There are several key differences between data warehouses and OLTP systems that have significant impacts on backup and recovery.

These differences are:

1. A data warehouse is typically much larger than an OLTP system. Data warehouses over 10's of terabytes are not uncommon and the largest data warehouses grow to orders of magnitude larger. Thus, scalability is a particularly important consideration for data warehouse backup and recovery.

2. A data warehouse often has lower availability requirements than an OLTP system. While data warehouses are critical to businesses, there is also a significant cost associated with the ability to recover multiple terabytes in a few hours compared to recovering in a day. Some organizations may determine that in the unlikely event of a failure requiring the recovery of a significant portion of the data warehouse, they may tolerate an outage of a day or more if they can save significant expenditures in backup hardware and storage.

3. A data warehouse is typically updated through a controlled process called the ETL (Extract, Transform, Load) process, unlike in OLTP systems where users are modifying data themselves. Because the data modifications are done in a controlled process, the updates to a data warehouse are often known and reproducible from sources other than redo logs.

4. A data warehouse contains historical information, and often, significant portions of the older data in a data warehouse are static. For example, a data warehouse may track five years of historical sales data. While the most recent year of data may still be subject to modifications (due to returns, restatements, and so on), the last four years of data may be entirely static. The advantage of static data is that it does not need to be backed up frequently.

These four characteristics are key considerations when devising a backup and recovery strategy that is optimized for data warehouses.

Before you begin to think seriously about a backup and recovery strategy, the physical data structures relevant for backup and recovery operations must be identified.

These components include the files and other structures that constitute data for an Oracle data store and safeguard the data store against possible failures. Three basic components are required for the recovery of Oracle Database:

• Data files

• Redo Logs

• Control Files

Backups are divided into physical backups and logical backups.

• Physical backups are backups of the physical files used in storing and recovering your database, such as data files, control files, and archived redo logs. Ultimately, every physical backup is a copy of files storing database information to some other location, whether on disk or offline storage, such as tape.

• Logical backups contain logical data (for example, tables or stored procedures) extracted from a database with Oracle Data Pump (export/import) utilities. The data is stored in a binary file that can be imported into Oracle Database.

Physical backups are the foundation of any backup and recovery strategy. Logical backups are a useful supplement to physical backups in many circumstances but are not sufficient protection against data loss without physical backups.

Reconstructing the contents of all or part of a database from a backup typically involves two phases: retrieving a copy of the data file from a backup, and reapplying changes to the file since the backup, from the archived and online redo logs, to bring the database to the desired recovery point in time. To restore a data file or control file from backup is to retrieve the file from the backup location on tape, disk, or other media, and make it available to Oracle Database. To recover a data file, is to take a restored copy of the data file and apply to it the changes recorded in the database's redo logs. To recover a whole database is to perform recovery on each of its data files.

Oracle Database provides several tools to manage backup and recovery of Oracle Databases.

Each tool gives you a choice of several basic methods for making backups. The methods include:

• Oracle Recovery Manager (RMAN)

  RMAN reduces the administration work associated with your backup strategy by maintaining an extensive record of metadata about all backups and needed recovery-related files. In restore and recovery operations, RMAN uses this information to eliminate the need for the user to identify needed files. RMAN is efficient, supporting file multiplexing and parallel streaming, and verifies blocks for physical and (optionally) logical corruptions, on backup and restore.

  Backup activity reports can be generated using V$BACKUP views.

• Oracle Data Pump

  Oracle Data Pump provides high speed, parallel, bulk data and metadata movement of Oracle Database contents. This utility makes logical backups by writing data from Oracle Database to operating system files. This data can later be imported into Oracle Database.

• User-Managed Backups

  The database is backed up manually by executing commands specific to your operating system.

A Recovery Time Objective (RTO) is the time duration in which you want to be able to recover your data.

Your backup and recovery plan should be designed to meet RTOs your company chooses for its data warehouse. For example, you may determine that 5% of the data must be available within 12 hours, 50% of the data must be available after a complete loss of the database within 2 days, and the remainder of the data be available within 5 days. In this case you have two RTOs. Your total RTO is 7.5 days.

To determine what your RTO should be, you must first identify the impact of the data not being available. To establish an RTO, follow these four steps:

1. Analyze and identify: Understand your recovery readiness, risk areas, and the business costs of unavailable data. In a data warehouse, you should identify critical data that must be recovered in the n days after an outage.

