Hadoop With the Best brings the world’s top Hadoop experts to your living room!
Enjoy a comfortable conference experience, get insights from the experts and engage in live Q&A with your favorite presenters.
Exclusive webinars will be centered around Hadoop and Big Data with a focus on data streaming.
10-15 presentations on Hadoop aimed towards developers focused on Hadoop.
Live presentations: Speakers will be presenting live and hosting Q&A after each session.
A unique learning experience on a newly developed platform.
Get the best seat in the house from the best seat in your house!
15 minutes of exclusive 1:1 exchanges with presenters using the video chat and screen sharing functions.
Replay your favorite presentations: presentations are recorded and available to you at any time!
Q&A forum: message a presenter to ask questions directly on the platform.
Exclusive blog articles from the presenters
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Today, if a byte of data were a gallon of water, in only 10 seconds there would be enough data to fill an average home. In 2020, it will only take 2 seconds. The Internet of Things is driving a tremendous amount of this growth, providing more data at a higher rate then we’ve ever seen. With this explosive growth comes the demand from consumers and businesses to leverage and act on what is happening right now. Without stream processing, these demands will never be met, and there will be no big data and no Internet of Things. The worlds of stream processing and Hadoop have been on a collision course for some time. The Holy Grail that everyone has been chasing for many years is near real-time queries from Hadoop. Where are we in this pursuit, how can you get there today and what can you do to prepare for tomorrow? In this talk, I aim to help you get there and be ready to take on the world of stream processing with the Hadoop ecosystem.
How to use the power with open source stream-driven technology toolsets (e.g., Kafka, Flume, Storm, Flume, Spark, Cassandra, HBase, and HDFS) and machine learning to create a real-time, 360-degree view of the customer for “moments that matter."
An IoT world full of people interacting with connected devices shows great promise for many businesses. However, raw interaction events from such devices provide only a very limited picture of what's actually happening. Was an accelerometer trigger part of a real user gesture, or was the device merely jostled? At Silicon Valley Data Science (SVDS), we use Kafka, Spark streaming, and Hadoop to catch streams of human-computer-interaction events and turn them into a usable representations of user activity. I'll take examples from across various industries and discuss implementation trade-offs for both analysis results and performance.
One of the most exciting areas for applying Hadoop technologies is the rapid growth of data from smart, connected products. We draw from experience in managing data from diverse domains including cell phones, fitness trackers, connected cars, enterprise high tech, and medical devices. While there are many differences, there are common patterns in working with complex configuration data, mining intelligence about product usage and predicting failures from logs and tracking time series of sensor data. We discuss the role of batch and streaming analytics and how organizations can collect and trust these data sets to make better decisions and to drive real-time business processes.
The Big Data ecosystem is growing rapidly. Seemingly every week new and 'cool' Big Data projects are abuzz. As an enterprise architect, how do you decide which technologies to adopt? The challenge becomes: do I take a risk and adopt a new technology, or should I remain conservative and stick with the tried and true technology? More often than not, the answer is– it depends. In this presentation, I will discuss a different approach to tackling this question. Using real world examples, I will break down the best method for choosing a Big Data stack to apply. Examples will include ETL workloads, streaming data and real time dashboards. Finally, I will cover a few of the up and coming Big Data technologies in the ecosystem.
You are planning your hadoop cluster and decide you want to move terabytes of data to store and process in HDFS, but how do you make sure all of that data is now secure? Let's discuss and strategize how and why we need to secure data inside our hadoop cluster, while looking at methods available to us today. We will cover securing access, authorization, encryption, and security policies with big data technologies that are readily available.
Big Data brings new challenges in assuring the data and solution quality, in terms of volume, scalability, type, performance, availability, etc. A big question is, how can we test the elephant in the room? This talk presents a holistic method for Big Data quality assurance. We will drill down to how to formulate a systematic approach, develop a comprehensive QA framework, strategize the Big Data testing, operationalize the implementation, and govern the operations. A set of discipline components for functional and non-functional tests will be discussed, along with industry best practices and lessons learned.
We live in a world today that is always abuzz with news, anecdotes, and the everlasting omnipresence and omniscience of big data. While marketers acorss our industry are looking at ways to monetize the vastness of available zettabytes, data scientists the world over are burning the midnight oil to harness new technologies (like streaming, Hadoop, and other NoSQL stores), commodity hardware, and cloud computing to create insights that at the right perspectives will literally transform the business organizations. As we all dread the future state of an enterprise, we quickly realize that with effective Data Governance and Information Governance, we can deliver the efficiencies that companies need to realize with integrating Big Data. Attend this session for an information filled discussion on the subject, issues we face, potential solutions and how the traditional companies are getting into the game. We will discuss some case studies as examples for the discussion including home depot and a large international soft drink beverage vendor.