Strata Data Conference

Automated data exploration: Building efficient analysis pipelines with dask

December 5, 2019

Data exploration usually entails making endless one-use exploratory plots. Victor Zabalza shares a Python package based on dask execution graphs and interactive visualization in Jupyter widgets built to overcome this drudge work. Victor offers an overview of the tool and explains how it was built and why it will become essential in the first steps of every data science project.

Big data computations: Comparing Apache HAWQ, Druid, and GPU databases

December 5, 2019

The class of big data computations known as distributed merge trees was built to aggregate user information across multiple data sources in the media domain. Vijay Srinivas Agneeswaran explores prototypes built on top of Apache HAWQ, Druid, and Kinetica, one of the open source GPU databases. Results show that Kinetica on a single G2.8x node outperformed clusters of HAWQ and Druid nodes.

Big data governance for the hybrid cloud: Best practices and how-to

December 5, 2019

Big data needs governancenot just for compliance but also for data scientists. Governance empowers data scientists to find, trust, and use data on their own, yet it can be overwhelming to know where to start, especially if your big data environment spans beyond your enterprise to the cloud. Mark Donsky and Vikas Singh share a step-by-step approach to kickstart your big data governance.

Big data science, the IoT, and the transportation sector

December 5, 2019

Wael Elrifai leads a journey through the design and implementation of a predictive maintenance platform for Hitachi Rail. The industrial internet, the IoT, data science, and big data make for an exciting ride.

Build big data enterprise solutions faster on Azure HDInsight

December 5, 2019

Pranav Rastogi explains how to simplify your big data solutions with Datameer, AtScale, Dataiku, and StreamSets on Microsofts Azure HDInsight, a cloud Spark and Hadoop service for the enterprise. Join in to learn practical information that will enable faster time to insights for you and your business.

Building a modern data architecture for scale

December 4, 2019

When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma offers lessons learned from the field to get you started.

Building containerized Spark on a solid foundation with Quobyte and Kubernetes

December 4, 2019

Multiple challenges arise if distributed applications are provisioned in a containerized environment. Daniel Burer and Sascha Askani share a solution for distributed storage in cloud-native environments using Spark on Kubernetes.

Building deep learning-powered big data

December 4, 2019

Radhika Rangarajan explains how Intel works with its users to build deep learning-powered big data analytics applications (object detection, image recognition, NLP, etc.) using BigDL.

Cloudy with a chance of on-prem

December 4, 2019

The cloud is becoming pervasive, but it isnt always full of rainbows. Defining a strategy that works for your company or for your use cases is critical to ensuring success. Jim Scott explores different use cases that may be best run in the cloud versus on-premises, points out opportunities to optimize cost and operational benefits, and explains how to get the data moved between locations.

Continuous analytics: Integrating the data hub in a DevOps pipeline

December 4, 2019

Arturo Bayo and Alvaro Fernandez Velando explain how a data hub strategy helps clarify data sharing and governance in an organization and share one way to implement a data hub architecture using big data technology and resources that are already established in the enterprise.

Conversation AI: From theory to the great promise

December 4, 2019

For years, people have been talking about the great promise of conversation AI. Recently, deep learning has taken us a few steps further toward achieving tangible goals, making a big impact on technologies like speech recognition and natural language processing. Yishay Carmiel offers an overview of the impact of deep learning, recent breakthroughs, and challenges for the future.

Data citizenship: The next stage of data governance

December 4, 2019

Successful organizations are becoming increasingly Agile, and the autonomy and empowerment that Agile brings create new, active modes of engagement. Data governance however is still very much a centralized task that only CDOs and data owners actively care about. Antonio Alvarez and Lidia Crespo outline a more engaging and active method of data governance: data citizenship.

Data wrangling for insurance

December 4, 2019

Drawing on use cases from Trifacta customers, the speaker explains how to leverage data wrangling solutions in the insurance industry to streamline, strengthen, and improve data analytics initiatives on Hadoop.

Deep learning: Assessing analytics project feasibility and computational requirements

December 4, 2019

Adam Grzywaczewski offers an overview of the types of analytical problems that can be solved using AI and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Adam then covers the computational requirements for the deep learning training process, leaving you with the key tools you need to initiate an analytical AI project.

Deploy Spark ML TensorFlow AI models from notebooks to hybrid clouds (including GPUs)

December 4, 2019

Chris Fregly explores an often-overlooked area of machine learning and artificial intelligencethe real-time, end-user-facing "serving layer in hybrid-cloud and on-premises deployment environmentsand shares a production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.

