to develop this disease faster than others.

The rate of growth of data volume and velocity has been accelerating along with increases in the variety of data sources. This poses a significant challenge to extracting actionable insights in a timely fashion. Arun Kejariwal and Francois Orsini explain how marrying correlation analysis with anomaly detection can help and share techniques to guide effective decision making.

Roger Barga, Sudipto Guha, and Kapil Chhabra explain how unsupervised learning with the robust random cut forest (RRCF) algorithm enables insights into streaming data and share new applications to impute missing values, forecast future values, detect hotspots, and perform classification tasks. They also demonstrate how to implement unsupervised learning over massive data streams.

"Anyone who does not have the command line at their beck and call is really missing something," tweeted Tim O'Reilly when Jeroen Janssens's Data Science at the Command Line was recently made available online for free. Join Jeroen to learn what you're missing out on if you're not applying the command line and many of its power tools to typical data science problems.

In this panel discussion, venture capital investors explain how startups can accelerate enterprise adoption of machine learning and explore the new tech trends that will give rise to the next transformation in the big data landscape.

Bethann Noble, Abhishek Kodi, and Daniel Huss share their experience and best practices for designing and executing on a roadmap for open data science and AI for business.

Time series data has many applications in industry, from analyzing server metrics to monitoring IoT signals and outlier detection. Mikio Braun offers an overview of time series analysis with a focus on modern machine learning approaches and practical considerations, including recommendations for what works and what doesn’t, and industry use cases.

State-of-the-art recommendation algorithms are increasingly complex and no longer one size fits all. Current monolithic development practice poses significant challenges to rapid, iterative, and systematic, experimentation. Longqi Yang explains how to use OpenRec to easily customize state-of-the-art solutions for diverse scenarios.

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.

Precipitation nowcasting is used to predict the future rainfall intensity over a relatively short timeframe. The forecasting resolution and time accuracy required are much higher than for other traditional forecasting tasks. Alexander Heye and Ding Ding explain how to build a precipitation nowcasting system with recurrent neural networks using BigDL on Apache Spark.

Large online fashion retailers must efficiently maintain catalogues of millions of items. Due to human error, it's not unusual that some items have duplicate entries. Since manually trawling such a large catalogue is next to impossible, how can you find these entries? Patty Ryan, CY Yam, and Elena Terenzi explain how they applied deep learning for image segmentation and background removal.

GPUs have allowed financial firms to accelerate their computationally demanding workloads. Today, the bottleneck has moved completely to ETL. The GPU Open Analytics Initiative (GoAi) is helping accelerate ETL while keeping the entire workflow on GPUs. Joshua Patterson and Onur Yilmaz discuss several GPU-accelerated data science tools and libraries.

Did you know that your Uber rides are powered by Apache Spark? Join Felix Cheung to learn how Uber is building its data platform with Apache Spark at enormous scale and discover the unique challenges the company faced and overcame.

In the last few years, Netflix's data warehouse has grown to more than 100 PB in S3. Ryan Blue and Daniel Weeks share lessons learned, the tools Netflix currently uses and those it has retired, and the improvements it is rolling out, including Iceberg, a new table format for S3.

Recommender systems enhance user experience and business revenue every day. Sophie Watson demonstrates how to develop a robust recommendation engine using a microservice architecture.

Cloud object stores are becoming the bedrock of cloud data warehouses for modern data-driven enterprises, and it's become a necessity for data teams to have the ability to directly query data stored in S3 or ADLS. Greg Rahn and Mostafa Mokhtar discuss optimal end-to-end workflows and technical considerations for using Apache Impala over object stores for your cloud data warehouse.

Jacques Nadeau leads a deep dive into a new Apache-licensed lightweight distributed in-memory cache that allows multiple applications to consume Arrow directly using the Arrow RPC and IPC protocols. You'll explore the system design and deployment architecture—including the cache life cycle, update patterns, cache cohesion, and appropriate use cases—learn how it all works, and see it in action.

