Data Warehousing Fundamentals A Comprehensive Guide for IT Professionals phần 10 pot

prove funding for the full data warehouse. You define the scope of the pilot based on the audience. Regardless of the scope, this type of pilot must provide a sampling of all the major features to show the utility of the warehouse and how easy it is to get information. You focus on the interaction of the information delivery system with the users. Proof-of- concept pilots work with limited amounts of data. The project team thinks of this type of pilot much earlier in the development scheme. Do not take an inordinately long time. The main goal here is to impress on the minds of the users that the data warehouse is a very effective means for information delivery. Gen- erally, no proof-of-concept pilot should take longer than six months to build and explore. You need to keep the focus on effectively presenting the concepts and quickly gaining the approval. Proof-of-Technology Pilot. This is perhaps the simplest and easy to build, but as it is mainly built for IT to prove one or two technologies at a time, this type of pilot is of lit- tle interest to the users. You may just want to test and prove a dimensional modeling tool or a data replication tool. Or, you may want to prove the validity and usefulness of the ETL tools. In this type of pilot, you want to get beyond the product demonstrations and claims of the vendors and look for yourself. The utility of the proof-of-technology pilots lies in your ability to concentrate and fo- cus on one or two technologies and prove them to your satisfaction. You can check the ap- plicability of a particular type of replication tool to your data warehouse environment. However, as the pilot is confined to proving a small part of the collection of all the tech- nologies, it does not indicate anything about how all the pieces will work together. This brings us to the next type. Comprehensive Test Pilot. This is developed and deployed to verify that all the in- frastructure and architectural components work together and well. It is not as complete in scope as a full-fledged data warehouse and works with a smaller database, but you verify the data flow throughout the data warehouse from all the source operational systems through the staging area to the information delivery component. This pilot enables the IT professionals and the users on the project team to appreciate the complexities of the data warehouse. The team gains experience with the new technolo- gies and tools. This pilot cannot be put together and deployed within a short time. The scope of the pilot encompasses the entire spectrum of data warehouse functions. It is also deployed to benefit the project team more than the users. User Tool Appreciation Pilot. The thrust of this type of pilot is to provide the users with tools they will see and use. You place the emphasis on the end-user information de- livery tools. In this type of pilot, you keep the data content and the data accuracy in the background. The focus is just on the usability of the tools. The users are able to observe all the features of the end-user tools for themselves, work with them, and appreciate their features and utility. If different tool sets are provided to different groups of users, you have to deploy a few versions of this type of pilot. Note that there is little regard for the integrity of the data, nor does this type of pilot work with the entire data content of the data warehouse. User tool appreciation pilots have rather limited applications. One area where this type is more useful is in the OLAP sys- tem. 464 DATA WAREHOUSE DEPLOYMENT Broad Business Pilot. In contrast to the previous type, this type of pilot has a broad- er business scope. Try to understand how this type of pilot gets started. Management iden- tifies some pressing need for decision support in some special business. They are able to define the requirements fairly well. If something is put together to meet the requirements, the potential for success is great. Management wants to take advantage of the data ware- housing initiatives in the organization. The responsibility rests on the project team to come up with this highly visible early deliverable business pilot. This type of pilot based on a specific set of requirements has a few problems. First, you are under time pressure. Depending on the requirements, the scope of the pilot could be too narrow to get integrated with the rest of the data warehouse later on. Or, the pilot could turn out to be too complex. A complex project cannot be considered as a pilot. Expandable Seed Pilot. First, note the motivations for this type of pilot. You want to come up with something with business value. The scope must be manageable. You want to keep it as technically simple as possible for the users. Nevertheless, you have a choice of suitable simple subjects. Simple does not mean useless. Choose a simple, useful, and fair- ly visible business area but plan to go through the length and breadth of the data ware- house features with the pilot. This is like planting a good seed and watching it germinate and then grow. The project team benefits from such a pilot because they will observe and test the working of the various parts. Users gain an appreciation of the tools and understand how they interact with the data warehouse. The data warehouse administration function may also be tested. Choosing the Pilot Please understand that there is no industry-standard naming convention for the pilot types. One data warehouse practitioner may call a specific type an infrastructure test pilot and another an architectural planning pilot. The actual names do not matter. The scope, con- tent, and motivations count. Also note that these groupings