Sharing and Deploying Data Science with KNIME Server - February 2019

Rapid Miner and Azure HDInsight work together to deliver a complete data science and machine learning platform for massive amounts of data using popular open source frameworks such as Hadoop, Hive, MapReduce, and Spark.Hear from RapidMiner Product Manager, Jesus Puente and Cloud Software Engineer Beth Zeranski, Cloud at Microsoft for this 60-minute webinar where they will discuss.

In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.

Navigating the course of data center management comes with a multitude of obstacles. Most IT professionals must balance maximizing uptime, improving security, optimizing operations and meeting budget realities. In the case of data center maintenance, what is the best route to take after initial warranty support expires? Uptime a

AI is seen by many as the best way to secure the future of their organisations, but there is significant public concern about its possible detrimental impact. Some are concerned about the concentration of power in the hands of huge tech companies, while some see automation as a threat to their employment.