Technical Debt

Daniel Hutmacher takes on the idea of technical debt:

When you think of technical debt, you may think only of classic shortcuts like making assumptions about the data, not using a TRY-CATCH block or perhaps hard-coding a manual correction into a stored procedure or view.

But I would argue that not paying attention to performance is just as much a technical debt. And rather than just crashing with an error message, performance issues are not always easy to just fix in production when your business users are working late to meet their deadlines, or when your web request are timing out. Start thinking of performance as an important part of your development process – half the job is getting the right data in the right place, the other half is making sure that your solution will handle double or triple the workload, preferably under memory pressure conditions with other workloads running at the same time.

Read the whole thing.

Related Posts

Using Databricks Delta In Lieu Of Lambda Architecture

Jose Mendes contrasts the Lambda architecture with the Databricks Delta architecture and gives us a quick example of using Databricks Delta: The major problem of the Lambda architecture is that we have to build two separate pipelines, which can be very complex, and, ultimately, difficult to combine the processing of batch and real-time data, however, […]

Read More

An Overview Of Apache Kafka

Leona Zhang has a series going on Apache Kafka.  Part one covers some of the concepts around messaging systems: There is a difference between batch processing applications and stream processing applications. A visible boundary determines the most significant difference between batch processing and stream processing. If it exists, it is called batch processing. For example, […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031