or types are arbitrary. You may very well come up with another four types. However, the major thrust of any pilot comes from the same motivations as one of the types described above. Remember that no actual pilot falls exclusively within one specific type. You will see traces of many types in the pi- lot you want to adopt. As the project team is building the data warehouse, it is introducing the new decision support system in a particular technical and business environment. The technical and business environment of the organization influences the choice of the pilot. Again, the choice also depends on whether the data warehouse project is primarily IT- driven, user-driven, or driven by a truly joint team. Let us examine the conditions in the organization and determine if we can match them with the type of pilot that is suitable. Please study the guidelines described below. If your organization is totally new to the concept of data warehousing and your senior management needs convincing and first-hand proof, adopt a proof-of concept pilot. But most companies are not in this condition. With so much literature, seminars, and vendor presentations about data warehousing, practically everyone is at least partially sold on the concept. The only question may be the applicability of the concept to your organization. Proof-of-technology and comprehensive test pilots serve the needs of IT. Users do not gain directly from these two types. If you are expanding your current infrastructure exten- CONSIDERATIONS FOR A PILOT 465 sively to accommodate the data warehouse, and if you are adopting new parallel process- ing hardware and MOLAP techniques, then these two types merit your consideration. The importance of user involvement and user training in a data warehouse cannot be overstated. The more the users gain an appreciation of the data warehouse and its benefits, the better it is for the success of the project. Therefore, the user tool appreciation pilot and the broad business pilot pose substantial advantages. Although the user tool appreciation pilot is very limited in scope and application, it has its place. Usually it is a throw-away pilot. It cannot be integrated into the main warehouse deployment but it can continue to be used as a training tool. A word about the broad business pilot: this type has the potential for great success and can elevate the data warehouse project in the eyes of the top man- agement, but be careful not to bite off more than you can chew. If the scope is too com- plex and large, you could risk failure. At first blush, the expandable seed pilot appears to be the best choice. Although the users and the project team can both benefit from this type of pilot because of its con- trolled and limited scope, this pilot may not traverse all the functions and features. But a pilot is not really meant to be elaborate. It serves its purpose well if it touches all the im- portant functions. Expanding and Integrating the Pilot The question arises about what you do with a pilot after it has served its intended primary purpose. What exactly is the purpose and shelf-life of a pilot? Do you have to throw the pi- lot away? Is all the effort expended on a pilot completely wasted? Not so. Every pilot has specific purposes. You build and deploy a pilot to achieve certain defined results. The proof-of-concept pilot has one primarily goal, and one goal only—prove the validity of the 466 DATA WAREHOUSE DEPLOYMENT INITIAL DEPLOYMENT OF THE DATA WAREHOUSE Proof-of- Concept Proof-of- Technology Comprehensive Test User Tool Appreciation Broad Business Expandable Seed Small-scale, works with limited data, not suitable for integration. PILOT TYPES Intended only to prove new technologies for IT. Manageable and simple, but designed for integration. Early deliverable with broader scope, may be integrated. Only intended for users to test and become familiar with tools. Only intended for IT to test all infrastructure/architecture. Figure 19-5 Integrating the pilot into the warehouse. data warehousing concept to the users and top management. If you are able to prove this proposition with the aid of the pilot, then the pilot is successful and serves its purpose. Understand the place of a pilot in the whole data warehouse development effort. A pi- lot is not the initial deployment. It may be a prelude to the initial deployment. Without too much modification, a pilot may be expanded and integrated into the overall data ware- house. Please see Figure 19-5 examining each type of pilot and illustrating how some may be integrated into the data warehouse. Note that the expandable seed pilot stands out as the best candidate for integration. In each case, observe what needs to be done for integra- tion. SECURITY A data warehouse is a veritable gold mine of information. All of the organization’s critical information is readily available in a format easy to retrieve and use. In a single operational system, security provisions govern a smaller segment of the corporate data but data ware- house security extends to a very large portion of the enterprise data. In addition, the secu- rity provisions must cover all the information that is extracted from the data warehouse and stored in other data areas such as the OLAP system. In an operational system, security is ensured by authorizations to access the database. You may grant access to users by individual tables or through database views. Access re- strictions are difficult to set up in a data warehouse. The analyst at a data warehouse may start an analysis by getting information from one or two tables. As the analysis continues, more and more tables are involved. The entire query process is mainly ad hoc. Which ta- bles must you restrict and which ones must you