Big data infrastructure has evolved from flat files in a distributed filesystem to an efficient ecosystem to a fully deconstructed and open source database with reusable components. Julien Le Dem discusses the key open source components of the big data ecosystem and explains how they relate to each other and how they make the ecosystem more of a database and less of a filesystem.

Before releasing a public dataset, practitioners need to thread the needle between utility and protection of individuals. Felipe Hoffa and Damien Desfontaines explore how to handle massive public datasets, taking you from theory to real life as they showcase newly available tools that help with PII detection and brings concepts like k-anonymity and l-diversity to the practical realm.

Minh Chau Nguyen and Heesun Won explain how to implement analytics services in data marketplace systems on a single Hadoop cluster across distributed data centers. The solution extends the overall architecture of the Hadoop ecosystem with the blockchain so that multiple tenants and authorized third parties can securely access data while still maintaining privacy, scalability, and reliability.

Jim Scott details relevant use cases for blockchain-based solutions across a variety of industries, focusing on a suggested architecture to achieve high-transaction-rate private blockchains and decentralized applications backed by a blockchain. Along the way, Jim compares public and private blockchain architectures.

Zipline is Airbnb’s soon to be open-sourced data management platform specifically designed for ML use cases. It has taken the task of feature generation from months to days and offers features to support end-to-end data management for machine learning. Varant Zanoyan covers Zipline's architecture and dives into how it solves ML-specific problems.

Mist consumes several terabytes of telemetry data daily from its globally deployed wireless access points, a significant portion of which is consumed by ML algorithms. Last year, Mist saw 10x infrastructure growth. Osman Sarood explains how Mist runs 75% of its production infrastructure, reliably, on AWS EC2 spot instances, which has brought its annual AWS cost from $3 million to $1 million.

Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. Jay Kreps explores some of the difficulties of building production machine learning systems and explains how Apache Kafka and stream processing can help.

Gwen Shapira shares design and architecture patterns that are used to modernize data engineering. You'll learn how modern engineering organizations use Apache Kafka, microservices, and event streams to efficiently build data pipelines that are scalable, reliable, and built to evolve.

What's easier than building a data pipeline? You add a few Apache Kafka clusters and a way to ingest data, design a way to route your data streams, add a few stream processors and consumers, integrate with a data warehouse. . .wait, this looks like a lot of things. Join Yaroslav Tkachenko to learn best practices for building a data pipeline, drawn from his experience at Demonware/Activision.

Danny Chen, Omkar Joshi, and Eric Sayle offer an overview of Marmaray, a generic Hadoop ingestion and dispersal framework recently released to production at Uber. You'll learn how Marmaray can meet a team's data needs by ensuring that data can be reliably ingested into Hive or dispersed into online data stores and take a deep dive into the architecture to see how it all works.

Mauricio Aristizabal shares lessons learned from migrating Impact's traditional ETL platform to a real-time platform on Hadoop (leveraging the full Cloudera EDH stack). Mauricio also discusses the company's data lake in HBase, Spark Streaming jobs (with Spark SQL), using Kudu for "fast data" BI queries, and using Kafka's data bus for loose coupling between components.

Neelesh Srinivas Salian explains how Stitch Fix built a service to better understand the movement and evolution of data within the company's data warehouse, from the initial ingestion from outside sources through all of its ETLs. Neelesh covers why and how Stitch Fix built the service and details some use cases.

Do your analysts always trust the insights generated by your data platform? Ensuring insights are always reliable is critical for use cases in the financial sector. Sandeep Uttamchandani outlines a circuit breaker pattern developed for data pipelines, similar to the common design pattern used in service architectures, that detects and corrects problems and ensures always reliable insights.

Apache Spark has two streaming APIs: Spark Streaming and Structured Streaming. Gerard Maas offers a critical overview of their differences with regard to key aspects of a streaming application: API usability, dealing with time, dealing with state and machine learning capabilities, and more. You'll learn when to pick one over the other or combine both to implement resilient streaming pipelines.

