A distinction should be made between alternatives to relational query languages and alternatives to SQL. Below are proposed relational alternatives to SQL. See navigational database for alternatives to relational:
.QL - object-oriented Datalog
4D Query Language (4D QL)
Datalog
HTSQL - URL based query method
IBM Business System 12 (IBM BS12) - one of the first fully relational database management systems, introduced in 1982
ISBL
Java Persistence Query Language (JPQL) - The query language used by the Java Persistence API in Java EE5 and Hibernate persistence library
LINQ
Object Query Language
QBE (Query By Example) created by Moshè Zloof, IBM 1977
Quel introduced in 1974 by the U.C. Berkeley Ingres project.
Tutorial D
XQuery
Showing posts with label DATA WAREHOUSE. Show all posts
Showing posts with label DATA WAREHOUSE. Show all posts
Sunday, April 3, 2011
Know About SQL
SQL was developed at IBM by Donald D. Chamberlin and Raymond F. Boyce in the early 1970s. This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM's original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s. The acronym SEQUEL was later changed to SQL because "SEQUEL" was a trademark of the UK-based Hawker Siddeley aircraft company.
The first Relational Database Management System (RDBMS) was RDMS, developed at MIT in the early 1970s, soon followed by Ingres, developed in 1974 at U.C. Berkeley. Ingres implemented a query language known as QUEL, which was later supplanted in the marketplace by SQL.
In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. Oracle V2 beat IBM's August release of the System/38 RDBMS to market by a few weeks.[citation needed]
After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.
The first Relational Database Management System (RDBMS) was RDMS, developed at MIT in the early 1970s, soon followed by Ingres, developed in 1974 at U.C. Berkeley. Ingres implemented a query language known as QUEL, which was later supplanted in the marketplace by SQL.
In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. Oracle V2 beat IBM's August release of the System/38 RDBMS to market by a few weeks.[citation needed]
After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.
A Little Bit about Cognos
Cognos was founded in 1969 by Alan Rushforth and Peter Glenister. Michael Potter joined Cognos in 1972. It began as a consulting company for the Canadian federal government and offered its first software product, QUIZ, in 1979. During the Canadian recession in the 1980s, Cognos shifted its focus from consulting to software sales.
In 1995, Ron Zambonini was named CEO and brought new marketing strategies. Cognos grew successful with its business intelligence products for local area networks (LAN), but new Internet technologies had come into the BI industry faster than the company had anticipated. After Cognos issued a notice informing shareholders of a slowdown in growth, many sold their shares, causing prices to fall quickly.
This prompted Cognos to build its web technology through the acquisition of other companies rather than internal research and development. In September 2007, Cognos announced that it would be acquiring Applix. It had previously acquired 4Thought, Relational Matters, LEX2000, Interweave, DecisionStream, NoticeCast, Adaytum, Frango, Databeacon and Celequest, an operational intelligence company.
In 1995, Ron Zambonini was named CEO and brought new marketing strategies. Cognos grew successful with its business intelligence products for local area networks (LAN), but new Internet technologies had come into the BI industry faster than the company had anticipated. After Cognos issued a notice informing shareholders of a slowdown in growth, many sold their shares, causing prices to fall quickly.
This prompted Cognos to build its web technology through the acquisition of other companies rather than internal research and development. In September 2007, Cognos announced that it would be acquiring Applix. It had previously acquired 4Thought, Relational Matters, LEX2000, Interweave, DecisionStream, NoticeCast, Adaytum, Frango, Databeacon and Celequest, an operational intelligence company.
Saturday, October 30, 2010
BI - Business Intelligence in Data warehouse
Business Intelligence is a term introduced by Howard Dresner of Gartner Group in 1989. He described Business Intelligence as a set of concepts and methodologies to improve decision making in business through use of facts and fact based systems. Over time as use of Business Intelligence has become mainstream more definitions of Business Intelligence have emerged. Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
Business intelligence applications can be:
* Mission-critical and integral to an enterprise's operations or occasional to meet a special requirement
* Enterprise-wide or local to one division, department, or project
* Centrally initiated or driven by user demand
Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
Business intelligence applications can be:
* Mission-critical and integral to an enterprise's operations or occasional to meet a special requirement
* Enterprise-wide or local to one division, department, or project
* Centrally initiated or driven by user demand
ETL - Extraction Transformation and Loading In Data warehosuing
The extract-transform-load (ETL) system, or more informally, the "back room," is often estimated to consume 70 percent of the time and effort of building a data warehouse. But there hasn't been enough careful thinking about just why the ETL system is so complex and resource intensive. Everyone understands the three letters: You get the data out of its original source location (E), you do something to it (T), and then you load it (L) into a final set of tables for the users to query.