keep open to the analyst? Security Policy The project team must establish a security policy for the data warehouse. If you have a se- curity policy for the organization to govern the enterprise information assets, then make the security policy for the data warehouse an add-on to the corporate security policy. First and foremost, the security policy must recognize the immense value of the information contained in the data warehouse. The policy must provide guidelines for granting privi- leges and for instituting user roles. Here are the usual provisions found in the security policy for data warehouses: ț The scope of the information covered by the policy ț Physical security ț Security at the workstation ț Network and connections ț Database access privileges ț Security clearance for data loading ț User roles and privileges ț Security at levels of summarization ț Metadata security ț OLAP security SECURITY 467 ț Web security ț Resolution of security violations Managing User Privileges As you know, users are granted privileges for accessing the databases of OLTP systems. The access privilege relates to individuals or groups of users with rights to perform the operations to create, read, update, or delete data. The access restrictions for these opera- tions may be set at the level of entire tables or at the level of one or more columns of an in- dividual table. Most RDBMSs offer role-based security. As you know, a role is just a grouping of users with some common requirements for accessing the database. You can create roles by executing the appropriate statements using the language component of the database man- agement system. After creating the roles, you can set up the users in the appropriate roles. Access privileges may be granted at the level of a role. When this is done, all the users as- signed to that role receive the same access privileges granted at the role level. Access priv- ileges may also be granted at the individual user level. How do you handle exceptions? For example, let us say user JANE is part of the role ORDERS. You have granted a certain set of access privileges to the role ORDERS. Al- most all these access privileges apply to JANE with one exception. JANE is allowed to ac- cess one more table, namely, the promotion dimension table. How do you work out this exception? You separately grant privilege to JANE to access the promotion table. From this granting of the additional privilege, JANE can access the promotion table. For every- thing else, JANE derives the privileges from the role ORDERS. Figure 19-6 presents a sample set of roles, responsibilities, and privileges. Please ob- 468 DATA WAREHOUSE DEPLOYMENT Run queries and reports against data warehouse tables Query Tool Specialists Data Warehouse Administration End-Users Power Users / Analysts Helpdesk / Support Center Security Administration ROLES RESPONSIBILITIES ACCESS PRIVILEGES System: none; Database Admin: none; Tables and Views : selected Install and maintain DBMS; provide backup and recovery System : yes; Database Admin: yes; Tables and Views : all Run ad hoc complex queries, design and run reports System : none; Database Admin: none; Tables and Views : all Help user with queries and reports; analyze and explain System : none; Database Admin: none; Tables and Views : all Install and trouble-shoot end- user and OLAP tools System : none; Database Admin: none; Tables and Views : all Grant and revoke access privileges; monitor usage System : yes; Database Admin: yes; Tables and Views : all Systems / Network Admin Install and maintain Operating Systems and networks System : yes; Database Admin: no; Tables and Views : none Figure 19-6 Roles, responsibilities, and privileges. serve the responsibilities as they relate to the data warehousing environment. Also, please notice how the privileges match up with the responsibilities. Password Considerations Security protection in a data warehouse through passwords is similar to how it is done in operational systems. Updates to the data warehouse happen only through the data load jobs. User passwords are less relevant to the batch load jobs. Deletes of the data ware- house records happen infrequently. Only when you want to archive old historical records do the batch programs delete records. The main issue with passwords is to authorize users for read-only data access. Users need passwords to get into the data warehouse environ- ment. Security administrators can set up acceptable patterns for passwords and also the ex- piry period for each password. The security system will automatically expire the password on the date of expiry. A user may change to a new password when he or she receives the initial password from the administrator. The same must be done just before the expiry of the current password. These are additional security procedures. Follow the standards for password patterns in your company. Passwords must be cryp- tic and arbitrary, not easily recognizable. Do not let your users have passwords with their own names or the names of their loved ones. Do not let users apply their own exotic pat- terns. Have a standard for passwords. Include text and numeric data within a password. The security mechanism must also record and control the number of unauthorized at- tempts by users to gain access with invalid passwords. After a prescribed number of unau- thorized attempts, the user must be suspended from the data warehouse until the adminis- trator reinstates the user. Following a successful sign-on, the numbers of illegal attempts must be displayed. If the number is fairly high, this must be reported. It could mean that someone is trying to work at a user workstation while that user is not there. Security Tools In the data warehouse environment, the security