Dell Technologies, Inc
For many complex database applications such as Oracle E-Business suite, it is a quite common that a production environment needs 10 or more copies of various development and test purposes. This database clone process to create these many copies of production database can be very challenging and costly especially when the database gets larger than multiple even over 10 terabytes (TBs). This session will discuss two snapshot based methods that can help IT simplify this database cloning process and reduce the time and resource cost: The method is called Oracle gDBClone which is based on Oracle ACFS snapshot. The second method is based on database storage volume snapshot provided by the storage array. With a real example in our IT department, we will discuss the implementation details of these two methods and discuss the pros and cons of these two methods. Objectives: Examine the need and challenges to clone large over 10TB Oracle EBs database for DEV/Test Discuss two snapshot based methods: Oracle gDBClone which is based on Oracle ACFS snapshot and Database storage volume snapshot method Discuss the implementations of these two methods and their pros and cons comparison through a real example.
Oracle Autonomous Database services are built around Oracle database and comes with fully automated with self-driving, self-securing, self-repairing and offer two options optimized by workloads : Autonomous Data Warehouse (ADW) and Autonomous Transaction Processing (ATP). In this session you will learn the easy provisioning process of these two database cloud service, learn how to navigate the service console for service management, how to access the databases, define the database objects, load the data and run the query and transactions. For Autonomous Data Warehouse (ADW), you will how import and export large set of data to/from ADW database, learn how to use data visualization tool built in the Oracle cloud to run advanced analytics and machine learning applications as well as some troubleshooting experiences and tips using ADW. We will also discuss the options and process to migrate your on-premise databases to these database cloud services.
Oracle Sharding was further improved in 18c/19c for linear scalability and complete fault isolation of OLTP workloads. With Oracle sharding Data are partitioned horizontally partitioned across discrete Oracle Databases (shards) in shared-nothing architecture that collectively form a single logical database. Come to this session to learn its latest improvements in Oracle 19c and 20c, sharding on database cloud(DBCS) and leveraging sharding for your business. We will share the experience and tips of configuring sharded database architecture with HA replication for massive scalability and complete fault isolation. We will also discuss some of considerations of choosing sharding method in a global geographic distribution application and the experience of using Sharing Advisor . We will also discuss some new sharding features that are coming in Oracle 20c such as Federated Sharding and sharding with database in Persistent memory.
Many Machine Learning tasks need to access a lot of data set, which in many case are stored in a database such as Oracle Database. Instead of moving this large data set across network to an separate machine learning system, it makes a more scalable solution to do the machine learning task in the database, which is called in-database machine learning. Oracle Autonomous Data Warehouse (ADW) comes a library of Oracle machine learning algorithms and a set of building tools such as SQL notebooks for predicative analytics and machine learning. This allows Data scientists to run Machine Learning projects in Oracle Database without moving data and also allows Database developers to pick up Machine learning with database tables, SQL and PL/SQL. l This session will examine Oracle Machine Learning as part of ADW collaborative environment. We will discuss process to develop machine learning applications in the ADW Oracle Machine Learning environment: analyze and prepare data set; build and evaluate and apply machine learning model. We also will discuss other Oracle Machine Learning family products and what news are coming up in Oracle 20c