When asked about breaking down the three big steps, many designers say, "Well, that depends." It depends on the source, it depends on funny data idiosyncrasies, it depends on the scripting languages and ETL tools available, it depends on the skills of the in-house staff, and it depends on the query and reporting tools the end users have.
The "it depends" response is dangerous because it becomes an excuse to roll your own ETL system, which in the worst-case scenario results in an undifferentiated spaghetti-mess of tables, modules, processes, scripts, triggers, alerts, and job schedules. Maybe this kind of creative design approach was appropriate a few years ago when everyone was struggling to understand the ETL task, but with the benefit of thousands of successful data warehouses, a set of best practices is ready to emerge.
I have spent the last 18 months intensively studying ETL practices and ETL products. I have identified a list of 38 subsystems that are needed in almost every data warehouse back room. That's the bad news. No wonder the ETL system takes such a large fraction of the data warehouse resources. But the good news is that if you study the list, you'll recognize almost all of them, and you'll be on the way to leveraging your experience in each of these subsystems as you build successive data warehouses.
The 38 Subsystems
1. Extract system. Source data adapters, push/pull/dribble job schedulers, filtering and sorting at the source, proprietary data format conversions, and data staging after transfer to ETL environment.
2. Change data capture system. Source log file readers, source date and sequence number filters, and CRC-based record comparison in ETL system.
3. Data profiling system. Column property analysis including discovery of inferred domains, and structure analysis including candidate foreign key — primary relationships, data rule analysis, and value rule analysis.
4. Data cleansing system. Typically a dictionary driven system for complete parsing of names and addresses of individuals and organizations, possibly also products or locations. "De-duplication" including identification and removal usually of individuals and organizations, possibly products or locations. Often uses fuzzy logic. "Surviving" using specialized data merge logic that preserves specified fields from certain sources to be the final saved versions. Maintains back references (such as natural keys) to all participating original sources.
5. Data conformer. Identification and enforcement of special conformed dimension attributes and conformed fact table measures as the basis for data integration across multiple data sources.
6. Audit dimension assembler. Assembly of metadata context surrounding each fact table load in such a way that the metadata context can be attached to the fact table as a normal dimension.
7. Quality screen handler. In line ETL tests applied systematically to all data flows checking for data quality issues. One of the feeds to the error event handler (see subsystem 8).
8. Error event handler. Comprehensive system for reporting and responding to all ETL error events. Includes branching logic to handle various classes of errors, and includes real-time monitoring of ETL data quality
9. Surrogate key creation system. Robust mechanism for producing stream of surrogate keys, independently for every dimension. Independent of database instance, able to serve distributed clients.
10. Slowly Changing Dimension (SCD) processor. Transformation logic for handling three types of time variance possible for a dimension attribute: Type 1 (overwrite), Type 2 (create new record), and Type 3 (create new field).
11. Late arriving dimension handler. Insertion and update logic for dimension changes that have been delayed in arriving at the data warehouse.
12. Fixed hierarchy dimension builder. Data validity checking and maintenance system for all forms of many-to-one hierarchies in a dimension.
13. Variable hierarchy dimension builder. Data validity checking and maintenance system for all forms of ragged hierarchies of indeterminate depth, such as organization charts, and parts explosions.
14. Multivalued dimension bridge table builder. Creation and maintenance of associative (bridge) table used to describe a many-to-many relationship between dimensions. May include weighting factors used for allocations and situational role descriptions.
15. Junk dimension builder. Creation and maintenance of dimensions consisting of miscellaneous low cardinality flags and indicators found in most production data sources.
16. Transaction grain fact table loader. System for updating transaction grain fact tables including manipulation of indexes and partitions. Normally append mode for most recent data. Uses surrogate key pipeline (see subsystem 19).