component of the database system itself is the primary security tool. We have discussed role-based security provided by the DBMSs. Security protection goes down to the level of columns in most commercial data- base management systems. Some organizations have third-party security and management systems installed to govern the security of all systems. If this is the case in your organization, take advantage of the installed security system and bring the data warehouse under the larger security umbrella. Such overall security systems provide the users with a single sign-on feature. A user then needs only one sign-on user-id and password for all the computer systems in the organization. Users need not memorize multiple sign-ons for individual systems. Some of the end-user tools come with their own security system. Most of the OLAP tools have a security feature within the toolset. Tool-based security is usually not as flexi- ble as the security provided in the DBMS. Nevertheless, tool-based security can form some part of the security solution. Once you set the users up on the security systems in the toolset, you need not repeat it at the DBMS level, but some data warehouse teams go for double protection by invoking the security features of the DBMS also. The tool-based security, being an integral part of the toolset, cannot be suspended. Just to get into the toolset for accessing the data, you need to get security clearance from the SECURITY 469 toolset software. If you are already planning to use the DBMS itself for security protec- tion, then tool-based security may be considered redundant. Each set of tools from a cer- tain vendor has its own way of indicating information interfaces. Information is organized into catalogs, folders, and items as the hierarchy. You may provide security verification at any of the three levels. BACKUP AND RECOVERY You are aware of the backup and recovery procedures in OLTP systems. Some of you, as database administrators, must have been responsible for setting up the backups and proba- bly been involved in one or two disaster recoveries. In an OLTP mission-critical system, loss of data and downtime cannot be tolerated. Loss of data can produce serious consequences. In a system such as airlines reservations or online order-taking, downtime even for a short duration can cause losses in the millions of dollars. How critical are these factors in a data warehouse environment? When an online order- taking system is down for recovery, you probably can survive for a few hours using manu- al fall-back procedures. If an airlines reservation system is down, there can be no such manual fall-back. How do these compare with the situation in the data warehouse? Is downtime critical? Can the users tolerate a small loss of data? Why Back Up the Data Warehouse? A data warehouse houses huge amounts of data that has taken years to gather and accu- mulate. The historical data may go back 10 or even up to 20 years. Before the data arrives at the data warehouse, you know that it has gone through an elaborate process of cleans- ing and transformation. Data in the warehouse represents an integrated, rich history of the enterprise. The users cannot afford to lose even a small part of the data that was so painstakingly put together. It is critical that you are able to recreate the data if and when any disaster happens to strike. When a data warehouse is down for any length of time, the potential losses are not as apparent as in an operational system. Order-taking staff is not waiting for the system to come back up. Nevertheless, if the analysts are in the middle of a crucial sales season or racing against time to conduct some critical analytical studies, the impact could be more pronounced. Observe the usage of a data warehouse. Within a short time after deployment, the num- ber of users increases rapidly. The complexity of the types of queries and analysis sessions intensifies. Users begin to request more and more reports. Access through Web technolo- gy expands. Very quickly, the data warehouse gains almost mission-critical status. With a large number of users intimately dependent on the information from the warehouse, back- ing up the data content and ability to recover quickly from malfunctions reaches new heights of importance. In an OLTP system, recovery requires the availability of backed up versions of the data. You proceed from the last backup and recover to the point where the system stopped working. But you might think that the situation in a data warehouse differs from that in an OLTP system. The data warehouse does not represent an accumulation of data directly through data entry. Did not the source operational systems produce the data feeds in the 470 DATA WAREHOUSE DEPLOYMENT first place? Why must you bother to create backups of the data warehouse contents? Can you not reextract and reload the data from the source systems? Although this appears to be a natural solution, it is almost always impractical. Recreation of the data from the source systems takes enormous lengths of time and your data warehouse users cannot tolerate such long periods of downtime. Backup Strategy Now that you have perceived the necessity to back up the data warehouse, several ques- tions and issues arise. What parts of the data must be backed up? When to back up? How to back up? Formulate a clear and well-defined backup and recovery strategy. Although the strategy for the data warehouse may have similarities to that for an OLTP system, still you need a separate strategy. You may build on the one that is available in your organiza- tion for OLTP systems, but do take into account the special needs for this new environ- ment. A sound backup strategy comprises several crucial factors. Let us go