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import unittestimport osimport sqlalchemy import myapp class TestDB(unittest.TestCase): def setUp(self): url = os.getenv("DB_TEST_URL") if not url: self.skipTest("No database URL set") self.engine = sqlalchemy.create_engine(url)  def test_foobar(self): self.assertTrue(myapp.store_integer(self.engine, 42))

import unittestimport osimport sqlalchemy import myapp class TestDB(unittest.TestCase): def setUp(self): url = os.getenv("DB_TEST_URL") if not url: self.skipTest("No database URL set") self.engine = sqlalchemy.create_engine(url)  def test_foobar(self): self.assertTrue(myapp.store_integer(self.engine, 42))

import unittestimport osimport sqlalchemy import myapp class TestDB(unittest.TestCase): def setUp(self): url = os.getenv("DB_TEST_URL") if not url: self.skipTest("No database URL set") self.engine = sqlalchemy.create_engine(url)  def test_foobar(self): self.assertTrue(myapp.store_integer(self.engine, 42))

Multidimensional Expressions (MDX) is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.

The specification was quickly followed by commercial release of Microsoft OLAP Services 7.0 in 1998 and later by Microsoft Analysis Services.

While it was not an open standard, but rather a Microsoft-owned specification, it was adopted by the wide range of OLAP vendors. This included both vendors on the server side such as Applix, icCube, MicroStrategy, NCR, Oracle Corporation, SAS, SAP, Teradata, Symphony Teleca, and vendors on the client side such as Panorama Software, PowerOLAP, XLCubed, Proclarity, AppSource, Jaspersoft, Cognos, Business Objects, Brio Technology, Crystal Reports, Microsoft Excel, and Microsoft Reporting Services.

Versioning is an extra-feature that is not fundamental to document databases. MongoDB and CouchDB do not support it by default, but there are different solutions available

Versioning is not supported by either MongoDB and CouchDB. MVCC should not be confused for document versioning

Built-in Versioning: Most document databases support versioning of documents with the flip of a switch.

we promise that your client application which uses the database is a separate work. To facilitate this, the mongodb.org supported drivers (the part you link with your application) are released under Apache license, which is copyleft free.

MongoDB Database Server and Tools¶ Free Software Foundation’s GNU AGPL v3.0. Commercial licenses are also available from 10gen, including free evaluation licenses. Drivers¶ mongodb.org supported drivers: Apache License v2.0. Third parties have created drivers too; licenses will vary there.

The usual interface to manipulate an OLAP cube is a matrix interface like Pivot tables

MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database

The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time

MongoDB includes a basic authentication scheme that works with single server deployments and replica sets. However, the built-in authentication does not work with sharding, so for this and other cases, we recommend running the database in a secure environment.

The current version of Mongo supports only very basic security.  One authenticates a username and password in the context of a particular database.  Once authenticated, a normal user has full read and write access to the database in question while a read only user only has read access.

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

One pretty easy way to include some “built-in” database documentation for your databases (which could be the beginnings of a data dictionary) is to add extended properties on your objects.

What is tSQLt? tSQLt is a database unit testing framework for Microsoft SQL Server. tSQLt is compatible with SQL Server 2005 (service pack 2 required) and above on all editions.

DbUnit is a JUnit extension (also usable with Ant) targeted at database-driven projects that, among other things, puts your database into a known state between test runs.

SchemaSpy is a Java-based tool (requires Java 5 or higher) that analyzes the metadata of a schema in a database and generates a visual representation of it in a browser-displayable format. It lets you click through the hierarchy of database tables via child and parent table relationships as represented by both HTML links and entity-relationship diagrams.

Document a database in a couple of clicks, from within SSMS

you can add further descriptions to your database objects if necessary.

QLDoc Sharp is an interactive tool designed to generate the SQL Server 2005/2008 documentation. It allows you to export documentation to CHM format (Microsoft Compiled HTML Help).It is also easy and interactive, which allows multiple database documentation. For more details, please refer to http://www.amitchaudhary.com/. Background Finding the answer to, "Is there any free tool for generating documentation from SQL 2005/2008". Using the Code After running the application, the initial UI looks like below: Three steps required to generate the documentation. Step # 1 In the top section, provide the details about the SQL Server. It includes: Source: Instance Name of the SQL Server 2005/2008  If want to connect with SQL authentication then: User Name: Name of the user whose credentials you want to use Password: Password of the user whose credentials you want to use In case, to connect with Windows authentication then, only check the checkbox   (Integrated Security) and your current Windows credentials would be used to connect with the specified SQL Server Instance. Step # 2 Choose the database name from the Database dropdown list, whose documentation you want to generate and then click on the Fetch button. Meanwhile you can choose/change the File Name of the CHM file which would be generated. And also if required, you then choose to export the metadata/documentation in the XML format too. Step # 3 Click on the Generate button. And locate the CHM file at the path specified. After making the selection the SQLDoc Sharp, the UI should look like: Screen Shots of the Documentation Generated Index

SQLDoc Sharp is an interactive tool designed to generate the SQL Server 2005/2008 documentation.