17. Periodic snapshot grain fact table loader. System for updating periodic snapshot grain fact tables including manipulation of indexes and partitions. Includes frequent overwrite strategy for incremental update of current period facts. Uses surrogate key pipeline (see subsystem 19).
18. Accumulating snapshot grain fact table loader. System for updating accumulating snapshot grain fact tables including manipulation of indexes and partitions, and updates to both dimension foreign keys and accumulating measures. Uses surrogate key pipeline (see subsystem 19).
19. Surrogate key pipeline. Pipelined, multithreaded process for replacing natural keys of incoming data with data warehouse surrogate keys.
20. Late arriving fact handler. Insertion and update logic for fact records that have been delayed in arriving at the data warehouse.
21. Aggregate builder. Creation and maintenance of physical database structures, known as aggregates, that are used in conjunction with a query-rewrite facility, to improve query performance. Includes stand-alone aggregate tables and materialized views.
22. Multidimensional cube builder. Creation and maintenance of star schema foundation for loading multidimensional (OLAP) cubes, including special preparation of dimension hierarchies as dictated by the specific cube technology.
23. Real-time partition builder. Special logic for each of the three fact table types (see subsystems 16, 17, and 18) that maintains a "hot partition" in memory containing only the data that has arrived since the last update of the static data warehouse tables.
24. Dimension manager system. Administration system for the "dimension manager" who replicates conformed dimensions from a centralized location to fact table providers. Paired with subsystem 25.
25. Fact table provider system. Administration system for the "fact table provider" who receives conformed dimensions sent by the dimension manager. Includes local key substitution, dimension version checking, and aggregate table change management.
26. Job scheduler. System for scheduling and launching all ETL jobs. Able to wait for a wide variety of system conditions including dependencies of prior jobs completing successfully. Able to post alerts.
27. Workflow monitor. Dashboard and reporting system for all job runs initiated by the Job Scheduler. Includes number of records processed, summaries of errors, and actions taken.
28. Recovery and restart system. Common system for resuming a job that has halted, or for backing out a whole job and restarting. Significant dependency on backup system (see subsystem 36).
29. Parallelizing/pipelining system. Common system for taking advantage of multiple processors, or grid computing resources, and common system for implementing streaming data flows. Highly desirable (eventually necessary) that parallelizing and pipelining be invoked automatically for any ETL process that meets certain conditions, such as not writing to the disk or waiting on a condition in the middle of the process.
30. Problem escalation system. Automatic plus manual system for raising an error condition to the appropriate level for resolution and tracking. Includes simple error log entries, operator notification, supervisor notification, and system developer notification.
31. Version control system. Consistent "snapshotting" capability for archiving and recovering all the metadata in the ETL pipeline. Check-out and check-in of all ETL modules and jobs. Source comparison capability to reveal differences between different versions.
32. Version migration system. development to test to production. Move a complete ETL pipeline implementation out of development, into test, and then into production. Interface to version control system to back out a migration. Single interface for setting connection information for entire version. Independence from database location for surrogate key generation.
33. Lineage and dependency analyzer. Display the ultimate physical sources and all subsequent transformations of any selected data element, chosen either from the middle of the ETL pipeline, or chosen on a final delivered report (lineage). Display all affected downstream data elements and final report fields affected by a potential change in any selected data element, chosen either in the middle of the ETL pipeline, or in an original source (dependency).
34. Compliance reporter. Comply with regulatory statutes to prove the lineage of key reported operating results. Prove that the data and the transformations haven't been changed. Show who has accessed or changed any such data.
35. Security system. Administer role-based security on all data and metadata in the ETL pipeline. Prove that a version of a module hasn't been changed. Show who has made changes.
36. Backup system. Backup data and metadata for recovery, restart, security, and compliance requirements.
37. Metadata repository manager. Comprehensive system for capturing and maintaining all ETL metadata, including all transformation logic. Includes process metadata, technical metadata, and business metadata.
38. Project management system. Comprehensive system for keeping track of all ETL development.
When asked about breaking down the three big steps, many designers say, "Well, that depends." It depends on the source, it depends on funny data idiosyncrasies, it depends on the scripting languages and ETL tools available, it depends on the skills of the in-house staff, and it depends on the query and reporting tools the end users have.
The "it depends" response is dangerous because it becomes an excuse to roll your own ETL system, which in the worst-case scenario results in an undifferentiated spaghetti-mess of tables, modules, processes, scripts, triggers, alerts, and job schedules. Maybe this kind of creative design approach was appropriate a few years ago when everyone was struggling to understand the ETL task, but with the benefit of thousands of successful data warehouses, a set of best practices is ready to emerge.
I have spent the last 18 months intensively studying ETL practices and ETL products. I have identified a list of 38 subsystems that are needed in almost every data warehouse back room. That's the bad news. No wonder the ETL system takes such a large fraction of the data warehouse resources. But the good news is that if you study the list, you'll recognize almost all of them, and you'll be on the way to leveraging your experience in each of these subsystems as you build successive data warehouses.
The 38 Subsystems
1. Extract system. Source data adapters, push/pull/dribble job schedulers, filtering and sorting at the source, proprietary data format conversions, and data staging after transfer to ETL environment.
2. Change data capture system. Source log file readers, source date and sequence number filters, and CRC-based record comparison in ETL system.
3. Data profiling system. Column property analysis including discovery of inferred domains, and structure analysis including candidate foreign key — primary relationships, data rule analysis, and value rule analysis.
4. Data cleansing system. Typically a dictionary driven system for complete parsing of names and addresses of individuals and organizations, possibly also products or locations. "De-duplication" including identification and removal usually of individuals and organizations, possibly products or locations. Often uses fuzzy logic. "Surviving" using specialized data merge logic that preserves specified fields from certain sources to be the final saved versions. Maintains back references (such as natural keys) to all participating original sources.
5. Data conformer. Identification and enforcement of special conformed dimension attributes and conformed fact table measures as the basis for data integration across multiple data sources.
6. Audit dimension assembler. Assembly of metadata context surrounding each fact table load in such a way that the metadata context can be attached to the fact table as a normal dimension.
7. Quality screen handler. In line ETL tests applied systematically to all data flows checking for data quality issues. One of the feeds to the error event handler (see subsystem 8).
8. Error event handler. Comprehensive system for reporting and responding to all ETL error events. Includes branching logic to handle various classes of errors, and includes real-time monitoring of ETL data quality
9. Surrogate key creation system. Robust mechanism for producing stream of surrogate keys, independently for every dimension. Independent of database instance, able to serve distributed clients.
10. Slowly Changing Dimension (SCD) processor. Transformation logic for handling three types of time variance possible for a dimension attribute: Type 1 (overwrite), Type 2 (create new record), and Type 3 (create new field).
11. Late arriving dimension handler. Insertion and update logic for dimension changes that have been delayed in arriving at the data warehouse.
12. Fixed hierarchy dimension builder. Data validity checking and maintenance system for all forms of many-to-one hierarchies in a dimension.
13. Variable hierarchy dimension builder. Data validity checking and maintenance system for all forms of ragged hierarchies of indeterminate depth, such as organization charts, and parts explosions.
14. Multivalued dimension bridge table builder. Creation and maintenance of associative (bridge) table used to describe a many-to-many relationship between dimensions. May include weighting factors used for allocations and situational role descriptions.
15. Junk dimension builder. Creation and maintenance of dimensions consisting of miscellaneous low cardinality flags and indicators found in most production data sources.
16. Transaction grain fact table loader. System for updating transaction grain fact tables including manipulation of indexes and partitions. Normally append mode for most recent data. Uses surrogate key pipeline (see subsystem 19).
17. Periodic snapshot grain fact table loader. System for updating periodic snapshot grain fact tables including manipulation of indexes and partitions. Includes frequent overwrite strategy for incremental update of current period facts. Uses surrogate key pipeline (see subsystem 19).
18. Accumulating snapshot grain fact table loader. System for updating accumulating snapshot grain fact tables including manipulation of indexes and partitions, and updates to both dimension foreign keys and accumulating measures. Uses surrogate key pipeline (see subsystem 19).
19. Surrogate key pipeline. Pipelined, multithreaded process for replacing natural keys of incoming data with data warehouse surrogate keys.
20. Late arriving fact handler. Insertion and update logic for fact records that have been delayed in arriving at the data warehouse.
21. Aggregate builder. Creation and maintenance of physical database structures, known as aggregates, that are used in conjunction with a query-rewrite facility, to improve query performance. Includes stand-alone aggregate tables and materialized views.
22. Multidimensional cube builder. Creation and maintenance of star schema foundation for loading multidimensional (OLAP) cubes, including special preparation of dimension hierarchies as dictated by the specific cube technology.
23. Real-time partition builder. Special logic for each of the three fact table types (see subsystems 16, 17, and 18) that maintains a "hot partition" in memory containing only the data that has arrived since the last update of the static data warehouse tables.
24. Dimension manager system. Administration system for the "dimension manager" who replicates conformed dimensions from a centralized location to fact table providers. Paired with subsystem 25.
25. Fact table provider system. Administration system for the "fact table provider" who receives conformed dimensions sent by the dimension manager. Includes local key substitution, dimension version checking, and aggregate table change management.
26. Job scheduler. System for scheduling and launching all ETL jobs. Able to wait for a wide variety of system conditions including dependencies of prior jobs completing successfully. Able to post alerts.
27. Workflow monitor. Dashboard and reporting system for all job runs initiated by the Job Scheduler. Includes number of records processed, summaries of errors, and actions taken.
28. Recovery and restart system. Common system for resuming a job that has halted, or for backing out a whole job and restarting. Significant dependency on backup system (see subsystem 36).
29. Parallelizing/pipelining system. Common system for taking advantage of multiple processors, or grid computing resources, and common system for implementing streaming data flows. Highly desirable (eventually necessary) that parallelizing and pipelining be invoked automatically for any ETL process that meets certain conditions, such as not writing to the disk or waiting on a condition in the middle of the process.
30. Problem escalation system. Automatic plus manual system for raising an error condition to the appropriate level for resolution and tracking. Includes simple error log entries, operator notification, supervisor notification, and system developer notification.
31. Version control system. Consistent "snapshotting" capability for archiving and recovering all the metadata in the ETL pipeline. Check-out and check-in of all ETL modules and jobs. Source comparison capability to reveal differences between different versions.
32. Version migration system. development to test to production. Move a complete ETL pipeline implementation out of development, into test, and then into production. Interface to version control system to back out a migration. Single interface for setting connection information for entire version. Independence from database location for surrogate key generation.
33. Lineage and dependency analyzer. Display the ultimate physical sources and all subsequent transformations of any selected data element, chosen either from the middle of the ETL pipeline, or chosen on a final delivered report (lineage). Display all affected downstream data elements and final report fields affected by a potential change in any selected data element, chosen either in the middle of the ETL pipeline, or in an original source (dependency).
34. Compliance reporter. Comply with regulatory statutes to prove the lineage of key reported operating results. Prove that the data and the transformations haven't been changed. Show who has accessed or changed any such data.
35. Security system. Administer role-based security on all data and metadata in the ETL pipeline. Prove that a version of a module hasn't been changed. Show who has made changes.
36. Backup system. Backup data and metadata for recovery, restart, security, and compliance requirements.
37. Metadata repository manager. Comprehensive system for capturing and maintaining all ETL metadata, including all transformation logic. Includes process metadata, technical metadata, and business metadata.
38. Project management system. Comprehensive system for keeping track of all ETL development.
Data Warehouse Architecture
A great listing found on it.toolbox.comA data warehouse architecture is primarily based on the business processes of a business enterprise taking into consideration the data consolidation across the business enterprise with adequate security, data modeling and organization, extent of query requirements, meta data management and application, warehouse staging area planning for optimum bandwidth utilization and full technology implementation.
The Data Warehouse Architecture includes many facets. Some of these are listed as follows:
1 Process Architecture
2 Data Model Architecture
3 Technology Architecture
4 Information Architecture
5 Resource Architecture
6 Various Architectures
7 More Resources
Process Architecture
Describes the number of stages and how data is processed to convert raw / transactional data into information for end user usage.
The data staging process includes three main areas of concerns or sub- processes for planning data warehouse architecture namely “Extract”, “Transform” and “Load”.
