Determine appropriate scenarios for Transact-SQL joins versus SSIS lookup

-
Some common design patterns for data warehouse loads invlove extracting data from one or more sources and performing business and dimension key column lookups and joins. In most instances, SSIS packages can be optimized to perform these lookups and joins efficiently by tuning package control and data flow properties. In addition, it might be necessary to ensure that database object maintenance activities are done in a regular basis including, index defragmentation and statistics update.

The SSIS Lookup transform comes very handy when doing lookup of dimension keys. For example, when loading a fact table with customer transactions, we lookup the customer’s business key against the business keys stored in the customer dimension. If we find a match, we retrieve the customer’s surrogate key and insert it in the fact table along with the corresponding transactions.

In some cases, the best alternative to overcome SSIS Lookup limitations is to use T-SQL joins. Joins peformed using T-SQL can perform much better in those instances in which the SSIS server cannot fit an entire table in its buffer. The SQL Query Optitmizer can make use of indexes and statistics to produce a more efficient query plan to process large datasets in less time than it would take SSIS to cache all the data. Partition elimination can further improve large query performance.

In addition to lookups and joins, merge operations can scale better using T-SQL than the SSIS Merge, Merge Join or SCD transformations. This is true when staging large volumes of data that need to be merged to identify what needs to be inserted, updated or deleted. Using the SSIS Merge, Merge Join or SCD transformations means that the data needs to be brought into the SSIS server’s memory buffer to apply merge and join operations. This can be very costly when processing large volumes of data or when more complex business rules need to be applied.

In some scenarios in which you are joining and merging data from sources other than SQL Server, SSIS may be the only viable solution if the data is not being staged in a SQL Server database or not being staged at all.

Other scenarios that may influence the decision when to use SSIS over T-SQL may include:
  • Complexity of business rules and data transformations
  • Number of lookups
  • Developer skillsets
  • Organization standards and regulations

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