Fabian Hueske discusses why SQL is a great approach to unify batch and stream processing. He gives an update on Apache Flink's SQL support and shares some interesting use cases from large-scale production deployments. Finally, Fabian presents Flink's new query service that enables users and applications to submit streaming and batch SQL queries and retrieve low-latency updated results.

Streaming systems like Apache Heron are being used for an increasingly broad array of applications. Karthik Ramasamy and Andrew Jorgensen offer an overview of Fabric Answers, which provides real-time insights to mobile developers to improve their product experience at Google Fabric using Apache Heron.

Streaming big data is a rapidly growing field but currently involves a lot of operational complexity and expertise. Bill Chambers shares a decision making framework for determining the best tools and technologies for successfully deploying and maintaining streaming data pipelines to solve business problems and offers an overview of Apache Spark’s Structured Streaming processing engine.

Automating the success of digital ad campaigns is complicated and comes with the risk of wasting the advertiser's budget or a trader's margin and time. Brian Wu describes the evolution of Inventory Discovery, a streaming control system of eligibility, prioritization, and real-time evaluation that helps digital advertisers hit their performance goals with AppNexus.

Uber has a real need to provide faster, fresher data to its data consumers and products, which are running hundreds of thousands of analytical queries every day. Nishith Agarwal, Balaji Varadarajan, and Vinoth Chandar share the design, architecture, and use cases of the second-generation of Hudi, an analytical storage engine designed to serve such needs and beyond.

Cory Minton and Colm Moynihan explain how to choose the right deployment model for on-premises infrastructure to reduce risk, reduce costs, and be more nimble.

Data governance has grown from a set of mostly data management-oriented technologies in the data warehouse era to encompass catalogs, glossaries, and more in the data lake era. Now new requirements are emerging, and new products are rising to meet the challenge. Andrew Brust tracks data governance's past and present and offers a glimpse of the future.

Kubernetes (K8s)—the open source container orchestration system for modern big data workloads—is increasingly popular. While the promised land is a unified platform for cloud-native stateless and stateful data services, stateful, multiservice big data cluster orchestration brings unique challenges. Anant Chintamaneni and Nanda Vijaydev outline the considerations for big data services for K8s.

Once the data has been captured, how can the cloud, containers, and a data fabric combine to build the infrastructure to provide the business insights? Paul Curtis explores three customer deployments that leverage the best of the private clouds and containers to provide a flexible big data environment.

The focus on the IoT is turning increasingly to the edge, and the way to make the edge more intelligent is by building machine learning models in the cloud and pushing them back out to the edge. Dave Shuman and Bryan Dean explain how Cloudera and Red Hat executed this architecture at one of Europe's leading manufacturers, along with a demo highlighting this architecture.

Owen O'Malley and Ryan Blue offer an overview of Iceberg, a new open source project that defines a new table layout with properties specifically designed for cloud object stores, such as S3. It provides a common set of capabilities such as partition pruning, schema evolution, atomic additions, removal, or replacements of files regardless of whether the data is stored in Avro, ORC, or Parquet.

JF Gagne explains why the CIO is going to need a broader mandate in the company to better align their AI training and outcomes with business goals and compliance. This mandate should include an AI governance team that is well staffed and deeply established in the company, in order to catch biases that can develop from faulty goals or flawed data.

Airbnb has open-sourced many high-leverage data tools, including Airflow, Superset, and the Knowledge Repo, but adoption of these tools across the company was relatively low. Erin Coffman offers an overview of Data University, launched to make data more accessible and utilized in decision making at Airbnb.

Firms are struggling to leverage their data. Lawrence Cowan outlines a methodology for assessing four critical areas that firms must consider when looking to make the analytical leap: data strategy, data culture, data analysis and implementation, and data management and architecture.

Friederike Schuur and Rita Ko explain how the Hive (an internal group at USA for UNHCR) and Cloudera Fast Forward Labs transformed USA for UNHCR, enabling the agency to use data science and machine learning (DS/ML) to address the refugee crisis. Along the way, they cover the development and implementation of a DS/ML strategy, identify use cases and success metrics, and showcase the value of DS/ML.

Many products, whether data driven or not, chase “the one metric that matters.” It may be engagement, revenue, or conversion, but the common theme is the pursuit of improvement in one metric. Product development teams should instead focus on the design of metrics that measure our goals. Adil Aijaz shares an approach to designing metrics and discusses best practices and common pitfalls.

After big investments in collecting and cleaning data and building machine learning (ML) models, enterprises face big challenges in deploying models to production and managing a growing portfolio of ML models. Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.

Agile methodologies have been widely successful for software engineering teams but seem inappropriate for data science teams, because data science is part engineering, part research. Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient.

Privacy by design is a fundamentally important approach to achieving compliance with GDPR and other data privacy or data protection regulations. Les McMonagle outlines how organizations can save time and money while improving data security and regulatory compliance and dramatically reduce the risk of a data breach or expensive penalties for noncompliance.

What is the moral responsibility of a data team today? As AI and machine learning technologies become part of our everyday life and as data becomes accessible to everyone, CDOs and data teams are taking on a very important moral role as the conscience of the corporation. Harry Glaser highlights the risks companies will face if they don't empower data teams to lead the way for ethical data use.

Machine learning is becoming prevalent across industries, creating new types of risk. Managing this risk is quickly becoming the central challenge of major organizations, one that strains data science teams, legal personnel, and the C-suite alike. Andrew Burt shares lessons from past regulations focused on similar technology along with a proposal for new ways to manage risk in ML.

Too often, the discussion of AI and ML includes an expectation—if not a requirement—for infallibility. But as we know, this expectation is not realistic. So what’s a company to do? While risk can’t be eliminated, it can be rationalized. Kimberly Nevala demonstrates how an unflinching risk assessment enables AI/ML adoption and deployment.

Forget about the fake news; data and analytics in politics is what drives elections. John Thuma shares ethical dilemmas he faced while proposing analytical solutions to the RNC and DNC. Not only did he help causes he disagreed with, but he also armed politicians with real-time data to manipulate voters.

In May 2018, the General Data Protection Regulation (GDPR) went into effect for firms doing business in the EU, but many companies still aren't prepared for the strict regulation or fines for noncompliance (up to €20 million or 4% of global annual revenue). Mark Donsky and Steven Ross outline the capabilities your data environment needs to simplify compliance with GDPR and future regulations.

Tony Baer and Florian Douetteau share the results of research cosponsored by Ovum and Dataiku that surveyed a specially selected sample of chief data officers and data scientists on how to map roles and processes to make success with AI in the business repeatable.

Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.

Creating a successful big data practice in your organization presents new challenges in managing projects and teams. Ted Malaska and Jonathan Seidman share guidance and best practices to help technical leaders deliver successful projects from planning to implementation.

Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness, and hiring experts doesn’t seem to help. Cassie Kozyrkov examines what it takes to build a truly data-driven organizational culture and highlights a vital yet often neglected job function: the data science manager.

If the last few years were spent proving the value of data lakes, the emphasis now is to monetize the big data architecture investments. The rallying cry is to onboard new workloads efficiently. But how do you do so if you don’t know what data is in the lake, the level of its quality, or the trustworthiness of models? Sanjeev Mohan explains why data governance is the linchpin to success.

Usama Fayyad and Troels Oerting share outcomes and lessons learned from building and deploying a global data fusion, incident analysis/visualization, and effective cybersecurity defense based on big data and AI at a major EU bank, in collaboration with several financial services institutions.

