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Js-test-driver is an open source JavaScript unit tests runner written in Java. The project was started at Google and is under active development. It is available under Apache License 2.0 license

s-test-driver is able to run from command line and reports results to the standard output. As a result, it is possible to fully automate JavaScript tests and run them on a continuous integration server.

s-test-driver is able to run from command line and reports results to the standard output. As a result, it is possible to fully automate JavaScript tests and run them on a continuous integration server.

We can collect the data when we run tests through Hudson, but that doesn’t make the output much more readable, doesn’t capture metadata (such as which environment configuration the tests ran against) and doesn’t even give useful historic snapshot data, as the jobs run different suites of tests based on parameters.

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))

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.

R is one of the most popular open source statistical computing and graphical software. It can work with various data sources from comma separated files to web contents referred by URLs to relational databases to NoSQL (e.g. MongoDB or Cassandra) and Hadoop.

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration

racle SQL Developer Data Modeler provides a model driven approach for database design and generation, implemented by integrated set of models – Logical, Data types, Dimensional, Relational, Data Flow diagrams and Physical models for supported Oracle Databases, Microsoft SQL Server 2000 and 2005

Cube Views metadata

Dimensional Models   Dedicated full featured Dimensional model – star and snowflake schema easily can be built and expressed on detailed and compact diagrams   Dimensions – support for merging (level can belongs to more than one dimension), shared, fact and role playing dimensions   Hierarchies – value based hierarchies (parent-child), and regular and ragged level based hierarchies   Measures – fully, semi and none additive; different aggregation functions on different dimensions ; fact dimension; calculated measures   Query wizard allows Select statements to be generated from the dimensional model   Support for Oracle OLAP. This includes specifics like cube partitioning, sparse dimensions, and compressed measures   Built-in wizards help to define all required object types and view definitions that enable SQL access to dimensional data in Oracle AW (using the OLAP_TABLE interface)   Bidirectional integration with Oracle physical model - Dimensional model can be created using SQL dimension definitions in physical model or the definitions can be created from the dimensional model

Access clear summary reports which allow you to drill down to screenshots, stack traces and video

Easily integrated with Continuous Delivery tools like TeamCity and Hudson

Facilitates all team usability and avoids silosBuilt for Agile - make your tests easily re-factored and robust to change

Toreo Data integrates SAP Business Objects with Tableau so end users can easily access and analyze data.

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.

Seamless integration with “Big Data” using PolyBase.

Up to 50x performance gains with the xVelocity updateable columnstore.

XML file is a CppUnit result. It would not be easy for me to convert this file to a trx file. BUT i found a quite nice solution. First i use the new MSBuild target XslTransformation and transform the result to an jUnit XML result file. I then used the PublishJUnit task from the Ant integration to transform the new XML file to trx files and publish them in the TFS.

Dr. Kimball explains how Hadoop can be both: A destination data warehouse, and also An efficient staging and ETL source for an existing data warehouse

Building a Hadoop Data Warehouse: Hadoop 101 for EDW Professionals Dr. Ralph Kimball explains how Hadoop can be both a destination data warehouse, and also an efficient staging and ETL source for an existing data warehouse. Learn how enterprise conformed dimensions can be used as the basis for integrating Hadoop and conventional data warehouses.

The ADRM Software Healthcare Services Provider models consists of a set of integrated Enterprise, Business Area and Data Warehouse data models developed for organizations in the healthcare service provider industry. The focus of these models is to provide a blueprint of the complex data required to support a variety of applications, analytics and information services for health plan service providers. ·  Service Provider ·  Encounter ·  Claim ·  Patient ·  Service ·  Facility ·  Insurer ·  Health Plan

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.

With Tableau's JavaScript API you can integrate Tableau visualizations into your own web applications. The API lets you tightly control your users' interactions and combine functionality that otherwise couldn't be combined. For example, you can code a single control that filters a group of marks, selects some of those marks, and presents their data for download

Developers creating web applications can integrate fully interactive Tableau content into their applications via the new JavaScript API. The API provides a tremendous range of interactivity in the Tableau view. For example, you can use the JavaScript API to set and create filters, select and highlight elements in the view, and process the view to retrieve updated data. This enables you to provide a high level of interactivity between a Tableau visualization and the rest of the web page.