Group items tagged

GL_JE_BATCHES (je_batch_id)                                   => GL_JE_HEADERS (je_batch_id) GL_JE_HEADERS (je_header_id)                                  => GL_JE_LINES (je_header_id) GL_JE_LINES (je_header_id,  je_line_num)                      => GL_IMPORT_REFERENCES (je_header_id, je_line_num) GL_IMPORT_REFERENCES (gl_sl_link_table, gl_sl_link_id)        => XLA_AE_LINES (gl_sl_link_table, gl_sl_link_id) XLA_AE_LINES (application_id, ae_header_id)                   => XLA_AE_HEADERS (application_id, ae_header_id)  XLA_AE_HEADERS (application_id, event_id)                     => XLA_EVENTS (application_id, event_id)    XLA_EVENTS (application_id, entity_id)                        => XLA.XLA_TRANSACTION_ENTITIES (application_id, entity_id) 

The source_id_int_1 column of xla.xla_transaction_entities stores the primary_id value for the transactions. You can join the xla.xla_transaction_entities table with the corresponding transactions table for obtaining additional information of the transaction. For e.g you join the xla.xla_transaction_entities table with ra_customer_trx_all for obtaining receivables transactions information or with mtl_material_transactions table for obtaining material transactions information. The entity_id mappings can be obtained from the XLA_ENTITY_ID_MAPPINGS table

MySQL supports replication from one major version to the next higher major version. For example, you can replicate from a master running MySQL 4.1 to a slave running MySQL 5.0, from a master running MySQL 5.0 to a slave running MySQL 5.1, and so on.

Important It is strongly recommended to use the most recent release available within a given MySQL major version because replication (and other) capabilities are continually being improved. It is also recommended to upgrade masters and slaves that use early releases of a major version of MySQL to GA (production) releases when the latter become available for that major version.

Plug-in Details: CLOB Load Have you ever used a Rich Text Editor item type in APEX? Has the amount of text ever exceeded 32k? Choke! And so begins the search for a work around... While work arounds exists they can be a little difficult find and repeat each time you need them. We designed the Enkitec CLOB Load plug-in to simplify and standardize how large amounts of text are moved from the database to APEX page items and then back to the database.

Next, open VMWare and select Create a new virtual machine Select “I will install the operating system later” Make your OS selection about the OS that’s currently on the vmdk you will be using. (The guest OS, not the host OS). Later on, you will have the option to use an existing vmdk image as your virtual hard drive. Do so. You should now be able to finish setup and boot your converted disk image.

 Table Eav_Entity_attribute

Save file to file system rather than into table

mysqldump -h localhost -u [MySQL user, e.g. root] -p[database password] -c --add-drop-table --add-locks --all --quick --lock-tables [name of the database] > sqldump.sql

Extract the Database

Summary: Check Search whole cells and uncheck Regular expressions

Entity Attribute Value

number of attributes (properties and parameters) which can be used to describe them are potentially vast, but the number of attributes which will actually apply to a given entity are relatively modest.

sparse matrix

AR_PAYMENT_SCHEDULES_ALL

OBIEE 11G configuration’s Maximum Limits

Be careful when you are setting the Cache attribute values. For implementations under significant stress increase this value to 1000 or a higher value as mentioned below. The main factor that affects its size is the memory consumption. Maximum range of Global Cache storage size (in Megabytes) to use when BI Server cache is enabled – 9,999 Maximum number of cache entries; used to tune cache performance – 99,999 Maximum size (in Megabytes) for individual server cache entries – 9,999 Data Display/Download: The Maximum no.of rows that we can set to display/download in the Table/Pivot views. But keep in mind OBIEE is NOT meant to be a tool to extract huge amounts of information. Maximum number of rows of data to include in Table views – 9,999,999 Maximum number of rows of data to include in delivered emails – 9,999,999 Maximum number of rows to export to excel – 9,999,999

In OBIEE 11G, The instanceconfig.xml file stores the OBI PS configuration settings. Many configuration settings are available in EM and those should be controlled within EM only unless if we disabled centralConfigurationEnabled option. If any particular setting is not available in EM then we should make changes directly in instanceconfig.xml file. There are many situations where we need to change the default OBI configuration settings to a new value.

java -jar ords.war configdir /tmp/wls/conf

Configure ORDS by doing: 1java -jar apex.war

Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.

The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.

The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.

a lot of articles calling data scientists and statisticians the jobs of the future

there are more immediate needs that, when addressed, will have a much greater business impact.

Right now we have huge opportunities to make the data more accessible, more “joinable” and more consumable. Leaders don’t want more data – they want more information they can use to run their businesses.

f you are running a large Oracle data warehouse you should be using parallel

Like all tools you have to use parallel correctly; no more would we think of using a wrench to hammer a nail then should you think parallel is the answer to all performance problems. Sometimes parallel will make things worse, sometimes parallel will make performance less predictable.

Parallel introduces additional work to a query, simplistically we need to: split the query into multiple parallel processes, execute them, wait for the processes to complete and finally coordinate the results. This all takes time to do. Our time saving comes from being able to process multiple smaller chunks of data simultaneously. If the time to execute the step in parallel is not significantly faster than doing it without parallel then the additional overhead may make parallel processing a slower option; this is typically the case with small tables where a full tablescan or an indexed access is fast. Use too few parallel processes and we will not gain much in performance, too many and we risk starving the database of resource for other work or even slow our own process as it waits for resource. If you have implemented some form of CPU resource management on your system you may find that you experience delays as your parallel slaves ‘wait their turn’

Integration BI infrastructure: All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Metadata management: Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects. Development tools: The platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.

Information Delivery Reporting: Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. Dashboards: Includes the ability to publish Web-based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications, or in conjunction with a complex-event processing engine. Ad hoc query: Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to enable users to navigate available data sources. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data "refreshes" and pivot tables. Advanced integration includes cell locking and write-back. Search-based BI: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. Mobile BI: Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client's location awareness.

Analysis Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and "what if" modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). Interactive visualization: Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Predictive modeling and data mining: Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms. Scorecards: These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Prescriptive modeling, simulation and optimization: Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.

its a very powerful query engine/data transformation tool.

what's fascinating is its ability to load web data from static html pages, tables on web pages, web services, etc. I had a need to search recent activity on Twitter and decided to test drive Data Explorer in Excel.