Extracting value from data goes beyond robust ingestion pipelines and flexible storage. A solid data architecture addresses the needs of analysts and engineers by providing a simple, self-service ecosystem capable of handling any workload. Alex Silva discusses how the platform team at Pluralsight has been conquering these challenges by designing a platform using distributed, reactive services.

Most already know HBase, but many don't know that it can be coupled with other tools from the ecosystem to increase efficiency. Jean-Marc Spaggiari and Kevin O'Dell walk attendees through some real-life HBase use cases and demonstrate how they have been efficiently implemented.

There are (too?) many options for BI on Hadoop. Some are great at exploration, some are great at OLAP, some are fast, and some are flexible. Understanding the options and how they work with Hadoop systems is a key challenge for many organizations. Jacques Nadeau provides a survey of the main options, both traditional (Tableau, Qlik, etc.) and new (Platfora, Datameer, etc.).

Software-defined networking (SDN) and network functions virtualization (NFV) hold tremendous potential to enable efficiency and flexibility in service delivery, but SDN/NFV environments are also highly complex and multilayered. Matt Olson explains why effective support for SDN/NFV services requires leveraging the tremendous amount of service and data streaming from the platform.

Jayant Shekhar, Amandeep Khurana, Krishna Sankar, and Vartika Singh guide participants through techniques for building machine-learning apps using Spark MLlib and Spark ML and demonstrate the principles of graph processing with Spark GraphX.

Organizations do not need a big data strategy. They need a business strategy that incorporates big data. Most organizations lack a roadmap for using big data to uncover new business opportunities. Bill Schmarzo explains how to explore, justify, and plan big data projects with business management.

Ted Malaska and Jeff Holoman explain how to go from zero to full-on time series and mutable-profile systems in 40 minutes. Ted and Jeff cover code examples of ingestion from Kafka and Spark Streaming and access through SQL, Spark, and Spark SQL to explore the underlying theories and design patterns that will be common for most solutions with Kudu.

Thomas Phelan and Joel Baxter investigate the advantages and disadvantages of running specific Hadoop workloads in different infrastructure environments. Thomas and Joel then provide a set of rules to help users evaluate big data runtime environments and deployment options to determine which is best suited for a given application.

Traditional data-warehousing techniques are sometimes limited by the scalability of the implementation tools themselves. Arun Thangamani explains how the advanced architectural approaches by tools like Apache Phoenix and HBase allow new, highly scalable live-analytics solutions using the same traditional techniques and showcases a successful implementation at CDK.

Moty Fania shares Intels IT experience implementing an on-premises big data IoT platform for internal use cases. This unique platform was built on top of several open source technologies and enables highly scalable stream analytics with a stack of algorithms such as multisensor change detection, anomaly detection, and more.

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.

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.

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.

MQTT is a standards-based, lightweight pub/sub messaging protocol ideal for Internet of Things use cases. With implementations in a number of different languages, using MQTT in your applications is simple and straightforward. Ryan Baxter shows how to build an application that connects to an MQTT broker and can receive data fromand issue commands toa number of different IoT devices.

The new HTTP/2 protocol is mostly focused on web performance, but what kind of performance improvement can you expect by switching to HTTP/2? Ragnar Lnn explains the key improvements within HTTP/2 and demos a new application developers can use to see how their applications will function on the new protocol.

As JavaScript has evolved over the past several years, so have the patterns for handling its asynchronous nature. Through callbacks, Promises, and now async functions, JavaScript has made asynchronous control flow easier to tackle. Jeremy Fairbank dives into the new async functions landing in ES7 and investigates various use cases and gotchas along the way.