Q:

Social-networking: Hadoop, HBase, Spark over MongoDB or Postgres?

I am architecting a social-network, incorporating various features, many powered by big-data intensive workloads (such as Machine Learning). E.g.: recommender systems, search-engines and time-series sequence matchers.
Given that I currently have 5< users—but forsee significant growth—what metrics should I use to decide between:

Spark (with/without HBase over Hadoop)
MongoDB or Postgres

Looking at Postgres as a means of reducing porting pressure between it and Spark (use a SQL abstraction layer which works on both). Spark seems quite interesting, can imagine various ML, SQL and Graph questions it can be made to answer speedily. MongoDB is what I usually use, but I've found its scaling and map-reduce features to be quite limiting.

A:

I think you are on the right direction to search for software stack/architecture which can:

handle different types of load: batch, real time computing etc.
scale in size and speed along with business growth
be a live software stack which are well maintained and supported
have common library support for domain specific computing such as machine learning, etc.

To those merits, Hadoop + Spark can give you the edges you need. Hadoop is relatively mature for now to handle large scale data in a batch manner. It supports reliable and scalable storage(HDFS) and computation(Mapreduce/Yarn). With the addition of Spark, you can leverage storage (HDFS) plus real-time computing (performance) added by Spark.
In terms of development, both systems are natively supported by Java/Scala. Library support, performance tuning of those are abundant here in stackoverflow and everywhere else. There are at least a few machine learning libraries(Mahout, Mlib) working with hadoop, spark.
For deployment, AWS and other cloud provider can provide host solution for hadoop/spark. Not an issue there either.