The largest infrastructure paradigm change of the 21st century is the shift to the cloud. Companies now face the difficult decision of which cloud to go with. This decision is not just financial and in many cases rests on the underlying infrastructure. Jason Wang, Suraj Acharya, and Tony Wu compare the relative strengths and weaknesses of AWS and Azure.

Successfully building and deploying a machine learning model is difficult to do once. Enabling other data scientists to reproduce your pipeline, compare the results of different versions, track what's running where, and redeploy and rollback updated models is much harder. Mani Parkhe and Andrew Chen offer an overview of MLflow—a new open source project from Databricks that simplifies this process.

Michael Freedman explains how to leverage Postgres for high-volume time series workloads using TimescaleDB, an open source time series database built as a Postgres plug-in. Michael covers the general architectural design principles and new time series data management features, including adaptive time partitioning and near-real-time continuous aggregations.

Dan Harple explains how distributed systems are being influenced by and are influencing operational, financial, and social impact requirements of a wide range of enterprises and how trust in these distributed systems is being challenged, elevated, and resolved by engineers and architects today.

Mike Tung offers an overview of available open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of them to provide intelligent experiences and enhanced business efficiency. Mike then discusses what's coming in the future.

Make your way from booth to booth while you check out all the exhibitors in the Expo Hall on Wednesday after sessions end.

Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects.

While every enterprise is on a mission to infuse its business with deep learning, few know how to build the infrastructure to get them there. Darrin Johnson shares insights and best practices learned from NVIDIA's deep learning deployments around the globe that you can leverage to shorten deployment timeframes, improve developer productivity, and streamline operations.

Michael Balint explains how NVIDIA employs its own distribution of Kubernetes, in conjunction with DGX hardware, to make the most efficient use of GPU resources and scale its efforts across a cluster, allowing multiple users to run experiments and push their finished work to production.

Explore case studies from Datalogue, FASTDATA.io, and H20.ai that demonstrate how GPU-accelerated analytics, machine learning, and ETL help companies overcome slow queries and tedious data preparation process, dynamically correlate among data, and enjoy automatic feature engineering.

Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.

Tim Davis discusses key pain points and solutions to problems many enterprises face with data in silos, poor-quality data that cannot always be trusted, and managing and making large volumes of data available to derive more accurate insights and machine learning models.

When China Construction Bank wanted to migrate 23,000+ reports to mobile, it chose Apache Kylin as the high-performance and high-concurrency platform to refactor its data warehouse architecture to serving 400K+ users. Zhi Zhu and Luke Han detail the necessary architecture and best practices for refactoring a data warehouse for mobile analytics.

Intelligent enterprises—fueled by rapid advances in artificial intelligence (AI), machine learning (ML), and the internet of things (IoT)—promise significant business value. Richard Mooney explains how to achieve the game-changing outcomes of an intelligent enterprise, delivering value across business functions with the synergy of machine and human intelligence.

Eagle Investment Systems, a leading provider of financial services technology, is building a new Hadoop and cloud-based data management solution. Chris Stirrat explains how Eagle went from incubation to an enterprise-scale solution in just 10 months, using a Hadoop-based big data stack and multitenant architecture, transforming software creation, delivery, quality, technology, and culture.

Every organization is considering its storage options, with an eye toward the cloud. Paul Scott-Murphy explores what makes different large-scale storage systems and services unique, their clear (and unexpected) differences, the options you have to use them, and the surprises you can expect along the way.

Ben Szekely shares a vision for digital innovation: The data fabric connects enterprise data for unprecedented access in an overlay fashion that does not disrupt current investments. Interconnected and reliable data drives business outcomes by automating scalable AI and ML efforts. Graph technology is the way forward to realize this future.

Drawing on his experience working with customers across many industries, including chemical sciences, healthcare, and oil and gas, Jim Scott details the major impediments to successful completion of deep learning projects and solutions while walking you through a customer use case.