over some of them. Here is a collection of useful tips on what to include in your backup strategy: ț Determine what you need to back up. Make a list of the user databases, system data- bases, and database logs. ț The enormous size of the data warehouse stands out as a dominant factor. Let the factor of the size govern all decisions in backup and recovery. The need for good performance plays a key role. ț Strive for a simple administrative setup. ț Be able to separate the current from the historical data and have separate procedures for each segment. The current segment of live data grows with the feeds from the source operational systems. The historical or static data is the content from the past years. You may decide to back up historical data less frequently. ț Apart from full backups, also think of doing log file backups and differential backups. As you know, a log file backup stores the transactions from the last full backup or picks up from the previous log file backup. A variation of this is a full differential backup. A differential backup contains all the changes since the last full backup. ț Do not overlook backing up system databases. ț Choosing the medium for backing up is critical. Here, size of the data warehouse dictates the proper choice. ț The commercial RDBMSs adopt a “container” concept to hold individual files. A container is a larger storage area that holds many physical files. The containers are known as table spaces, file groups, and the like. RDBMSs have special methods to back up the entire container more efficiently. Make use of such RDBMS features. ț Although the backup functions of the RDBMSs serve the OLTP systems, data ware- house backups need higher speeds. Look into backup and recovery tools from third- party vendors. ț Plan for periodic archiving of very old data from the data warehouse. A good archival plan pays off by reducing the time for backup and restore and also con- tributes to improvement in query performance. BACKUP AND RECOVERY 471 Setting up a Practical Schedule Without question, you need to back up the data warehouse properly. Many users will eventually depend on the data warehouse for constant flow of information. But the enor- mous size is a serious factor in all decisions about backup and recovery. It takes an inordi- nately long time to back up the full data warehouse. In the event of disasters, reextracting data from the source operational systems and reloading the data warehouse does not seem to be an option. So, how can you set up a practical schedule for backups? Consider the following issues for making the decisions: ț As you know, backups for OLTP systems usually run at night. But in the data ware- house environment, the night slots get allocated for the daily incremental loads. The backups will have to contend with the loads for system time. ț If your user community is distributed in different time zones, finding a time slot be- comes even more difficult. ț Mission-critical OLTP systems need frequent backups. In forward recovery, if you do not have regular full backups and frequent log file backups, the users must reen- ter the portion of the data that cannot be recovered. Compare this with the data warehouse. Reentering of data by the users does not apply here. Whatever portion cannot be recovered will have to be reloaded from the source systems if that is pos- sible. The data extraction and load systems do not support this type of recovery. ț Setting up a practical backup schedule comes down to these questions. How much downtime can the users tolerate before the recovery process is completed? How much data are the users willing to lose in the worst case scenario? Can the data warehouse continue to be effective for a long period until the lost data is somehow recovered? A practical backup schedule for your data warehouse certainly depends on the condi- tions and circumstances in your organization. Generally, a practical approach includes the following elements: ț Division of the data warehouse into active and static data ț Establishing different schedules for active and static data ț Having more frequent periodic backups for active data in addition to less frequent backups for static data ț Inclusion of differential backups and log file backups as part the backup scheme ț Synchronization of the backups with the daily incremental loads ț Saving of the incremental load files to be included as part of recovery if applicable Recovery Let us conclude with a few pointers on the recovery process. Before that, please refer to Figure 19-7 illustrating the recovery process in the data warehouse environment. Notice the backup files and how they are used in the recovery process. Also, note how the possi- bility of some loss of data exists. Here are a few practical tips: ț Have a clear recovery plan. List the various disaster scenarios and indicate how re- covery will be done in each case. 472 DATA WAREHOUSE DEPLOYMENT ț Test the recovery procedure carefully. Conduct regular recovery drills. ț Considering the conditions in your organization and the established recovery proce- dure, estimate an average downtime to be expected for recovery. Get a general agreement from the users about the downtime. Do not surprise the users when the first disaster strikes. Let them know that this is part of the whole scheme and that they need to be prepared if it should ever happen. ț In the case of each outage, determine how long it will take to recover. Keep the users properly and promptly informed. ț Generally, your backup strategy determines how recovery will be done. If you plan to include the possibility of recovering from the daily incremental load files, keep the backups of these files handy. ț If you have to go to the source systems to complete