HIGH FIVE POUG

Bi is can be a complex matter in big organisations. This presentation will tell the story about a strategic business decision to move the on premises BI stack to the Microsoft Azure cloud. This talk will focus more on the db-part than the bi part.

This presentation will cover - how we managed to move +300TB divided over several multi TB databases to the cloud - which services were chosen to run the database servers and why - how performance testing and sizing has been done - how automatic provisioning has been done for providing developers what they need in agreed SLA’s. - how we dealed with the short deadline - why the Azure Cloud was chosen over the Oracle cloud - Cost savings?

The deadline of the project was end of the year 2016. This gives the opportunity to give you an insight in a very fresh project of the move to the cloud from a complete BI environment.

CIMA won The Oracle Excellence Award 2016 for Cloud Innovation and Integration. Want to know why?, In this talk we will be looking at the decisions businesses have to consider when moving applications such as ERP and HCM to the cloud. . In this talk we will look at the real-world experiences when implementing a PAAS Cloud service such as DBCS and SOA. We will discuss topics such as: topologies; backups; user and key management; provisioning; Security; monitoring post go-live, etc. Delegates will learn about important design considerations and some of the vital tasks they need to do to make their Oracle PaaS environments consistent and easily manageable (over and above the click-click-click of demonstrations!). Once you have the data in the cloud, how do we report on this?, we will look at ways to extract this data for BI?

At the end of the talk the audience should have a clear understanding of the challenges and possible solutions in moving to the cloud. You should be able to walk away with answers to the following :

1) Things to consider when moving to the cloud. 2) Security in the Cloud. 3) Behaviors that may need to change in the business. 4) How to extract data from the cloud. 5) Integrating Cloud Data with Premise Data.

Warning: Please don’t expect any fancy Microsoft PowerPoint slides in this session. It is pure (live) Oracle core hacking fun but also very educational at the same time. For example you will learn how to extract various information from the Oracle core and how to use it in different troubleshooting scenarios or just for educational purpose. Understanding the Oracle core helps you to become a better troubleshooter :-)

Direct NFS is an Oracle implementation of the standard NFS protocol inside Oracle Database. Using this native solution Oracle instance is able to better handle IO traffic and bypass a kernel layer of operating system. dNFS is commonly used for Exadata backups into ZFS Appliance and for Oracle databases using a NFS based solutions (like NetApp or Delphix ).

The goal of this presentation is a show DBA’s what is a Direct NFS, how to configure, use and monitor it. Proper configuration and monitoring are important to achieve a maximum performance and easy problem diagnostic. There will be examples of configuration with and without direct NFS.

NFS protocol is based on the TCP/IP protocol and this presentation will also show a recommended configuration of the TCP stack and explain how network should be monitored.

You might (or might not) have heard of Docker. But you have no idea what it is and why you should care. But if you are a database or APEX developer and still work with Virtual Machines, it is about time to broaden your horizon. In this session you’ll learn what Docker is and how you can benefit from it in your daily work.

This session is going to discuss simple things to remember while tackling performance issues with complex applications deployed in multiple tiers.

As a DBA or Database-person you will be asked to join a Dev_Ops team and use some of the Agile methods. When this happens, you will have to absorb new knowledge and work-habits. But you will also have to educate your colleagues on what is possible and impossible at the database level. We will try to give some guidelines, and point out a few embarrassing mistakes we made in these situations.

A familiar scenario for database administrators - a user reports a “Lock in the database”. This situation is often very critical and there is a limited time for the solution. During the presentation, there will be a classification and an explanation of the different kind of locks.

A brief high level overview of AWR Warehouse, and why anyone responsible for managing database instances should be using it we will drill into some of the internals of how it works and finish with some ideas about how to troubleshoot and resolve issues when it breaks.

Graph databases have become increasingly popular over the last few years, largely because graph structures are highly flexible and provide a simple and intuitive way of modeling data which subsequently can be queried easily. In graph databases data is held in a graph consisting of a set of nodes with links between them rather than keeping data records in tables as is the case in relational technologies.

Since 2015 Oracle offers a graph database based on the concept of property graphs as part of Oracle Big Data Spatial and Graph. The actual data store can either be an Oracle Database, Oracle NoSQL or Apache HBase. Graph analytics are done using an in-memory engine.

For data access it uses an API based on the open source Apache Tinkerpop project, which has evolved into the industry standard in these kinds of environments. Besides this it includes the PGQL query language designed for pattern matching, and it comes with a large number of built-in algorithms for graph analysis.

In this paper we will explain the fundamentals of property graph databases and highlight use cases in which property graph implementations are superior to relational technologies. Besides looking into the architecture and query language of Oracle Big Data Spatial and Graph we will show which kinds of applications can benefit from specific algorithms.

We will cover the conversion of relational tables to graph structures, as well as the visualization of property graph data using commercial and open source tools. And finally, we will look into a series of benchmarks based on typical datasets which have been conducted against similar technologies.