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Data warehousing features in oracle


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Jinal Shah

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Data warehousing features in oracle

  1. 1. 1. The SQL Model Clause 1. The SQL Model clause has been designed to address the sort of situation where, in the past, clients have taken data out of relational databases and imported it into a model held in a spreadsheet 2. The SQL Model clause allows users to embed 'spreadsheet-like' models in a SELECT statement, in a way that was previously the domain of dedicated multidimensional OLAP servers such as Oracle. 3. The aim of the SQL Model clause is to give normal SQL statements the ability to create a multidimensional array from the results of a normal SELECT statement, carry out any number of interdependent inter-row and inter-array calculations on this array, and then update the base tables with the results of the model Data Warehousing Features In Oracle In this link data warehouse features provided []
  2. 2. Data Warehousing Features In Oracle 1. The SQL Access Adviser is a new feature that recommends the best combination of indexes and materialized views for a given database workload. 2. The SQL Access Adviser is based on the index and summary advisors previously bundled with Oracle. 3. Provides a 'one-stop-shop' for tuning and summarising your warehouse data. 2. The SQL Access Adviser
  3. 3. Data Warehousing Features In Oracle 1. First up is improvements to the way large Analytic Workspaces can be partitioned, introducing into the Oracle OLAP world some of the advanced partitioning options currently enjoyed by Oracle database users. 2. This was of some benefit, splitting by segment size was the only way of partitioning the data, and you couldn't specify what objects within the analytic workspace went in to each partition. 3. A real improvement over what was available with Express, is support for multi-user read-write access to individual analytic workspaces. 3. Improvements To The Multidimensional OLAP Engine
  4. 4. Data Warehousing Features In Oracle 1. Query Rewrite (the ability for Oracle to transparently redirect queries from detail level to summary tables) is one of the best data warehousing features in Oracle. 2. Oracle has a number of improvements to Query Rewrite and the materialized view tuning process that should make this process a bit more productive. 3. With Oracle Database , query rewrite is now possible when your SELECT statement contains analytic functions, full outer joins, and set operations such as UNION, MINUS and INTERSECT. 4. The Tune MView Advisor, and improvements to Query Rewrite
  5. 5. 1. Data Pump is a replacement for the venerable IMP and EXP applications used for creating logical backups of Oracle tables, schemas or databases. 2. Data Pump is a server application (as opposed to IMP and EXP, which were client applications) which, in beta testing, was twice as fast as the old EXP for exporting data, and ten times as fast as the old IMP for importing data. 3. Data Pump is callable either through the DBMS_DATAPUMP package, through the replacements for IMP and EXP, known as IMPDP and EXPDP, or through a wizard delivered as part of Oracle Enterprise Manager Data Warehousing Features In Oracle 5. Data Pump, The Replacement For Import and Export
  6. 6. 6. Improvements To Storage Management Data Warehousing Features In Oracle 1. Automatic Storage Management (ASM) is one of the 'cool new features' in Oracle 10g that is meant to reduce the workload for Oracle DBAs. 2. ASM completely automates the process of creating logical volumes, file systems and file names, with the DBA only specifying the location of raw disks and ASM doing the rest. 3. ASM deals with the problems caused by rapidly expanding data warehouses, where administrators can no longer deal with the sheer number of disk units, nodes and logical groupings, and is a key feature of the Oracle .
  7. 7. 7. Faster Full Table Scans Data Warehousing Features In Oracle 1. Full Table Scans are common in data warehousing environments, and with this in mind table scan performance has been improved in Oracle. 2. Code optimisation in Oracle Database 10g has decreased CPU consumption and this leads to faster table scan execution (when queries are CPU bound, rather than I/O bound), and gives the potential for greater query concurrency, offering up to 30-40% speed improvements when comparing CPU-bound queries. 3. Improvements in the handling of large numbers of partitions, improvements to the optimizer CPU cost model, automatic statistics gathering, and partition-aware materialized view refreshes.
  8. 8. 8. Automatic Tuning And Maintenance Data Warehousing Features In Oracle 1. Automatic maintenance and tuning has always been one of the key product differentiators for Microsoft SQL Server and with Oracle 10g, features that meet and match those found in competitor products are being introduced to the server technology stack. 2. The first component in this framework is the Automatic Workload Repository, which uses an enhanced version of Statspack to collect instance statistics every thirty minutes and stores these for a rolling seven day period. 3. The usage information stored in the Automatic Workload Repository is then used as the basis for all the self-management functionality in Oracle Database 10g. 4. Automatic Maintenance Tasks feature, which acts on the statistics gathered by the Automatic Workload Repository, and carries out tasks such as index rebuilding, refreshing statistics, and so on, where such tasks don't require any manual intervention by the DBA. 5. The third component of the self-managing framework is 'Server Generated Alerts', a method where the database server sends notifications via email to the DBA - including a recommendation as to how best to deal with the situation.
  9. 9. 9. Asynchronous Change Data Capture Data Warehousing Features In Oracle 1. Oracle Change Data Capture was introduced with Oracle , and provided the ability to track changes to tables and store them in a change table, for further consumption by an ETL process. 2. Oracle Change Data Capture worked by creating triggers on the source tables, transferring data synchronously but creating a processing overhead and requiring access to the structure of the source tables. 3. Oracle introduces Asynchronous Change Data Capture, which instead of using triggers uses the database log files to capture changes and apply them to collection tables.
  10. 10. 10. Improvements To Oracle Data Mining Data Warehousing Features In Oracle 1. Alongside the inclusion of the Oracle Express multidimensional OLAP engine, Oracle 9i also embedded data mining functionality in the database together, and this data mining functionality has been enhanced with Oracle Database. 2. Oracle Database adds support for two new classification routines, 'Support Vector Machine' (used for top-down rather than a bottom-up calculations, assuming the best possible fit and then working backwards to what can be achieved) and Non-Negative Matrix Factorisation, together with support for Frequent Itemsets. 3. Used for such functions as market basket analysis and propensity analysis.