Strata Data Conference

A brave new world in mutable big data: Relational storage

January 6, 2020

To date, mutable big data storage has primarily been the domain of nonrelational (NoSQL) systems such as Apache HBase. However, demand for real-time analytic architectures has led big data back to a familiar friend: relationally structured data storage systems. Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu.

A comprehensive, enterprise-grade, open Hadoop solution from Hewlett Packard Enterprise (sponsored by Hewlett Packard Enterprise)

January 5, 2020

Bob Patterson offers an overview of Hewlett Packard Enterprise's enterprise-grade Hadoop solution, which has everything you need to accelerate your big data journey: innovative hardware architectures for diverse workloads certified for all leading distros, infrastructure software, services from HPE and partners, and add-ons like object storage.

A practitioners guide to Hadoop security for the hybrid cloud

January 5, 2020

Mark Donsky, Andr Araujo, Syed Rafice, and Manish Ahluwalia walk you through securing a Hadoop cluster. Youll start with a cluster with no security and then add security features related to authentication, authorization, encryption of data at rest, encryption of data in transit, and complete data governance.

A whole new way to think about your next-gen applications (sponsored by MapR Technologies)

January 5, 2020

Businesses struggle to build applications that harness all their data. RDBMS cannot handle modern data-intensive workloads, and NoSQL doesn't provide the capabilities for diverse applications. Anil Gadre explains how customers using a converged data platform are succeeding at creating breakthrough new apps for the enterprise.

An authenticated journey through big data security at Walmart

January 5, 2020

In todays world of data breaches and hackers, security is one of the most important components for big data systems, but unfortunately, it's usually the area least planned and architected. Matt Bolte and Toni LeTempt share Walmart's authentication journey, focusing on how decisions made early can have significant impact throughout the maturation of your big data environment.

An open source architecture for the IoT

January 5, 2020

Eclipse IoT is an ecosystem of organizations that are working together to establish an IoT architecture based on open source technologies and standards. Dave Shuman and James Kirkland showcase an end-to-end architecture for the IoT based on open source standards, highlighting Eclipse Kura, an open source stack for gateways and the edge, and Eclipse Kapua, an open source IoT cloud platform.

Analytics everywhere, from things to cities (sponsored by Cisco)

January 5, 2020

Endless possibilities when we connect the unconnected. Raghunath Nambiar discusses the magnitude of new challenges and new opportunities across industry segments.

Apache Spark in the hands of data scientists

January 5, 2020

Neelesh Srinivas Salian offers an overview of the data platform used by data scientists at Stitch Fix, based on the Spark ecosystem. Neelesh explains the development process and shares some lessons learned along the way.

Architecting a next-generation data platform

January 5, 2020

Using Customer 360 and the IoT as examples, Jonathan Seidman, Mark Grover, and Gwen Shapira explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Impala, Kudu, Spark Streaming, and Spark SQL with Hadoop to enable new forms of data processing and analytics.

Automating cloud cluster deployment: Beyond the book

January 5, 2020

Speed and reliability in deploying big data clusters is key for effectiveness in the cloud. Drawing on ideas from his book Moving Hadoop to the Cloud, which covers essential practices like baking images and automating cluster configuration, Bill Havanki explains how you can automate the creation of new clusters from scratch and use metrics gathered using the cloud provider to scale up.

Big data analysis of futures trades

January 5, 2020

Whether an entity seeks to create trading algorithms or mitigate risk, predicting trade volume is an important task. Focusing on futures trading that relies on Apache Spark for processing the large amount data, Tobi Bosede considers the use of penalized regression splines for trade volume prediction and the relationship between price volatility and trade volume.

Building big data applications on Azure

January 4, 2020

As big data solutions are rapidly moving to the cloud, it's becoming increasingly important to know how to use Apache Hadoop, Spark, R Server, and other open source technologies in the cloud. Pranav Rastogi walks you through building big data applications on Azure HDInsight and other Azure services.

Building enterprise OLAP on Hadoop in finance with Apache Kylin (sponsored by Kyligence)

January 4, 2020

Luke Han offers an overview of Apache Kylin and its enterprise version KAP and shares a case study of how a top finance company migrated to Apache Kylin on top of Hadoop from its legacy Cognos and DB2 system.

