Lucas Jellema is solution architect and CTO at AMIS, The Netherlands. The running theme through most of his activities is transfer of knowledge and enthusiasm (and live demos). Lucas is JavaOne 2015 Rockstar, Oracle Developer Champion and ACE Director and a frequent speaker at conferences such as Oracle Code, Oracle OpenWorld, JavaOne and Devoxx. He publishes techy stuff at Github, Slideshare, DZone, OTN, and the AMIS Technology Blog (https://technology.amis.nl). He is the author of two books with O’Reilly Press.
Data has been and will be the key ingredient to enterprise IT. What is changing is the nature, scope and volume of data and the place of data in the IT architecture. BigData, unstructured data and non-relational data stored on Hadoop, in NoSQL databases and held in Elastic Search Indexes, Caches and Message Queues complements data in the enterprise RDBMS. Emerging patterns such as microservices that contain their own data, BASE, CQRS and Event Sourcing have changed the way we store, share and govern data. This session introduces patterns, technologies, trends and hypes around storing, processing and retrieving data using products such as MongoDB, MySQL, Kafka, Redis, Elastic Search and Hadoop/Spark -locally,in containers and on the cloud
Key take away: what an application architect and a developer should know about the various types of data in enterprise IT and how to store/manage/query/manipulate them. What products and technologies are at your disposal. How can you make these work together - for a consistent (enough) overall data presentation.
Our technology has gotten smart and fast enough to make predictions and come up with recommendations in near real time. Machine Learning is the art of deriving models from our Big Data collections – harvesting historic patterns and trends – and applying those models to new data in order to rapidly and adequately respond to that data. This presentation will explain and demonstrate in simple, straightforward terms and using easy to understand practical examples what Machine Learning really is and how it can be useful in our world of applications, integrations and databases. Hadoop and Spark, real time and streaming analytics, Watson and Cloud Datalab, Jupyter Notebooks and Citizen Data Scientists will all make their appearance, as will SQL.