These interrelated sub-processes are sometimes referred to as an “ETL” process.
1)Extract- Since data for the data warehouse can come from different sources and may be of different types, the plan to extract the data along with appropriate compression and encryption techniques is an important requirement for consideration.
2)Transform- Transformation of data with appropriate conversion, aggregation and cleaning besides de-normalization and surrogate key management is also an important process to be planned for building a data warehouse.
3)Load- Steps to be considered to load data with optimization by considering the multiple areas where the data is targeted to be loaded and retrieved is also an important part of the data warehouse architecture plan.
Data Model Architecture
In Data Model Architecture (also known as Dimensional Data Model), there are 3 main data modeling styles for enterprise warehouses:
1.3rd Normal Form - Top Down Architecture, Top Down Implementation
2.Federated Star Schemas - Bottom Up Architecture, Bottom Up Implementation
3.Data Vault - Top Down Architecture, Bottom Up Implementation
Technology Architecture
Scalability and flexibility is required in all facets. The extent of these features are largely depend upon organizational size, business requirements, nature of business etc.
Technology or Technical architecture primary evolved from derivations from the process architecture, meta data management requirements based on business rules and security levels implementations and technology tool specific evaluation.
Besides these, the Technology architecture also looks into the various technology implementation standards in database management, database connectivity protocols (ODBC, JDBC, OLE DB etc), Middleware (based on ORB, RMI, COM/DOM etc.), Network protocols (DNS, LDAP etc) and other related technologies.
Information Architecture
Information Architecture is the process of translating the information from one form to another in a step by step sequence so as to manage the storage, retrieval, modification and deletion of the data in the data warehouse.
Resource Architecture
Resource architecture is related to software architecture in that many resources come from software resources. Resources are important because they help determine performance. Workload is the other part of the equation. If you have enough resources to complete the workload in the right amount of time, then performance will be high. If there are not enough resources for the workload, then performance will be low.
Various Architectures
Please notice that with the different architectures there is one that stands out: Data Model Architecture. What is happening in the integration industry at large is: the ability to integrate information across the enterprise is becoming dependent on the quality of the data model architecture below.
The ability to be compliant, consistent and repeatable depends on how the data model is built under the covers.
There are 3 main data modeling styles for enterprise warehouses:
3rd Normal Form - Top Down Architecture, Top Down Implementation
Federated Star Schemas - Bottom Up Architecture, Bottom Up Implementation
Data Vault - Top Down Architecture, Bottom Up Implementation
You can read more about the Data Vault by searching for "Data Vault Data Model" on the web.
The point to Data Warehousing Architecture, is it is not JUST a data warehouse anymore. It is now a full-scale data integration platform, including right-time (real-time) data, and batch or strategic data sets in a single, auditable (and integrated) data store.
Requirements of a Data Warehouse
The data warehouse must make an organization’s information easily accessible.
The contents of the data warehouse must be understandable. The data must be intuitive and obvious to the business user, not merely the developer. Understandability implies legibility; the contents of the data warehouse need to be labeled meaningfully. Business users want to separate and combine the data in the warehouse in endless combinations, a process commonly referred to as slicing and dicing. The tools that access the data warehouse must be simple and easy to use. They also must return query results to the user with minimal wait times.
The data warehouse must present the organization’s information consistently.
The data in the warehouse must be credible. Data must be carefully assembled from a variety of sources around the organization, cleansed, quality assured, and released only when it is fit for user consumption. Information from one business process should match with information from another. If two performance measures have the same name, then they must mean the same thing. Conversely, if two measures don’t mean the
same thing, then they should be labeled differently. Consistent information means high-quality information. It means that all the data is accounted for and complete. Consistency also implies that common definitions for the contents of the data warehouse are available for users.
The data warehouse must be adaptive and resilient to change.
We simply can’t avoid change. User needs, business conditions, data, and technology are all subject to the shifting sands of time. The data warehouse must be designed to handle this inevitable change. Changes to the data warehouse should be graceful, meaning that they don’t invalidate existing data or applications. The existing data and applications should not be changed or disrupted when the business community asks new questions or new data is added to the warehouse. If descriptive data in the warehouse is modified, we must account for the changes appropriately.
The data warehouse must be a secure bastion that protects our informationassets.