In the past, there have been major challenges in quickly creating machine learning training environments and deploying trained models into production. Skyler Thomas details how Kubernetes helps data scientists and IT work in concert to speed model training and time-to-value.

Anand Raman and Wee Hyong Tok walk you through applying AI technologies in the cloud. You'll learn how to add prebuilt AI capabilities like object detection, face understanding, translation, and speech to applications, build cognitive search applications that understand deep content in images, text, and other data, use the Azure platform to accelerate machine learning, and more.

Bell Canada, Canada's largest communications company, leads the industry in providing world-class broadband communications services to consumers and business customers. Join Sara Alavi to learn how the network big data and AI team within Bell is using modern data environments and applying a startup mindset to transform traditional networks into insight-driven intelligent networks.

TD Bank’s data analytics team has undertaken a multiyear journey to modernize its data infrastructure for today and future needs. Joseph DosSantos explains how the team built a governed data lake foundation, enabling business users to leverage its big data environment to extract analytical insights while minimizing risks.

Pure Storage runs over 70,000 tests per day. Using Spark’s flexible computing platform, the company can write a single application for both streaming and batch jobs so the company's team of triage engineers can understand the state of the continuous integration pipeline. Ivan Jibaja discusses the use case for big data analytics technologies, the architecture of the solution, and lessons learned.

Data is the lifeblood of an enterprise, and it's being generated everywhere. To overcome the challenges of data gravity, data analytics, including machine learning, is best done where the data is located: ubiquitous machine learning. Han Yang explains how to overcome the challenges of machine learning everywhere.

SparklineData is an in-memory distributed scale-out analytics platform built on Apache Spark to enable enterprises to query on data lakes directly with instant response times. Srikanth Desikan offers an overview of SparklineData and explains how it can enable new analytics use cases working on the most granular data directly on data lakes.

Chris Wojdak explains how Symcor has transformed its big data architecture using Informatica’s comprehensive machine learning-based solutions for data integration, data quality, data cataloging, and data governance.

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.

As the data authority for hybrid cloud for big data analytics and AI, NetApp understands the value of the access, management, and control of data. Karthikeyan Nagalingam discusses the NetApp Data Fabric, which provides a unified data management environment that spans edge devices, data centers, and multiple hyperscale clouds using ONTAP software, all-flash systems, ONTAP Select, and cloud volumes.

The role of data and the demand to get it right, coupled with competitive pressures to move faster, have dramatically increased. Companies now recognize data as an asset and need to manage it that way. Join Dan Adams for the insights you need to ensure that your data addresses current and future needs and that your organization is set up for success.

Peet Smith explains how PwC is using modern database tools with a combination of open source technologies to automate and scale data ingestion and transformation to get data to engagement teams to help them streamline and accelerate client service delivery.

Persistent memory accelerates analytics, database, and storage workloads across a variety of use cases, bringing new levels of speed and efficiency to the data center and to in-memory computing. Arakere Ramesh and Bharath Yadla offer an overview of the newly announced Intel Optane data center persistent memory and share the exciting potential of this technology in analytics solutions.

Selwyn Collaco and Ben Sharma share insights from their real-world experience and discuss best practices for architecture, technology, data management, and governance to enable centralized data services and explain how to leverage the Zaloni Data Platform (ZDP), an integrated self-service data platform, to operationalize the enterprise data lake .

Michael Mahoney demonstrates how to leverage the power of GPUs to converge streaming data analysis, location analysis, and streamlined machine learning with a single engine. Along the way, Michael shares real-world case studies on how Kinetica is used to solve complex data challenges.

Is a single source of truth across the enterprise possible, or is it just an expensive myth? Anand Raman explains why you need a holistic decision framework that addresses multiple facets from platform to processes. Join in to explore EDW modernization strategies, self-service analytics, and interactive insights on big data and discover a process to get to a unified data model.

