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Mr. Lott has been involved in over 70 software development projects in a career that spans 30 years. He has worked in the capacity of internet strategist, software architect, project leader, DBA, programmer. Since 1993 he has been focused on data warehousing and the associated e-business architectures that make the right data available to the right people to support their business decision-making. Steven is a DZone MVB and is not an employee of DZone and has posted 143 posts at DZone. You can read more from them at their website. View Full User Profile

Legacy Code Preservation: Data Warehouse and Legacy Operations

05.04.2013
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A data warehouse preserves data.

It can be argued that a data warehouse preserves only data. This, however, is false.

To an extent, a data warehouse must also preserve processing details.

Indeed, a data warehouse exemplifies knowledge capture because the data and its processing steps are both captured.

The ETL process that prepares data for loading into the warehouse is tied to specific source applications that provide data in a known form and a known processing state. A warehouse isn't populated with random data. It's populated with data that is at a known, consistent state.

For example, when loading financial data, the various accounting applications (like the General Ledger) must be updated with precisely the same data that's captured for data warehouse processing. Failure to assure consistency between ledger and warehouse makes it difficult to believe that the warehouse data is correct.

Preserving Details

In some cases, legacy applications have a tangled architecture. Code can be repeated because of copy-and-paste programming. This can make it difficult to be sure that a data warehouse properly captures data in a consistent state.

What's distressingly common is to have a "code" or "status" field where the first or last position has been co-opted to have additional meanings. A "9" in the last position of a product number may be a flag for special processing.

These cryptic flags and indicators are difficult to identify in the first place. They are often scattered throughout the application code base. Sometimes they reflect work-arounds to handle highly-specialized situations. Other times, they're pervasive changes that were done via cryptic flags rather than make a first-class change to a file format.

When populating a warehouse, these codes and flags and secret processing handshakes need to be found and properly normalized. This may mean that an ETL program will recapitulate different pieces of special-case logic that's scattered around a number of legacy programs.
This is the essence of knowledge capture.

It also drives up the cost and expense of maintaining the ETL pipeline that feeds the data warehouse.
After all, the source application can make processing changes that aren't properly reflected in the ETL processing pipeline.

As if this isn't bad enough, many organization permit technology that makes processing even more obscure.

The Evils of Stored Procedures

In far too many cases, software architectures place code into two locations.
  • Application programs.
  • Data bases.
Putting code into a database is simply a mistake. There's no rational justification. None.

The irrational justifications include the following farcical claims.
  • Stored Procedures are faster.Not really. There's no reason why they should be faster, and simple benchmark measurements show that application programs outside the database will be as fast or faster than stored procedures. A process running outside the database doesn't compete for database resources the same way the stored procedure engine does.
  • Some processing is essential to data integrity.This is absurd, since it presumes that the folks writing stored procedures are trustworthy and folks writing non-stored procedure applications are a lying bunch of thieving scoundrels who will break the data integrity rules if given half the chance.
Let's look at this second justification.

The argument has two variants.
  1. Some logic is so essential to interpreting the contents of the database that it cannot meaningfully be packaged any other way.This makes the claim that all sharable programming technology (Java packages, Python modules, etc.) simply don't work, and the database is the only effective way to share code.
  2. Some logic is so essential to correct status of the database, that no application developer can be trusted to touch it.This presumes that application developers are willing to cut corners and break rules and force bad data into an otherwise pristine database. Data integrity problems come from those "other" developers. The DBA's can't trust anyone except the stored procedure author.
When confronted with other ways to share logic, the stored procedure folks fall back on "faster" or possibly the "Us vs. Them" nature of the second variant.

Stored Procedure Consequences

Stored procedures really are code. They should not be separated from the rest of the code base.

Stored procedures are maintained with different tools and through different organizations and processes. This leads to conflict and confusion.

It can also lead to weird secrecy.

A stored procedure can be difficult to extract from the database. It may require privileges and help from DBA's to locate the unencrypted original source text.

In a huge organization, it can take weeks to track down the right DBA to reveal the content of the stored procedure.

Why the secrecy?

Once exposed, of course, the stored procedure can then be rewritten as proper code, eliminating the stored procedure.

The proper question to ask is "Why is critical business knowledge encoded in so many different places?" Why not just application code? Why also try to encode some knowledge in database stored procedures? How does this bifurcation help make the origination more efficient?

Published at DZone with permission of Steven Lott, author and DZone MVB. (source)

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)