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Wednesday 29 August 2007

Data Integration Challenge – Understanding Lookup Process–I


One of the basic ETL steps that we would use in most of the ETL jobs during development is ‘Lookup’. We shall discuss further on what lookup is? when to use? how it works ? and some points to be considered while using a lookup process.
What is lookup process?
During the process of reading records from a source system and loading into a target table if we query another table or file (called ‘lookup table’ or ‘lookup file’) for retrieving additional data then its called a ‘lookup process’. The ‘lookup table or file’ can reside on the target or the source system. Usually we pass one or more column values that has been read from the source system to the lookup process in order to filter and get the required data.
How ETL products implement lookup process?
There are three ways ETL products perform ‘lookup process’
  • Direct Query: Run the required query against the table or file whenever the ‘lookup process’ is called up
  • Join Query: Run a query joining the source and the lookup table/file before starting to read the records from the source.
  • Cached Query: Run a query to cache the data from the lookup table/file local to the ETL server as a cache file. When the data flow from source then run the required query against the cache file whenever the ‘lookup process’ is called up
Most of the leading products like Informatica, Data stage support all the three ways in their product architecture. We shall see the pros and cons of this process and how these work in part II.
Read more about Data Integration Challenge

Thursday 16 August 2007

Business Intelligence Utopia – Enabler 2: Real Time Data Integration


Business Intelligence practitioners tend to have lot of respect and reverence for transaction processing systems (OLTP), for without them the world of analytical apps simply does not exist. That explains my previous blog in introducing the first enabler for BI Utopia – The Evolution of OLTP systems to support Operational BI.
In this post, I introduce the second enabler in the “Power of Ten” – Real Time Data Integration
Data Integration in the BI sense, is all about, extracting data from multiple source systems, transforming them using business rules and loading it back into data repositories built to facilitate analysis, reporting, etc.
Given that the raw data has to be converted to a different form more amenable for analysis & decision-making, there are 2 basic questions to be answered:
  1. From a business standpoint, how fast should the ‘data-information’ conversion happen?
  2. From a technology standpoint, how fast can the ‘data-information’ conversion happen?

Traditionally, BI being used more for strategic decision-making,  batch mode of data integration with periodicity of a day or later, was acceptable. But increasingly, businesses demand that the conversion has to happen much faster and technology has to support it. This leads to the concept of “Real Time BI” or more correctly "RightTime Data Integration"
Since the answer to the first question “How Fast” is fast becoming “as fast as possible”, the focus has shifted to the technology side. One area where I foresee a lot of activity, from a Data Warehouse architectural standpoint, is in the close interaction of messaging tools like IBM Websphere MQ etc. with data integration tools. At this point in time, though the technology is available, there aren’t too many places where messaging is embedded into the BI architectural landscape.
Bottom-line is that there is significant value gained by ensuring that raw business data is transformed to information by the BI infrastructure, as fast as possible – the limits being prescribed by business imperatives. The best explanation I have come across to explain the value of information latency is the article by Richard Hackathorn .
Active Data Warehousing is another topic closely related to Real Time Data Integration and you can get some perspective on it thro’ the blog on Decision management by James Taylor:

Wednesday 1 August 2007

Data Integration Challenge – Identifying changes from a table by a Scratch


In scenarios when a table in the staging area or in the data warehouse needs to be queried to find the changed records (inserted or updated), we can use the Scratch table design. Scratch table is a temporary table that can be designed to hold the changes happening against a table, once the changes are noted by the required application or process then the Scratch table can be cleaned-off.

The process to capture the changes and the clean up would be designed as part of ETL process. The scenarios where to use this concept and the steps to use the Scratch table is discussed below:
Steps to use Scratch table
  1. Create a Scratch table ‘S’ of structure to hold the Primary Key column value from the table ‘T’ that needs to the monitored for changes

  2. In the ETL process that loads the table ‘T’ add the logic in such way that while inserting or updating a record into table ‘T’ we insert the Primary Key column values of the record into the Scratch table ‘S’

  3. If required while inserting the record into the Scratch table ‘S’ have a flag column that says ‘Insert’ or ‘Update’

  4. Any process that needs to find the changes would join the Scratch table ‘S’ and the table ‘T’ to pull the changed records, if it just needs the key directly access ‘S’
  5. Once the changes have been pulled and processed, have a process that would clean up the Scratch table

  6. We can also bind the Scratch table ‘S’ to be always loaded to the memory for higher performance

When to use Scratch table
  1. When we have a persistent staging area, using Scratch table would be ideal choice to move the changes to the data warehouse

  2. When the base table ‘T’ is really huge and only few changes happen

  3. When the changes (or the Primary Key values) in table ‘T’ are required by multiple processes

  4. When the changes in table ‘T’ is to be joined with other tables i.e., now the Scratch Table ‘S’ can be used as the driving table in joins with other tables which would give better performance since the Scratch table would be thinner with few records

Alternate Option: Having a flag or a timestamp column in the table ‘T’ and having an index on it. Having an index on Timestamp is costly and a bit map index on the flag column may be seen as an option, but the aspect of updating the column during updates, huge volume and in scenarios of joining with other tables this would be a disadvantage, have seen Scratch table to be a best option. Let me know the other options you have used to handle such situations…
To add more variety to your thoughts, you can read it More Data Integration Challenge