To say this another way: if you modify the core database source code, the goal is that you have to contribute those modifications back to the community.

Embedding¶ To de-normalize data, store two related pieces of data in a single document. Operations within a document are less expensive for the server than operations that involve multiple documents. In general, use embedded data models when: you have “contains” relationships between entities. See Model Embedded One-to-One Relationships Between Documents. you have one-to-many relationships where the “many” objects always appear with or are viewed in the context of their parent documents. See Model Embedded One-to-Many Relationships Between Documents. Embedding provides the following benefits: generally better performance for read operations. the ability to request and retrieve related data in a single database operation.

Referencing¶ To normalize data, store references between two documents to indicate a relationship between the data represented in each document. In general, use normalized data models: when embedding would result in duplication of data but would not provide sufficient read performance advantages to outweigh the implications of the duplication. to represent more complex many-to-many relationships. to model large hierarchical data sets. See Model Tree Structures in MongoDB. Referencing provides more flexibility than embedding; however, to resolve the references, client-side applications must issue follow-up queries. In other words, using references requires more roundtrips to the server.

When considering implementing Operational BI solutions, many implementers first think of copying the operational data to an operational data store (ODS), data warehouse or data mart and analysing it there.  They are immediately faced with the problem of how to update the informational environment fast enough to satisfy the timeliness requirement of the users.  As that approaches real-time, traditional ETL tools begin to struggle.  Furthermore, in the case of the data warehouse, the question arises of the level of consistency among these real-time updates and between the updates and the existing content.  The way MongoDB is used points immediately to an alternative, viable approach--go directly against the operational data.

In the case of Operational BI, however, most experience indicates that the queries are usually relatively simple, and closely related to the primary access paths used operationally for the data concerned.

This offering is a complete analytics platform that combines market-leading data integration, data management, and business intelligence (BI) products, pre-certified to run together – for a more effective way to harness big data.

zed Analytics Server Analyze massive quantities of data 100 times faster than traditional relational databases – for more accurate insight into performance and market dynamics. Run big data analytics with unsurpassed query performance – for faster decision making Uncover new ways to reduce overhead, storage costs, and maintenance spend Offer accurate, timely information to end users across the organization Gain greater scalability with an open, flexible, column-based architecture SAP Sybase IQ .multilinkwidget a.btn-doc{ padding-bottom:10px; } $(document).ready(function(){ setTimeout(function() { if($("td em button.x-btn-text", $("tr.x-toolbar-left-row")).length > 0) $("td em button.x-btn-text", $("tr.x-toolbar-left-row")).each(function() { if($(this).html()== 'Edit' && $("td em button.x-btn-text", $(this).parents("td.x-toolbar-cell").next()).html() == 'Manage Links'){ $(this).parent().parent().parent().parent().remove(); } }); },3000); }); Less Business Intelligence Platform Make it easy to discover and share insight with a business intelligence platform that gives you flexibility, scalability, and function. Increase the range of data accessible to business users Reduce IT workload with simplified maintenance and administration options Integrate all enterprise data regardless of format or location Centrally manage, control, and configure your BI deployment SAP BusinessObjects BI Platform .multilinkwidget a.btn-doc{ padding-bottom:10px; } $(document).ready(function(){ setTimeout(function() { if($("td em button.x-btn-text", $("tr.x-toolbar-left-row")).length > 0) $("td em button.x-btn-text", $("tr.x-toolbar-left-row")).each(function() { if($(this).html()== 'Edit' && $("td em button.x-btn-text", $(this).parents("td.x-toolbar-cell").next()).html() == 'Manage Links'){ $(this).parent().parent().parent().parent().remove(); } }); },3000); }); Less $(document).ready(function() { // Expand all content by default // $('.rmuc_expandableLI').each(function () { $(this).css('height', 'auto'); }); });

Highly Optimized Analytics Server Analyze massive quantities of data 100 times faster than traditional relational databases – for more accurate insight into performance and market dynamics.

Common characteristics of a metadata registry A metadata registry typically has the following characteristics: Protected environment where only authorized individuals may make changes Stores data elements that include both semantics and representations Semantic areas of a metadata registry contain the meaning of a data element with precise definitions Representational areas of a metadata registry define how the data is represented in a specific format, such as in a database or a structured file format (e.g., XML)

ISO 11179

A metadata registry is a central location in an organization where metadata definitions are stored and maintained in a controlled method.

A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts.

Dimensions are often recycled for multiple applications within the same database. For instance, a "Date" dimension can be used for "Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often referred to as a "role-playing dimension".

If you want to do test-driven development for databases, SQL Test is the place to start. It lets you write database unit tests in T-SQL and run them in SQL Server Management Studio.