ETL with Apache Spark

In continuation to my previous post on Modern Data Warehouse Architecture, in this post I’ll give an example  using PySpark API from Apache Spark for writing ETL jobs to offload the data warehouse.

Spark is lightening-fast in data processing and works well with hadoop ecosystem, you can read more about Spark at Apache Spark home. For now, let’s talk about the ETL job. In my example, I’ll merge a parent and a sub-dimension (type 2) table form MySQL database and will load them to a single dimension table in Hive with dynamic partitions. When building a warehouse on hive, it is advisable to avoid snow-flaking to reduce unnecessary joins as each join task creates a map task. Just to raise the curiosity, the throughput on a stand alone Spark deployment for this example job is 1M+ rows/min. Continue reading


Modern Data Warehouse Architecture

With the changing trends in the world of BI and the Big Data wave everywhere, a lot of organizations have started initiatives to explore how it fits in. To leverage the data ecosystem at it’s fullest potential, it is necessary to think forward and ingest new technology pieces in the right place. That way, in a long run, both business and IT will reap its benefits.

Here’s an interesting prediction by Gartner

By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.

Imagine all the time, money and efforts you’ll save off your existing data and infrastructure components if the Big Data implementation goes well. The architecture diagram below , is a conceptual design of how you can leverage the computation power of Hadoop ecosystem in your traditional BI / Data warehousing processes along with all the real time analytics and data science. They call it a data lake, warehouse is old school now.


Alright, having a Hadoop ecosystem saves the computational time and provides all bells and whistles of real time analytics but “how does it save money? Continue reading

DataWarehouse vs BigData

To Be or not To Be is the question to ask today. I’m not being Hamlet here but with the evolution of Big Data and looking at the current technology trends, I’m curious to discover the cases where it can overcome conventional Data warehouse, where it cannot and most important what are the areas where both DW and BigData can be implemented in conjunction?

Data Warehousing concept has been in place for more than last four decades now  while Big Data, In Memory and the Cloud concept started prevailing in early 2Ks and are in high demand today. Between now and then the data has grown exponentially in the network and the competitive analysis of this data has lead  to evolution of new tools and BI concepts.  Does it mean a slow sunset for DW? I’d say it’s a myth as the business cases differ, an ideal approach will be a blend of both for sure. Continue reading