How do I use Data Guard, Broker, Snapshot Standby and Flashback database to perform integrated disaster recovery tests without interrupting production? In this session you will learn how Paychex leveraged Oracle Dataguard to build a new remote data center, automate Data Guard Broker to activate 21 mission and business critical databases as Snapshot Standby, perform…

With unprotected assets in plain sight, it's no wonder hackers seek to steal sensitive data from databases. Exploiting common vulnerabilities such as unpatched systems, overprivileged accounts, insecure database configurations, stolen passwords, and unencrypted data is a quick place to start. However, knowing the mind of a hacker can better help create a blueprint for protecting…

Uptime, Redundancy, Resiliency, High Availability, Disaster Recovery, Replication - all of these terms highlight the need to keep the wheels rolling for your Oracle systems. It is crucial to prepare for both planned and unplanned interruptions, and while the concepts will be easy to grasp, it can be difficult to know where to start. This…

Oracle Database recently switched to a new annual release model, and Oracle Database 18c will be the first release in this new model. This session is a unique opportunity to get ahead of the curve and learn what's new in Oracle Database 18c directly from the Oracle Database development team.

Oracle Advanced Analytics 18c delivers new features and significant performance gains.   OAA new features include an unsupervised feature selection algorithm, ESA algorithm, and partitioned models.  OAA can now build machine learning models on billions of records in minutes, “score” millions of records even faster and supports real-time model deployment. Integration with R now supports R…

Fun Facts about Fraud: #1 fraud detection method is a tip, 33% of business failures are due to theft and fraud, the median time for fraud detection is 18 months and 49% of victims do not recover their losses.  With such a significant cost, why can’t companies better combat against fraud? Oracle’s machine learning and advanced analytical capabilities transform your Oracle data management platforms into powerful fraud detection and prevention solutions. Using ML algorithms specifically designed to detect flag and predict rare events companies are turning the tide against this scourge and building applications to automate its detection.  Come see Oracle’s machine learning functions and how they can help you fight crime.

Imagine a world that anticipates your every move.  Datafication, smart phones, Twitter, Facebook, GPS and IoT, produce a digital exhaust that tracks your every movement, relationships and activities. Now, companies know everything about you and can anticipate your future.  Peter Tucker, author of “The Naked Future” paints a vivid picture of a present and “very near future” where machine learning and “cognitive” technologies can analyze gigabytes of data and even predict your future. Oracle’s Big Data, Machine Learning, Notebooks, Clouds and “predictive” Applications make this “naked future” more real today than you may realize.  Come hear about a future made possible by Oracle’s Data Management and Machine Learning technologies.

Most data science projects begin with data, “tools” and scripts but fail to get beyond the data scientist. They hit a wall when attempting to “operationalize” the models.  Netflix never implemented the algorithm that won the Netflix $1 Million Challenge.  This dichotomy between enterprise and algorithms is eliminated when algorithms are built into the data management platforms.  By “moving algorithms to the data”, Oracle Database and Big Data Clouds are now data management and advanced analytical platforms.   Developers use SQL, R and Oracle Data Miner UI to build, evaluate and deploy advanced analytical methodologies.  See how to go beyond “tools” to applications.  Several Oracle “predictive” Applications will be shown as examples.

  Developers often need their DBA’s to provide quicker identification and analysis on sub-optimal SQLs during load test cycles and peak production usage of the application. This becomes crucial, especially when the load test cycles are denser and there are relatively huge number of SQL statements in the entire application. Effective performance management and diagnosis…

AWR collects and persists thousands of performance metrics every hour; the problem is: the root causes of performance anomalies are difficult to detect and it is difficult to know which metrics/attributes are important for DBA’s to focus on to inform their root cause analysis and solutions. A full understanding of these thousands of metrics is impossible, thus many DBA’s (and even the off-the shelf tools) monitor only a standard set of well-known metrics. This “small model” approach may cause you to miss important system behavior or configuration that is relevant to the root cause of the performance problem. The presenters approach to this “small model” meta problem is to massively expand and dynamically extract only the relevant performance metrics. This paradigm shift is achieved by normalizing the data and looking across a wide array of AWR performance metrics gathered during the problem interval. Querying across the normalized data and using statistical analysis, this approach flags unusual trends. By targeting the right metrics at the right time, you “bundle” relevant results which results in event focused and actionable intelligence on the performance issue, and a richer insight into possible solutions.