Create an SSIS catalog

-
SQL Server 2012 introduced a new paradigm in storing and deploying SSIS packages. Before that release, all packages were individually managed and deployed.

The SSIS DB catalog and SSISDB database serves as a centralized place where deployed SSIS packages can be stored along with everything related to it, including:
  • Project and package parameters
  • Configuring runtime values for packages
  • Executing and troubleshooting packages
  • Managing Integration Services server operations













You find the SSISDB catalog in the folder labeled Integration Services Catalogs. This is where you find the Projects folder containing each project with all its packages. And the Environments folder contains the package configuration and environment values, which can be set before executing the packages. Each SQL Server instance gets only one SSISDB.







You also find another SSISDB in the Databases folder in SQL Server (Figure 2-113). This is essentially a system database containing all of the information about the projects and packages and their executions, parameters, logging, etc. Here you find tables and views that store a lot of information and details about environment and variable information; and stored procedures that can execute package-specific tasks. So you can see everything under the hood. By comparison, the SSISDB catalog shows us just the main working items needed to work on, such as the projects, packages, and environments.












You should be aware of some properties of the SSIS Catalog that can be configured if needed:













  • Clean Logs Periodically If logs should be cleared or retained (see Retention Period)
  • Retention Period (days) How long the longs should be kept before being cleared
  • Server-wide Default Logging Level The level of detail of the package execution log
    • None No logging
    • Basic Event types are captured
    • Performance Captures events and each step of the execution
    • Verbose Includes events, each step, as well as statistics for component
  • Maximum Number of Versions per Project Retains specified number of previous versions of the project within the SSISDB catalog
  • Periodically Remove Old Versions Specifies how many versions to retain

Comments

Post a Comment

Popular Posts

Non-blocking, semi-blocking, and blocking transforms in SSIS

-
Not all Data Flow transforms are created equal. In fact, some are much faster and require a  lot less memory (non-blocking transforms). Others are slower while consuming much more  memory (blocking transforms), and some are in between (semi-blocking transforms). We don’t  always have a choice if we need to use a certain transform to do a certain specified task, but  it is good to know the differences between these types of transforms so that we can better  understand performance and resource utilization issues. Synchronous and asynchronous There is another related aspect about Data Flow transforms, which is their ability to quickly  process a row as they are coming into the transform, independently of any other rows that  came before or after (synchronous transforms). The other type of transform needs to be  dependent on some or all of the rows that come before and after (asynchronous transforms). On the whole, non-blocking transforms are also synchronous, thus are fast and
Read more

Implement additive, semi-additive, and non-additive measures

-
Additive Additive measures are numeric values that can be summed up by all the dimensions in the  fact table, for example the Sales Amount. These are the most common measures and they are  the easiest to understand and implement. Semi-additive Semi-additive measures are numeric values that can be summed up by some dimensions  in the fact table, but not all. In most cases, they can be aggregated by any other dimension  except time.  An example of a semi-additive measure is Account Balance. You can determine  the ending balance of an account at the end of a month, but adding up these monthly account  balances do not make sense. Semi-additive measures are usually found in fact tables that store  periodic snapshots of data. Non-additive Non-additive measures, as the name implies, are measures that cannot be summed up.  These measures include percentages such as Discount Percent, ratios such as Profit Margin, and  averages such as Average Unit Cost. Exam 70-767
Read more

Design an appropriate storage solution, including hardware, disk, and file layout

-
Image
The data warehouse storage’s performance can be measured by  analyzing the time it takes for read and write operations to complete, known as latency , and by  analyzing how much data volume it can read or write in each time frame, known as throughput .  High latency and low throughput can be an indication of an improper selection for the  storage infrastructure or incorrect hardware and software configurations. Performance of a storage subsystem can  be estimated based on several criteria: Volume of data Rate of change and growth of the data Data retention policy Number of concurrent users Typical workloads expected Two important metrics associated with storage are throughput and Input/Output Per  Second (IOPS) . Throughput is a measure of the amount of data that can be transferred in a  period. This is typically measured in megabytes or gigabytes per second. IOPS is the number of  I/O read or write operations that can complete in a second.  It is important
Read more