2. Design: Transform the recovery requirements into backup and recovery strategies. This can be accomplished by organizing the data into logical relationships and criticality.

3. Build and integrate: Deploy and integrate the solution into your environment to back up and recover your data. Document the backup and recovery plan.

4. Manage and evolve: Test your recovery plans at regular intervals. Implement change management processes to refine and update the solution as your data, IT infrastructure, and business processes change.

A Recovery Point Objective, or RPO, is the maximum amount of data that can be lost before causing detrimental harm to the organization.

RPO indicates the data loss tolerance of a business process or an organization in general. This data loss is often measured in terms of time, for example, 5 hours or 2 days worth of data loss. A zero RPO means that no committed data should be lost when media loss occurs, while a 24 hour RPO can tolerate a day's worth of data loss.

This section contains the following topics:

• More Data Means a Longer Backup Window

• Divide and Conquer

BACKUP DATABASE NOT BACKED UP SINCE 'sysdate - 7' 
  PARTIAL DURATION 4:00 MINIMIZE TIME;

Archived redo logs are crucial for recovery when no data can be lost because they constitute a record of changes to the database.

Oracle Database can be run in either of two modes:

• ARCHIVELOG

  Oracle Database archives the filled online redo log files before reusing them in the cycle.

• NOARCHIVELOG

  Oracle Database does not archive the filled online redo log files before reusing them in the cycle.

Running the database in ARCHIVELOG mode has the following benefits:

• The database can be recovered from both instance and media failure.

• Backups can be performed while the database is open and available for use.

• Oracle Database supports multiplexed archive logs to avoid any possible single point of failure on the archive logs.

• More recovery options are available, such as the ability to perform tablespace point-in-time recovery (TSPITR).

• Archived redo logs can be transmitted and applied to the physical standby database, which is an exact replica of the primary database.

Running the database in NOARCHIVELOG mode has the following consequences:

• The database can be backed up only while it is closed after a clean shutdown.

• Typically, the only media recovery option is to restore the whole database to the point-in-time in which the full or incremental backups were made, which can result in the loss of recent transactions.

Many data warehouses, which were developed on earlier releases of Oracle Database, may not have integrated RMAN for backup and recovery.

However, just as there are many reasons to leverage ARCHIVELOG mode, there is a similarly compelling list of reasons to adopt RMAN. Consider the following:

1. Trouble-free backup and recovery

2. Corrupt block detection

3. Archive log validation and management

4. Block Media Recovery (BMR)

5. Easily integrates with Media Managers

6. Backup and restore optimization

7. Backup and restore validation

8. Downtime-free backups

9. Incremental backups

10. Extensive reporting

Enabling block change tracking allows incremental backups to be completed faster, by reading and writing only the changed blocks since the last full or incremental backup.

For data warehouses, this can be extremely helpful if the database typically undergoes a low to medium percentage of changes.

With the advent of big file tablespaces, data warehouses have the opportunity to consolidate a large number of data files into fewer, better managed data files.

For backing up very large data files, RMAN provides multisection backups as a way to parallelize the backup operation within the file itself, such that sections of a file are backed up in parallel, rather than backing up on a per-file basis.

For example, a one TB data file can be sectioned into ten 100 GB backup pieces, with each section backed up in parallel, rather than the entire one TB file backed up as one file. The overall backup time for large data files can be dramatically reduced.

An important issue facing a data warehouse is the sheer size of a typical data warehouse. Even with powerful backup hardware, backups may still take several hours.

One important consideration in improving backup performance is minimizing the amount of data to be backed up. Read-only tablespaces are the simplest mechanism to reduce the amount of data to be backed up in a data warehouse. Even with incremental backups, both backup and recovery are faster if tablespaces are set to read-only.

The advantage of a read-only tablespace is that data must be backed up only one time. If a data warehouse contains five years of historical data and the first four years of data can be made read-only, then theoretically the regular backup of the database would back up only 20% of the data. This can dramatically reduce the amount of time required to back up the data warehouse.

Most data warehouses store their data in tables that have been range-partitioned by time. In a typical data warehouse, data is generally active for a period ranging anywhere from 30 days to one year. During this period, the historical data can still be updated and changed (for example, a retailer may accept returns up to 30 days beyond the date of purchase, so that sales data records could change during this period). However, after data reaches a certain age, it is often known to be static.