Enterprise artificial intelligence

December 4, 2019

Laura Frolich explores applications of deep learning in companieslooking at practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research to prototype to scaled production deploymentand discusses the future of enterprise AI.

EU GDPR as an opportunity to address both big data security and compliance

December 3, 2019

Many businesses will have to address EU GDPR as they deploy big data projects. This is an opportunity to rethink data security and deploy a flexible policy framework adapted to big data and regulations. Eric Tilenius explains how consistent visibility and control at a granular level across data domains can address both security and GDPR compliance.

Executive Briefing: Data governance and evolving privacy legislationDaring to move beyond compliance

December 3, 2019

The EU's General Data Protection Regulation is an ambitious legal project to reinstate the rights of "data subjects" within an increasingly lucrative data ecosystem. Aurlie Pols explores the legal obligations on companies and their respective interpretations and looks at how scale and integrity will be safeguarded in the data we increasingly base decisions upon in the long term.

Executive Briefing: Dealing with device data

December 3, 2019

In 2007, a computer game company decided to jump ahead of competitors by capturing and using data created during online gaming, but it wasn't prepared to deal with the data management and process challenges stemming from distributed devices creating data. Mark Madsen shares a case study that explores the oversights, failures, and lessons the company learned along its journey.

From data dinosaurs to data stars in five weeks: Lessons from completing 80 data science projects

December 3, 2019

More organizations are becoming aware of the value of data and want to get started and scaled up as quickly as possible. But how? Is it possible to get something useful done in five weeks? Kim Nilsson shares her experiences, both good and bad, delivering over 80 five-week data science projects to over 50 organizations, as well as some concrete tips on how to become a data star organization.

Gaining additional labels for data: An introduction to using semisupervised learning for real problems

December 3, 2019

There are sometimes occasions where the labels on data are insufficient. In such situations, semisupervised learning can be of great practical value. Yingsong Zhang explores illustrative examples of how to come up with creative solutions, derived from textbook approaches.

GDPR, data privacy, anonymization, minimization. . .oh my!

December 3, 2019

The global populace is asking for the IT industry to be held responsible for the safe-guarding of individual data. Steve Touw examines some of the data privacy regulations that have arisen and covers design strategies to protect personally identifiable data while still enabling analytics.

Growing a data-driven organization at easyJet

December 3, 2019

Many large organizations want to develop data science capabilities, but the traditional complexity and legacy of such companies dont allow a fast and agile evolution toward data-driven decision making. EasyJet is working toward becoming completely data driven. Alberto Rey shares real-world examples on how easyJet is tackling the challenges of scaling up its analytics capabilities.

Hadoop and object stores: Can we do it better?

December 3, 2019

Trent Gray-Donald and Gil Vernik explain the challenges of current Hadoop and Apache Spark integration with object stores and discuss Stocator, an open source object store connector that overcomes these shortcomings by leveraging object store semantics. Compared to native Hadoop connectors, Stocator provides close to a 100% speedup for DFSIO on Hadoop and a 500% speedup for Terasort on Spark.

Hadoop as a service: How to build and operate an enterprise data lake supporting operational and streaming analytics

December 3, 2019

If you have Hadoop clusters in research or an early-stage data lake and are considering strategic vision and goals, this session is for you. Phillip Radley explains how to run Hadoop as a shared service, providing an enterprise-wide data platform hosting hundreds of projects securely and predictably.

How knowledge graphs can help dramatically improve recommendations

December 3, 2019

Collaborative filtering is great for recommendations, yet it suffers from the cold-start problem. New content with no views is ignored, and new users get poor recommendation. Aurlien Gron shares a solution: knowledge graphs. With a knowledge graph, you can truly understand your users' interests and make better, more relevant recommendations.

How to secure Apache Spark?

December 2, 2019

Security has been a large and growing aspect of distributed systems, specifically in the big data ecosystem, but it's an underappreciated topic within the Spark framework itself. Neelesh Srinivas Salian explains how detailed knowledge of setting up security and an awareness of what to be looking out for in terms of problems and issues can help an organization move forward in the right way.

Identifying and exploiting the keys to digital transformation

December 2, 2019

Leading companies are integrating operations and analytics to make real-time adjustments to improve revenues, reduce costs, and mitigate risks. There are many aspects to digital transformation, but the timely delivery of actionable data is both a key enabler and an obstacle. Jack Norris explores how companies from Altitude Digital to Uber are transforming their businesses.