ADW compatibility matrix
visibility into spending with Oracle Cloud Infrastructure
Get Started with EPM Cloud Planning
– Getting Started with Oracle EPM Cloud for Administrators: https://docs.oracle.com/en/cloud/saas/enterprise-performance-management-common/cgsad/index.html
– Getting Started with Oracle EPM Cloud for Users: https://docs.oracle.com/en/cloud/saas/enterprise-performance-management-common/cgsus/index.html
when provisioning Oracle Analytics Cloud, different shapes are available to run the Service. For shapes based on only 1 OCPU auto scaling will not be available after the initial provisioning of the service. Thus, if the OAC service needs to support scaling after the initial creation a shape with at least 2 OCPUs should be selected:
Golden Gate software available for free on Oracle OCI
Golden Gate is available for free on OCI Marketplace until May 2020. Any customer of the cloud service must only pay for the compute resources of OCI which are being used. This will only be available until and a customer must use the OCI Marketplace. The OCI shape is available here: https://cloudmarketplace.oracle.com/marketplace/en_US/listing/58489224
Golden Gate can be used to synchronize a Autonomous Datawarehouse from a on-Premise source with near real-time data feeds:
for more information on Golden Gate on OCI, check here.
Free Platinum Services for Oracle EPM Cloud are launched
The match recognize clause can be used since Oracle Database 12c. This clause is useful for data transformation of sequence data i.e. web session data. Considering a simple data set with web sessions of only two Users where the time_id denotes a point in time on the time axis and User_id the respective User:
A web session is defined as a series of action (on the timeaxis) within a gap of less then 10 time units.
Match recognize allows to transform the above data into partitions that meet certain conditions e.g. actions within less the 10 units from the previous action. Using the following statement on above Table:
SELECT time_id, userid, session_id FROM (SELECT TO_NUMBER(j.session_doc.time_id) as time_id, j.session_doc.user_id as userid FROM json_sessionization j) MATCH_RECOGNIZE( PARTITION BY userid ORDER BY time_id MEASURES match_number() as session_id ALL ROWS PER MATCH AFTER MATCH SKIP PAST LAST ROW PATTERN (b s*) DEFINE s as (time_id - PREV(time_id) <=10) );
The statement will create a new column that specifies a session indicator:
The statement can be enhanced to provide even more detail about the session in terms of start and end time and duration:
SELECT time_id, userid, session_id, no_of_events, start_time, end_time, session_duration FROM (SELECT TO_NUMBER(j.session_doc.time_id) as time_id, j.session_doc.user_id as userid FROM json_sessionization j) MATCH_RECOGNIZE( PARTITION BY userid ORDER BY time_id MEASURES match_number() as session_id, COUNT(*) as no_of_events, FIRST(b.time_id) start_time, LAST(s.time_id) end_time, LAST(s.time_id) - FIRST(b.time_id) session_duration ALL ROWS PER MATCH PATTERN (b s+) DEFINE s as (time_id - PREV(time_id) <= 10) );
This will return the following calculations on the session:
Thus, using match recognize the data can also be aggregated to calculate measures.
A complete tutorial to create the test data can be found here.
Using Tensorflow for Machine Learning
ADW Resource Management and Timeouts
Autonomous Database allows to specify resource limits in terms of time and I/O usage, check below link on resource management setting from the ADW Documentation: