Architecting Semi-Structured Data Solutions

James Serra gives four architectural scenarios for handling large quantities of semi-structured data:

An evolution of the three previous scenarios that provides multiple options for the various technologies.  Data may be harmonized and analyzed in the data lake or moved out to a EDW when more quality and performance is needed, or when users simply want control.  ELT is usually used instead of ETL (see Difference between ETL and ELT).  The goal of this scenario is to support any future data needs no matter what the variety, volume, or velocity of the data.

Hub-and-spoke should be your ultimate goal.  See Why use a data lake? for more details on the various tools and technologies that can be used for the modern data warehouse.

Check it out for a high-level architectural view of contemporary warehousing choices.  I prefer having both systems in play:  the EDW answers known business questions and gives you back report information relatively quickly; whereas the Hadoop cluster allows you to do spelunking, data cleansing, and answer unanticipated business questions.

Related Posts

Calculating YARN Utilization Metrics

Dmitry Tolpeko shows how you can calculate per-second cluster utilization measures from YARN’s resource manager logs: But even if you query YARN REST API every second it still can only provide a snapshot of the used YARN resources. It does not show which application allocates or releases containers, their memory and CPU capacity, in which […]

Read More

Spark Streaming DStreams

Manish Mishra explains the fundamental abstraction of Spark Streaming: Before going into details of the operations available on the DStream API, let us look at the input sources from which we can start a Stream. There are multiple ways in which we can get the inputs from e.g. Kafka, Flume, etc. Or simple Idle files. […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031