Data Integration Challenge – Understanding Lookup Process –II

Most of the leading products like Informatica, DataStage support all the three ways of lookup process in their product architecture. The following table lists ‘when to use’ and ‘when not to use’ the particular type of lookup process.

Lookup When To Use When Not To Use Direct Query (Uncached lookup in Informatica) When the lookup process is to be invoked only once or a very few times The ETL server and the database are co-located or well connected Reading in large volume of source records and executing lookup queries for every incoming record can be costly in terms network load, query parsing, data parsing and disk hits Querying the same set of records again and again Join Query (Joiner Transformation or a Join on the Source Qualifier in Informatica) When multiple records are returned by the lookup process and all the returned records are required for further processing Both the source and lookup table are on the same database When the source record performs a lookup based on some other ‘TRUE’ condition i.e., not all the records that are read from source do a lookup When the source and lookup table columns are not indexed by the ‘lookup condition’ When the database memory is fully utilized and the Outer Joins are badly executed by the database Cached Query (Cached Lookup in Informatica or Hash files in Datastage) Many times the lookup process being executed Presence of Large volume of data in the looked up table Set of records from the lookup table used by multiple jobs Disk space is a constraint Multiple records from lookup required for processing

Advantage Cache Lookup:

The advantages of using cache file based lookups are that

• Fields that are present in the cache file is only that is needed by the lookup process so when querying the cache file the return would be faster as compared to the lookup table that might have more fields present
• The data structure of the cache file would be designed in such that the query from the ETL server is easily understood without any additional layer like SQL

Though in general it is said in user manuals that usage of cache files is best suited for low volume of lookup but in practical scenarios I have seen cache files are more valuable in terms of performance when the lookup records are huge.

Dynamic Cache: We have the concept of Dynamic Cache in Informatica and as well in Hash files of Datastage where you can Insert/Update or delete records from these cache file. The feature of  updating the cache files is useful when we want to keep the cache file and the lookup table in sync.

Handling Multiple Return Records: Handling the return of multiple records by a lookup process is still a challenge not implemented in any of the leading products – limited to my knowledge. Probably in release 9 Informatica’s lookup can have a parameter for defining the number of records to return as an array like in its Normalizer transformer.

In Part III we shall see some of the things to be considered for better performance when using the lookup process

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Business Intelligence Utopia – Enabler 3: Data Governance

The “Power of Ten” introduced earlier in this forum is a list of pre-requisites to deliver the real promise of BI. We have already seen the first two – Changes to OLTP systems and Real time Data Integration.

The third enabler in the list is ‘Data Governance’. With increasing volumes of data coupled with regulatory compliance issues, the topic of Data Governance is very much in vogue, to the extent that anybody can look intelligent (beware!) by coining new terms like Data Clarity, Data Clairvoyance etc.

Data Governance at a very fundamental level is all about understanding the data generated by business, managing the quantity / quality of data and leveraging it to make sound business decisions for the future. From my view, the steps needed in a practical data governance program are:

1) Organizational entity, headed by a Chief Data Officer (CDO), whose task is to formulate and implement decisions related to Data Management across multiple dimensions, viz. Business Operations, Regulatory compliance etc.

2) Comprehensive understanding of the data ‘value chain’ – From the source of origination to its consumption. It is important to understand that the origination and / or consumption can also be outside the organizational boundaries.

3) Understand the types of data within the enterprise by following a ‘divide-and-conquer’ strategy. One of my previous posts on this blog illustrate one way of dividing data into ‘mutually exclusive collectively exhaustive’ (MECE) categories.

4) Profile data on a regular basis to statistically measure its quality.

5) Set-up a Business Intelligence infrastructure that effectively harnesses data assets for making decisions that affects (positively, of course!) the short, medium & long-term nature of business.

6) Continuous improvement program to ensure that data is optimally leveraged across all aspects of business. A data governance maturity model like the one illustrated here ,  can be envisaged for your organization.

‘Competing on Analytics’ – A classic Harvard Business Review article by Thomas Davenport illustrates the power of fact-based business decisioning. For businesses to realize that power, it is important to realize that good data is a source of competitive advantage and not ‘any’ data.

Data Governance is fundamental to making organizations better and that is the reason that it figures as number 3 in my list of ten enablers for Business Intelligence Utopia. . Informative articles on Data Governance are present at the following link.

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.

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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?
3. 
   

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:

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. 
   
3. 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’
4. 
   
5. If required while inserting the record into the Scratch table ‘S’ have a flag column that says ‘Insert’ or ‘Update’
6. 
   
7. 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’
8. Once the changes have been pulled and processed, have a process that would clean up the Scratch table
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10. We can also bind the Scratch table ‘S’ to be always loaded to the memory for higher performance
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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
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3. When the base table ‘T’ is really huge and only few changes happen
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5. When the changes (or the Primary Key values) in table ‘T’ are required by multiple processes
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7. 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
8. 
   

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