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Introductie van Micorosoft SQL Server 2016

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179 C H A P T E R 9 | Introducing Azure SQL Data Warehouse You can use the Transparent Data Encryption feature in SQL Data Warehouse to encrypt data at rest, if necessary. You can implement this feature by switching the Data Encryption setting to On for your SQL Data Warehouse in the Azure Management Portal (as we describe in Chapter 8, "Improved SQL Database"). As an alternative, you can use the ALTER DATABASE SET ENCRYPTION ON command in T- SQL. Auditing for SQL Data Warehouse is similar to the way SQL Database performs auditing, which we describe in Chapter 8. You have the option to enable or disable auditing as needed. Just as with SQL Database, you need an Azure storage account to store auditing events. Scalability The advantage of SQL Data Warehouse over a traditional on-premises data warehouse is the ability it gives you to scale the service quickly up or down as your workload demands change. If you experience rapid data or user growth in an on-premises environment, you typically undergo the expensive and time-consuming process of acquiring new hardware or justifying to an infrastructure team the need to add another 10 terabytes (TB) of storage for the data warehouse. With SQL Data Warehouse, you can easily change resource allocations in minutes. Scaling storage and compute resources SQL Data Warehouse decouples storage from compute resources by using nearly unlimited blob storage. This lets you effortlessly manage rapid data growth at a much lower cost than on-premises options. This separation of compute resources from the storage component also allows you to pause SQL Data Warehouse. During this time, you pay only for storage, which is a particularly good option for smaller organizations that do not require full-time report processing or for developers who need access to the data warehouse during normal business hours only. Compute resources are also scalable. The unit of measurement for compute usage in SQL Data Warehouse is a Data Warehouse Unit (DWU), a metric for the underlying computing power required for your warehouse to ensure a standard performance level at any given time. By adjusting the DWUs up or down in increments of 100, you can scale your warehouse without managing the underlying hardware configuration. In the background, Microsoft runs performance-benchmark tests to determine how best to improve the hardware configuration and architecture of the service without impacting the performance of your workload. Specifically, DWUs are a composite of three separate metrics that correlate highly with overall data- warehouse performance. Increasing the number of DWUs results in a linear increase in performance as measured by the following metrics: Scan/Aggregation This metric measures the time required to execute a standard data- warehouse query that scans a large number of rows and then performs a complex aggregation. It benchmarks the CPU and I/O (storage) capacity of the service. Load This metric benchmarks network and CPU performance by measuring the data-load process when using PolyBase to load a representative data set from blob storage into the data warehouse. CREATE TABLE AS SELECT (CTAS) CTAS is another benchmark of network and CPU performance. It measures the ability to create a copy of a table by reading data from storage, distributing it across the compute nodes, and writing it back to storage.

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