By taking advantage of partitioning, users can make the static portions of their data read-only. Currently, Oracle supports read-only tablespaces rather than read-only partitions or tables. To take advantage of the read-only tablespaces and reduce the backup window, a strategy of storing constant data partitions in a read-only tablespace should be devised. Here are two strategies for implementing a rolling window:

1. When the data in a partition matures to the point where it is entirely static, implement a regularly scheduled process to move the partition from the current read-write tablespace to a tablespace that can then be made read-only.

2. Create a series of tablespaces, each containing a small number of partitions, and regularly modify a tablespace from read-write to read-only as the data in that tablespace ages.

One consideration is that backing up data is only half the recovery process. If you configure a tape system so that it can back up the read-write portions of a data warehouse in 4 hours, the corollary is that a tape system might take 20 hours to recover the database if a complete recovery is necessary when 80% of the database is read-only.

In general, a high priority for a data warehouse is performance. Not only must the data warehouse provide good query performance for online users, but the data warehouse must also be efficient during the extract, transform, and load (ETL) process so that large amounts of data can be loaded in the shortest amount of time. One common optimization used by data warehouses is to execute bulk-data operations using the NOLOGGING mode.

The database operations that support NOLOGGING modes are direct-path load and insert operations, index creation, and table creation. When an operation runs in NOLOGGING mode, data is not written to the redo log (or more precisely, only a small set of metadata is written to the redo log). This mode is widely used within data warehouses and can improve the performance of bulk data operations by up to 50%.

However, the tradeoff is that a NOLOGGING operation cannot be recovered using conventional recovery mechanisms, because the necessary data to support the recovery was never written to the log file. Moreover, subsequent operations to the data upon which a NOLOGGING operation has occurred also cannot be recovered even if those operations were not using NOLOGGING mode. Because of the performance gains provided by NOLOGGING operations, it is generally recommended that data warehouses use NOLOGGING mode in their ETL process.

The presence of NOLOGGING operations must be taken into account when devising the backup and recovery strategy. When a database is relying on NOLOGGING operations, the conventional recovery strategy (of recovering from the latest tape backup and applying the archived log files) is no longer applicable because the log files are not able to recover the NOLOGGING operation.

The first principle to remember is, do not make a backup when a NOLOGGING operation is occurring. Oracle Database does not currently enforce this rule, so DBAs must schedule the backup jobs and the ETL jobs such that the NOLOGGING operations do not overlap with backup operations.

There are two approaches to backup and recovery in the presence of NOLOGGING operations: ETL or incremental backups. If you are not using NOLOGGING operations in your data warehouse, then you do not have to choose either option: you can recover your data warehouse using archived logs. However, the options may offer some performance benefits over an archive log-based approach for a recovery. You can also use flashback logs and guaranteed restore points to flashback your database to a previous point in time.

This section contains the following topics:

• Extract, Transform, and Load

• The Extract, Transform, and Load Strategy

• Incremental Backup

• The Incremental Approach

• Flashback Database and Guaranteed Restore Points

Not all of the tablespaces in a data warehouse are equally significant from a backup and recovery perspective.

Database administrators can use this information to devise more efficient backup and recovery strategies when necessary. The basic granularity of backup and recovery is a tablespace, so different tablespaces can potentially have different backup and recovery strategies.

On the most basic level, temporary tablespaces never need to be backed up (a rule which RMAN enforces). Moreover, in some data warehouses, there may be tablespaces dedicated to scratch space for users to store temporary tables and incremental results. These tablespaces are not explicit temporary tablespaces but are essentially functioning as temporary tablespaces. Depending upon the business requirements, these tablespaces may not need to be backed up and restored; instead, for a loss of these tablespaces, the users would re-create their own data objects.

In many data warehouses, some data is more important than other data. For example, the sales data in a data warehouse may be crucial and in a recovery situation this data must be online as soon as possible. But, in the same data warehouse, a table storing clickstream data from the corporate website may be much less critical to businesses. The business may tolerate this data being offline for a few days or may even be able to accommodate the loss of several days of clickstream data if there is a loss of database files. In this scenario, the tablespaces containing sales data must be backed up often, while the tablespaces containing clickstream data need to be backed up only once every week or two weeks.

While the simplest backup and recovery scenario is to treat every tablespace in the database the same, Oracle Database provides the flexibility for a DBA to devise a backup and recovery scenario for each tablespace as needed.