SQL Azure : Managing Your Azure Projects |
Now you're getting to the meat of Azure. After reading all the information in the last two chapters, and working through setup and activation in this chapter, the time has come to start getting your hands dirty. From here on out, it's hands-on. |
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An OLAP Requirements Example: CompSales International (part 15) - SSIS |
Data mining is the process of understanding potentially undiscovered characteristics or distributions of data. Data mining can be extremely useful for OLAP database design in that patterns or values might define different hierarchy levels or dimensions that were not previously known. |
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An OLAP Requirements Example: CompSales International (part 1) |
Following is an abbreviated requirement that reflects an actual implementation that was done for a large Silicon Valley company. We follow the mini-methodology as closely as possible to implement this requirement in SSAS, pointing out which facilities of SSAS should be used for which purpose along the way. |
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SQL Server 2008 Analysis Services : An Analytics Design Methodology |
A data warehouse can be built from the top down or from the bottom up. To build a top-down warehouse, you need to form a complete picture or logical data model for the entire organization (or all the subsystems within the scope of the project, such as all financial systems) |
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SQL Azure : Other Considerations |
Blobs are files that can be stored in Windows Azure. What is interesting about blobs is that they can be easily accessed through REST, there is no limit to the number of blobs that can be created, and each blob can contain as much as 50GB of data. |
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SQL Azure : Sample Design - Application SLA Monitoring |
To put a few of the patterns in perspective, let's create a formal design around a system that monitors application performance service-level agreements (SLAs). In this design, a company already has a monitoring product that can audit activity in existing SQL Server databases at customer sites |
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SQL Azure : Combining Patterns |
The previous design patterns provide the necessary basis to build systems with SQL Azure. Some of these patterns can be used as is, but you're very likely to combine patterns to deliver improved solutions |
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SQL Server 2008 Analysis Services : Understanding the SSAS Environment Wizards (part 1) |
Welcome to the “land of wizards.” This implementation of SSAS, as with older versions of SSAS, is heavily wizard oriented. SSAS has a Cube Wizard, a Dimension Wizard, a Partition Wizard, a Storage Design Wizard, a Usage Analysis Wizard, a Usage-Based Optimization Wizard, an Aggregation Wizard, a Calculated Cells Wizard, a Mining Model Wizard, and a few other wizards. |
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SQL Server 2008 Analysis Services : Understanding SSAS and OLAP |
Because OLAP is at the heart of SSAS, you need to understand what it is and how it solves the requirements of decision makers in a business. As you might already know, data warehousing requirements typically include all the capability needed to report on a business’s transactional history, such as sales history. |
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SQL Azure : Design Patterns (part 3) |
In the offloading pattern, the primary consumer represents an existing onsite application with its own database; but a subset of its data (or the entire database) is replicated to a cloud database using SQL Data Sync (or another mechanism). |
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SQL Azure : Design Patterns (part 2) - Sharding |
So far, you've seen patterns that implement a single connection at a time. In a shard, multiple databases can be accessed simultaneously in a read and/or write fashion and can be located in a mixed environment (local and cloud). |
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SQL Azure : Design Patterns (part 1) |
Let's review the important design patterns that use SQL Azure. Before designing your first cloud application, you should read this section to become familiar with a few design options. |
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SQL Azure : Design Factors (part 2) |
You may also consider developing Azure services to keep the database connection to a local network, and send the data back to the client using SOAP or REST messages |
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SQL Azure : Design Factors (part 1) |
Storing data in SQL Azure is similar to storing data in SQL Server. All you need to do is issue T-SQL statements and review some of the limitations of the syntax specific to SQL Azure, and off you go! |
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