Overview of High Availability Features
Computing environments configured to provide nearly full-time availability are know as high availability systems. Such systems typically have redundant hardware and software that makes the system available despite failures. Well-designed high availability systems avoid having single points-of-failure.
When failures occur, the fail over process moves processing performed by the failed component to the backup component. This process remasters systemwide resources, recovers partial of failed transactions, and restores the system to normal, preferably within a matter of microseconds. The more transparent that fail over is to users, the higher the availability of the system.
Oracle has a number of products and features that provide high availability in cases of unplanned downtime or planned downtime. These include Fast-Start Fault Recovery, Real Application Clusters, Recover Manager (RMAN), backup and recovery solutions, Oracle Flashback, partitioning, Oracle Data Guard, LogMiner, multiplexed redo log files, online reorganization. These can be used in various combinations to meet specific high availability needs.
Overview of Business Intelligence Features
Data Warehousing A data warehouse is a relational database designed for query and analysisrather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, and online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering in to business users.
Extraction, Transformation, and Loading (ETL) You must load your data warhouse regularly so that it can serve its purpose of facilitating business analysis. To do this, data from one or more operational systems must be extracted and copied into the warehouse. The process of extracting data from source systems and bringing it into the data warehouse is commonly called ETL, which stands for extraction, transformation, and loading.
Materialized Views A materialized view provides access to table data by storing the results of a query in a separarte schema object. Unlike an ordinary view, which does not take up any storage space or contain any data, a materialized view contains the rows resulting from a query against one or more base tables or views. A materialized view can be stored in the same database as its base tables or in a different database.
Materialized views stored in the same database as their base tables can improve query performance through query rewrites. Query rewrite is a mechanism where Oracle or applications from the end user or database transparently improve query response time, by automatically rewriting the SQL query to use the materialized view instead of accessing the original tables. Query rewrites are particularly useful in a data warhouse environment.
Bitmap Indexes in Data Warehousing Data warehousing environments typically have large amounts of data and hoc queries, but a low level of concurrent database manipulation language(DML) transactions. For such applications, bitmap indexing provides:
- Reduced response time for large classes of ad hoc queries
- Reduced storage requirements compared to other indexing techniques
- Dramtic performance gains even on hardware with a relatively small number of CPUs or a small amount of memory
- Efficient maintenance during parallel DML and loads
Table Compression To reduce disk use and memory use (specifically, the buffer cache), you can store tables and partitioned tables in a compressed format inside the database. This often leads to a better scaleup for read-only operations. Table compression can also speed up query execution. There is, however, a slight cost in CPU overhead.
Parallel Execution When Oracle runs SQL statements in parallel, multiple processes work together simultaneously to run a single SQL statement. By dividing the work necessary to run a statement among multiple processes, Oracle can run the statement more quickly than if only a single process ran it. This is called parallel execution or parallel processing.
Parallel execution dramatically reduces response time for data-intensive operations on large databases, because statement processing can be split up among many CPUs on a single Oracle system.
Analytic SQL Oracle has many SQL operations for performing analytic operations in the database. These include ranking, moving averages, cumulative sums, ration-to-reports, and period-over-period comparisons.
OLAP Capabilities Application developers can use SQL online analytical processing(OLAP) functions for standard and ad-hoc reporting. For additional analytic functionality, Oracle OLAP provides multidimensional calculations, forecasting, modeling, and what-if scenarios. This enables developers to build sophisticated analytic and planning applications such as sales and marketing analysis, enterprise budgeting and financial analysis, and demand planning systems. Data can be stored in either relational tables or multidimensional objects.
Oracle OLAP provides the query performance and calculation capability previously found only in multidimensional database to Oracle's relational platform. In addition, it provides a Java OLAP API that is appropriate for the development of internet-ready analytical applications. Unlike other combinations of OLAP and RDBMS technology, Oracle OLAP is not a multidimensional database using bridges to move data from the relational data store to a multidimensional data store. Instead, it is truly an OLAP-enabled relational database. As a result, Oracle provides the benefits of a multidimensional database along with the scalability, accessibility, security, manageability, and high availability of the Oracle database. The Java OLAP API, which is specifically designed for internet-based analytical applications, offers productive data access.
Data Mining With Oracle Data Mining, data never leaves the database - the data, data preparation, model building, and model scoring results all remain in the database. This enables Oracle to provide an infrastructure for application developers to integrate data mining seamlessly with database applications. Some typical examples of the applications that data mining are used in are call centers, ATMs, ERM, and business planning applications. Data mining functions such as model building, testing, and scoring are provided through a Java API.
Partitioning Partitioning addresses key issues in supporting very large tables and indexes by letting you decompose them into smaller and more manageable pieces called partitions. SQL queries and DML statements do not need to be modified in order to access and manipulate individuals partitions rather than entire tables or indexes. This is how partitioning can simplify the manageability of large database objects. Also, partitioning is entirely transparent to applications.
Partitioning is useful for many different types of applications, particularly applications that manage large volumes of data. OLTP systems often benefit from improvements in manageability and availability, while data warehousing systems benefit from performance and manageability.