Like all telecommunication giants, Bell Canada relies on huge volumes of data to make accurate business decisions and deliver better services. Mark Huang discusses why Bell Canada chose Kyvos’s OLAP on big data technology to achieve multidimensional analytics and how it helped the company deliver to its growing business reporting demands.

Software is eating the world, and open source is eating the software. Most contemporary analytics shops use a lot of open source software in their analytics platform. So where does commercial software like SAS fit? Paul Kent explains how you can achieve the best of both worlds by combining your favorite open source software with the power of SAS analytics.

Interested in how Ebates is using a hybrid on-premises and cloud implementation to scale out its centralized business intelligence and data hub? Mark Stange-Tregear shares the history, business context, and technical plan around Ebates’s hybrid Hadoop-AWS cloud approach.

The use of data lakes continue to grow, and the right business intelligence (BI) and analytics tools on data lakes are critical to data lake success. Randy Lea explains why existing BI tools work well for data warehouses but not data lakes and why every organization should have two BI standards: one for data warehouses and one for data lakes.

Basil Faruqui demonstrates how to simplify the automation and orchestration of an IoT-driven data pipeline in a cloud environment where machine learning algorithms predict failures.

Bruno Faria explains how to identify the components and workflows in your current environment and shares best practices to migrate these workloads to AWS.

The use of data lakes continue to grow, and the right business intelligence (BI) and analytics tools on data lakes are critical to data lake success. Randy Lea explains why existing BI tools work well for data warehouses but not data lakes and why every organization should have two BI standards: one for data warehouses and one for data lakes.

Program chairs Ben Lorica, Doug Cutting, and Alistair Croll welcome you to the first day of keynotes.

Data volumes don’t translate to business value. What matters is your data platform’s ability to support unprecedented numbers of business users and use cases. Anupam Singh and Brian Coyne look at some of the challenges posed by data-hungry organizations and share new techniques to extract meaningful insights at the speed of today’s modern business.

As companies begin adopting machine learning, important considerations, including fairness, transparency, privacy, and security, need to be accounted for. Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.

There’s real value in big data and more waiting when you add real-time, but to get the payoff, you need successful deployments of your AI and data-intensive applications. You need to be ready with your current applications in production but must have an architecture and infrastructure that are ready for the next ones as well. Ted Dunning explores how others have fared in this journey.

Jeffrey Wecker leads a deep dive on data in financial services, with perspectives on the evolving landscape of data science, the advent of alternative data, the importance of data centricity, and the future for machine learning and AI.

DD Dasgupta explores the exciting development of the edge-cloud continuum, which is redefining business models and technology strategies while creating a vast array of new applications that will power the digital age. The continuum is also destroying what we know about the centralized data centers and cloud computing infrastructures that were so vital to the success of the previous computing eras.

Why do businesses fail at machine learning despite its tremendous potential and the excitement it generates? Is the answer always in data, algorithms, and infrastructure, or is there a subtler problem? Will things improve in the near future? Let's talk about some lessons learned at Google and what they mean for applied data science.

Today’s successful businesses utilize data better than their competitors; however, data sprawl and inefficient data infrastructure restrict what’s possible. Blending the best of the past with the software innovations of today will solve future data challenges. Drew Paroski shares how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.

Ethereum is a world computer on top of a peer-to-peer network that runs smart contracts - applications that run exactly as programmed without the possibility of censorship, fraud, or third-party interference. Until now, businesses had to build their systems on database technologies that resulted in siloed and redundant information in typically adversarial contexts.

Gather before keynotes on Wednesday morning to enjoy casual conversation while meeting fellow attendees. If one of your goals at Strata is to meet new people, this session will jumpstart your networking with other attendees.

Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.

Join fellow executives, business leaders, and strategists for a networking lunch on Wednesday for Strata Business Summit attendees and speakers.

If you’re looking to find like minds and make new professional connections, come to the women's networking lunch on Wednesday.

Don't miss an exciting evening filled with cocktails, food, and entertainment at Data After Dark at Strata in New York.