An organization’s informational crown jewels are stored in the data warehouse. At a minimum, the warehouse likely contains information about what we’re selling to whom at what price—potentially harmful details in the hands of the wrong people. The data warehouse must effectively control access to the organization’s confidential information.
The data warehouse must serve as the foundation for improved decision making.
The data warehouse must have the right data in it to support decision making. There is only one true output from a data warehouse: the decisions that are made after the data warehouse has presented its evidence. These decisions deliver the business impact and value attributable to the warehouse. The original label that predates the data warehouse is still the best description of what we are designing: a decision support system.
The business community must accept the data warehouse if it is to be deemed successful.
It doesn’t matter that we’ve built an elegant solution using best-of-breed products and platforms. If the business community has not embraced the data warehouse and continued to use it actively six months after training, then we have failed the acceptance test. Unlike an operational system rewrite, where business users have no choice but to use the new system, data warehouse usage is sometimes optional. Business user acceptance has more to do with simplicity than anything else.
As this list illustrates, successful data warehousing demands much more than being a stellar DBA or technician. With a data warehousing initiative, we have one foot in our information technology (IT) comfort zone, while our other foot is on the unfamiliar turf of business users. We must straddle the two, modifying some of our tried-and-true skills to adapt to the unique demands of data warehousing. Clearly, we need to bring a bevy of skills to the party to behave like we’re a hybrid DBA/MBA.
The contents of the data warehouse must be understandable. The data must be intuitive and obvious to the business user, not merely the developer. Understandability implies legibility; the contents of the data warehouse need to be labeled meaningfully. Business users want to separate and combine the data in the warehouse in endless combinations, a process commonly referred to as slicing and dicing. The tools that access the data warehouse must be simple and easy to use. They also must return query results to the user with minimal wait times.
The data warehouse must present the organization’s information consistently.
The data in the warehouse must be credible. Data must be carefully assembled from a variety of sources around the organization, cleansed, quality assured, and released only when it is fit for user consumption. Information from one business process should match with information from another. If two performance measures have the same name, then they must mean the same thing. Conversely, if two measures don’t mean the
same thing, then they should be labeled differently. Consistent information means high-quality information. It means that all the data is accounted for and complete. Consistency also implies that common definitions for the contents of the data warehouse are available for users.
The data warehouse must be adaptive and resilient to change.
We simply can’t avoid change. User needs, business conditions, data, and technology are all subject to the shifting sands of time. The data warehouse must be designed to handle this inevitable change. Changes to the data warehouse should be graceful, meaning that they don’t invalidate existing data or applications. The existing data and applications should not be changed or disrupted when the business community asks new questions or new data is added to the warehouse. If descriptive data in the warehouse is modified, we must account for the changes appropriately.
The data warehouse must be a secure bastion that protects our informationassets.
An organization’s informational crown jewels are stored in the data warehouse. At a minimum, the warehouse likely contains information about what we’re selling to whom at what price—potentially harmful details in the hands of the wrong people. The data warehouse must effectively control access to the organization’s confidential information.
The data warehouse must serve as the foundation for improved decision making.
The data warehouse must have the right data in it to support decision making. There is only one true output from a data warehouse: the decisions that are made after the data warehouse has presented its evidence. These decisions deliver the business impact and value attributable to the warehouse. The original label that predates the data warehouse is still the best description of what we are designing: a decision support system.
The business community must accept the data warehouse if it is to be deemed successful.
It doesn’t matter that we’ve built an elegant solution using best-of-breed products and platforms. If the business community has not embraced the data warehouse and continued to use it actively six months after training, then we have failed the acceptance test. Unlike an operational system rewrite, where business users have no choice but to use the new system, data warehouse usage is sometimes optional. Business user acceptance has more to do with simplicity than anything else.
As this list illustrates, successful data warehousing demands much more than being a stellar DBA or technician. With a data warehousing initiative, we have one foot in our information technology (IT) comfort zone, while our other foot is on the unfamiliar turf of business users. We must straddle the two, modifying some of our tried-and-true skills to adapt to the unique demands of data warehousing. Clearly, we need to bring a bevy of skills to the party to behave like we’re a hybrid DBA/MBA.
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