Implement an ETL solution that supports incremental data loading

-
Design a control flow to load change data
In creating an incremental load Data Flow, you can create a dynamic query within the OLE DB Source Editor under Connection Manager page, go to the Data Access Mode drop-down and select SQL Command. This opens up a free text pane below labeled SQL Command Text. Here we can enter the query against the source table with a variable parameter in the WHERE clause. The parameter is identified by the “?” in the SQL text. “?” is the syntax used by the SSIS expression language for a parameter.
















With the SQL query in place, click the Parameters button to map the SQL query parameter to the SSIS variable @LastModifiedDate, which will pass in the value of MAX(ModifiedDate) from the fact table. Make sure the Param Direction is set to Input because the SSIS variable value is being passed in to be used by the query.

















Load data by using Transact-SQL Change Data Capture functions
In order to use Change Data Capture for automatic incremental load processing you need to make sure certain items are in place and specific tasks have been done. As mentioned earlier, CDC requires SQL Agent to function, if SQL Agent has been turned off or fails for any reason, the CDC process will be halted.

CDC needs to be enabled on the database level before it can be used. CDC also needs to be enabled on each table to be tracked. You can configure individual columns to be tracked, but by default all columns are select for tracking. CDC tracks the following changes:
  • Rows inserted
  • Rows updated (records column values before and after update)
  • Rows deleted (records column values before delete)
  • When the operation occurred

CDC works by creating a new CDC schema and new physical tables for each tracked table within the System Tables folder. These are the tables that contain the changes; there is a single row created for each INSERT and DELETE operation—and two rows created for each UPDATE operation: one row shows how the data looked before the update, and the second row shows the data after the UPDATE.

CDC is asynchronous, which means when a table data is changing, there is no contention with CDC, nor does it slow any transactions down. That is because CDC detects changes by reading the transaction log file, not the source tables. This is managed by the SQL Server Agent jobs. One of the jobs read the logs and records the changes, which means that when you query for changes, there may be a delay between when the data has changed and when it is available in CDC. Due to this delay, and depending on the speed of changes to your source data, you may not see all of the changes until the next time the CDC SQL Agent job runs. There is another SQL Server Agent job that removes the rows from the CDC tables that are older than the configured retention period. The default retention period is three days, which can be customized based on business retention requirements.

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