Group items tagged

Monitoring and Metrics

Automatic Software Patching

Automated Backups

This topic describes how to install and configure a test controller, and how to configure test agents so that you can run tests in a lab environment using Microsoft Test Manager.

Supports Google account and OpenID authentication and access control on each JSON document. Provides admin console for setting the access control.

Why Sikuli? Sikuli automates anything you see on the screen. It uses image recognition to identify and control GUI components. It is useful when there is no easy access to a GUI's internal or source code.

Why Sikuli? Sikuli automates anything you see on the screen. It uses image recognition to identify and control GUI components. It is useful when there is no easy access to a GUI's internal or source code.

Sentry Open Source, Fine-Grained Access Control for your Enterprise Data Hub Apache Sentry (incubating) is the next step in enterprise-grade big data security and delivers fine-grained authorization to data stored in Apache Hadoop

Improved Regulatory Compliance – Business teams can leverage the power of Hadoop while aligning with regulatory mandates like HIPAA, SOX, and PCI.

Role-Based Administration – Database administrators can unlock key role-based access control (RBAC) requirements and define what users and applications can do with data within a server, database, table, or view.

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

Placing a document under version control is very unintrusive. Vermongo only adds a version number property (an int32 called _version) to the document. It does not touch any other fields. In particular, it also does not place any requirements on the contents of the _id field. Older revisions are stored in a separate collection that shadows the original collection. They are MongoDB documents themselves, and can be queried.

When creating, updating, or deleting documents, you just have to follow the Vermongo protocol outlined here. There will be (at least) a Java library to facilitate this. The only major downside is that documents have to be updated one-by-one (no bulk updates possible), and that there is some overhead in retrieving and copying the old version. Query operations can just proceed as normal.

After inserting into collection 'foo' a document { a=>"x"}, updating it to { a: "y"}, then to { a: "z" }, and finally deleting it, the shadow collection will contain the following (the original collection will not contain the document anymore, as it has been deleted): > db.foo.vermongo.find() { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 1 }, "a" : "x", "_version" : 1 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 2 }, "a" : "y", "_version" : 2 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 3 }, "a" : "z", "_version" : 3 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 4 }, "_version" : "deleted:4" }

Data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data.

Data governance tools[edit] Leaders of successful data governance programs declared in December 2006 at the Data Governance Conference in Orlando, Fl, that data governance is between 80 and 95 percent communication.”[6] That stated, it is a given that many of the objectives of a Data Governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as Data Governance tools; due to the different focus areas of various data governance initiatives, any given tool may or may not be appropriate, in addition, many tools that are not marketed as governance tools address governance needs.[7]

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.

Source Plugin - uses just the version control side of TFS and allows Tableaux to checkout files from the TFS server. Build Plugin - uses the CI Build functionality of TFS. Tableaux can ask TFS to create a build from a defined Label. Tableaux can capture the build and deploy it just like any other component.

Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise

Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.

Each nominal ledger account is unique to allow its ledger to be located

A chart of accounts (COA) is a created list of the accounts used by an organization to define each class of items for which money or the equivalent is spent or received. It is used to organize the finances of the entity and to segregate expenditures, revenue, assets and liabilities in order to give interested parties a better understanding of the financial health of the entity.

Types of accounts[edit] Asset accounts: represent the different types of economic resources owned or controlled by business, common examples of Asset accounts are cash, cash in bank, building, inventory, prepaid rent, goodwill, accounts receivable[1] Liability accounts: represent the different types of economic obligations by a business, such as accounts payable, bank loan, bonds payable, accrued interest.[citation needed] Equity accounts: represent the residual equity of a business (after deducting from Assets all the liabilities) including Retained Earnings and Appropriations.[citation needed] Revenue accounts or income: represent the company's gross earnings and common examples include Sales, Service revenue and Interest Income.[citation needed] Expense accounts: represent the company's expenditures to enable itself to operate. Common examples are electricity and water, rentals, depreciation, doubtful accounts, interest, insurance.[citation needed] Contra-accounts: Some balance sheet items have corresponding contra accounts, with negative balances, that offset them. Examples are accumulated depreciation against equipment, and allowance for bad debts against long-term notes receivable.