Building the IoT data lifecycle (sponsored by Cisco)

January 4, 2020

For many enterprises, the internet of things represents an opportunity to transform the business by examining its data from a holistic lifecycle perspective and generating, analyzing, and archiving the data to reengineer the enterprise. Han Yang explores the latest trends and the role of infrastructure in enabling such a transformation.

Classification of telecom network traffic: Insight gained using statistical learning on a big data platform

January 4, 2020

Statistical learning techniques applied to network data provide a comprehensive view of traffic behavior that would not be possible using traditional descriptive statistics alone. Amie Elcan shares an application of the random forest classification method using network data queried from a big data platform and demonstrates how to interpret the model output and the value of the data insight.

Cloud data lakes: Analytic data warehouses in the cloud

January 4, 2020

John Hitchingham shares insights into the design and operation of FINRA's data lake in the AWS cloud, where FINRA extracts, transforms, and loads over 75B transactions per day. Users can query across petabytes of data in seconds on AWS S3 using Presto and Sparkall while maintaining security and data lineage.

Considerations for hardware-accelerated machine learning platforms

January 4, 2020

The advances we see in machine learning would be impossible without hardware improvements, but building a high-performance hardware platform is tricky. It involves hardware choices, an understanding of software frameworks and algorithms, and how they interact. Mike Pittaro shares the secrets of matching the right hardware and tools to the right algorithms for optimal performance.

Creating public value through data collaboratives

January 4, 2020

The data collaborative is a new form of public-private partnership that seeks to create public value for the worlds most marginalized children through the exchange of data and data science expertise. Natalia Adler offers an overview of the Data Collaboratives initiative, led by UNICEF and the GovLab at New York University's Tandon School of Engineering.

Data science for the most vulnerable at UNICEF Innovation

January 4, 2020

The growing availability of dataalong with advances in fields such as data science and artificial intelligencehas profoundly changed businesses. Manuel Garca-Herranz explains how to leverage these advances for the most vulnerable, while making sure that the existing data divide does not increase the gap in inequality, and integrate these advances into the humanitarian and development systems.

Deep learning for recommender systems

January 4, 2020

In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.

Deploying AI in mobile-first consumer-facing financial products: A tale of two cycles

January 4, 2020

AI is moving into the heart of the financial business model. Jike Chong discusses two fundamental business cycles in a financial institution: acquiring customers and sustaining customer relationships, highlighting opportunities in six areas where AI technologies can be readily deployed, along with reference use cases.

Empowering quants to trade faster: From Excel files to data packages

January 3, 2020

It doesn't matter how much data organizations collect; all that matters is how much data they can leverage. Aneesh Karve explores how a Fortune 500 bank leverages data packages to minimize data prep and maximize time spent on analysisusing a technique called source code-inspired data management.

Executive Briefing: Analytics centers of excellence as a way to accelerate big data adoption by business

January 3, 2020

Big data technology is mature, but its adoption by business is slow, due in part to challenges like a lack of resources and the need for a cultural change. Carme Artigas explains why an analytics center of excellence (ACoE), whether internal or outsourced, is an effective way to accelerate adoption and shares an approach to implementing an ACoE.

Expanding data literacy with data visualizations

January 3, 2020

While the value of data and its role in informing decisions and communications is well known, its meaning can be incorrectly interpreted without data visualizations that provide context and accurate representation of the underlying numbers. Julie Rodriguez shares new approaches and visual design methods that provide a greater perspective of the data.

From notebooks to cloud native: A modern path for data-driven applications

January 3, 2020

Notebook interfaces like Apache Zeppelin and Project Jupyter are excellent starting points for sketching out ideas and exploring data-driven algorithms, but where does the process lead after the notebook work has been completed? Michael McCune offers some answers as they relate to cloud-native platforms.

GDPR: Getting your data ready for heavy, new EU privacy regulations

January 3, 2020

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

Geospatial big data analysis at Uber

January 3, 2020

Uber's geospatial data is increasing exponentially as the company grows. As a result, its big data systems must also grow in scalability, reliability, and performance to support business decisions, user recommendations, and experiments for geospatial data. Zhenxiao Luo and Wei Yan explain how Uber runs geospatial analysis efficiently in its big data systems, including Hadoop, Hive, and Presto.

GPU-accelerating a deep learning anomaly detection platform

January 3, 2020

How can deep learning be employed to create a system that monitors network traffic, operations data, and system logs to reliably flag risk and unearth potential threats? Satish Dandu, Joshua Patterson, and Michael Balint explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools.