Learn about new architectures that successfully supply the needs of both business analysts and data scientists. Get a peek at the future.

Since the GoLive platform itself is built on top of Kafka, we also will highlight the advantages of using the same streaming platform to achieve asynchronous communication between micro-services and real-time web-socketing.

By exploring Service Level Objective theory, we’ll explore how to intentionally design effective and governable data products and how to move them into a state of automated data governance.

This Session will be on the real Use Case he did on a huge Medical Company, using open-source tools to get real-time data incrementally from Relation Database to Cloudera, will be a live demonstration on Getting events from Kafka and Data from RDS streamed to Cloudera using Stream sets Data Collector tools.

Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the scale and as events arrive.

He wants to present when Amazon SageMaker shines and when you should avoid it. Everything is supported by the experiences that we – at Pattern Match – have gained on real-world projects.

In this research, you will be able to see how it would be possible to use machine learning and neural networks on the source code itself to find any security flaws without actually executing or building the source code (none-compiled) code.

With this theory, we try to deal with different social and interaction problems such as fraud detection, min path searching, and link predictions.

In this talk we will go through the reasons why monitoring the performance of your open source databases is so important – attendees will learn how to keep their open source databases running smoothly without compromising on security, performance or availability at the same time.

In this session, Oliver will walk through some of the best serverless options on how to operationalize ML pipelines within the Tensorflow ecosystem and on the Google Cloud Platform, based on actual case studies.