the recovery process, ensure that the sources will still be available. CHAPTER SUMMARY ț Deployment of the first version of the data warehouse follows the construction phase. ț Major activities in the deployment phase relate to user acceptance, initial loads, desktop readiness, initial training, and initial user support. ț Pilot systems are appropriate in several sets of circumstances. Common types of pi- lots are proof-of-concept, proof-of-technology, comprehensive test, user tool appre- ciation, broad business, and expandable seed. ț Although data security in a data warehouse environment is similar to security in OLTP systems, providing access privileges is more involved because of the nature of data access in the warehouse. CHAPTER SUMMARY 473 Backup of historical data Backup of current data Full refresh a few tables Log File backup Incremental Load System Crash TIMELINE File 1 File 2 File 3 A recovery option: Use these backup files File 1 File 2 File 3 Possible loss of data from the last incremental load Figure 19-7 Data warehouse: recovery. File 1 File 2 File 3 File 1 File 2 File 3 [...]... can tolerate interruptions Managing Data Growth Managing data growth deserves special attention In a data warehouse, unless you are vigilant about data growth, it could get out of hand very soon and quite easily Data warehouses already contain huge volumes of data When you start with a large volume of data, even a small percentage increase can result in substantial additional data In the first place,... USER TRAINING AND SUPPORT DATA CONTENT APPLICATIONS TOOLS Subjects available in the warehouse Predefined queries 483 Features and functions of the end-user tools Data warehouse dimension and fact tables Data warehouse navigation Data granularity and aggregate tables Source systems and data extractions Data transformation and cleansing rules Business terms and meanings Query templates Preformatted reports... cover all the requirements The staging area has been laid out well and it supports every function carried out there Loading of the data warehouse takes place without a flaw Your INTRANET WEB-ENABLED DATA WAREHOUSE Warehouse Data WEB PAGE STATISTICS AND INFORMATION Metadata Warehouse Subjects Warehouse Tables Summary Data Warehouse Navigation Warehouse Statistics Predefined Queries Predefined Reports Last... arbitrary Always checkpoint the load jobs It is a good practice to load the fact tables before loading the dimension tables Initial training of the users must include basic database and data storage concepts Role-based security provision is not suitable for the data warehouse 2 Prepare a plan for getting the user desktops ready for the initial deployment of your data warehouse The potential users are... physical data model and allocate storage Test initial and ongoing data loads Complete overall system test DEPLOYMENT Complete user acceptance documents Load initial data Install and test all the required software on the client machines Train initial set of users Grant authorizations for data warehouse access to various classes of users Establish backup and recovery procedures MAINTENANCE Establish... place, a data warehouse may contain too much historical data Data beyond 10 years may not produce meaningful results for many companies because of the changed business conditions End-users tend to opt for keeping detailed data at the lowest grain At least in the initial stages, the users continue to match results from the data warehouse with those from the operational systems Analysts produce many types... unnecessary drill-down functions and eliminate the corresponding detail level data ț Limit the volume of historical data Archive old data promptly ț Discourage analysts from holding unplanned summaries ț Where genuinely needed, create additional summary tables Storage Management As the volume of data increases, so does the utilization of storage Because of the huge data volume in a data warehouse, storage... and recovery management Web technology administration Platform upgrades Ongoing training User support Platform Upgrades Your data warehouse deployment platform includes the infrastructure, the data transport component, end-user information delivery, data storage, metadata, the database components, and the OLAP system components More often, a data warehouse is a comprehensive cross-platform environment... procedures to gather statistics for ongoing monitoring Fine-tune the data warehouse as necessary Complete materials for training of users and establish training schedule Establish user support structure Establish procedures for data warehouse management Upgrade infrastructure as needed from time to time Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals Paulraj Ponniah Copyright... Warehouse Data Monitoring Statistics Statistics Collection Sampling Statistics Collection Event-driven Sample data warehouse activity at specific intervals and gather statistics Record statistics whenever specified events take place and trigger statistics Review statistics for growth planning and performance tuning Figure 20-1 DATA WAREHOUSE ADMINISTRATION Data warehouse monitoring when an update takes place . huge amounts of data that has taken years to gather and accu- mulate. The historical data may go back 10 or even up to 20 years. Before the data arrives at the data warehouse, you know that it has. such manual fall-back. How do these compare with the situation in the data warehouse? Is downtime critical? Can the users tolerate a small loss of data? Why Back Up the Data Warehouse? A data warehouse. day MONITORING THE DATA WAREHOUSE 479 DATA WAREHOUSE ADMINISTRATION END - USERS DATA WAREHOUSE Warehouse Data Monitoring Statistics Statistics Collection Sampling Sample data warehouse activity at