My observation that traditional database technologies are transforming themselves into 'hybrids' (as far document-oriented data types go). Example is Postgres.

My observation that traditional database technologies are transforming themselves into 'hybrids' (as far document-oriented data types go). Example is Postgres. Now has with JSONB as document-oriented field type. Now has Postgres XL, as horizontally scalable ACID database. Will have, by approximately august', abiltity to maintain views in memory (aka lambda.architecture speed layer) via PipelineDB extension, for fast streaming analytics. It seems that a combination of Postgres (with extensions) + Kafka +Redis -- is a strong stack for lambda architecture and, as initial data hub component of the overall puzzle While Spark (or even Python+Dask) can be viewed as distributed data analytics platforms that replaces 'non-ui centric' BI. I think ui-centric BI (eg adhoc reports/visualizatiosn) are going to be still dominated within enterprises by Tableu/QlickView type of solutions. For traditional BI-oriented data marts (that organized downstream from the datahub) -- probably traditional column oriented databases, and the new open source ones make sense. To me ,the promise of hadoop being a silver bullet for 'all the big data needs' -- was always nothing more than unsubstantiated hype. So it definitely failed the ones who believed, the hype -- but did not fail others who did not buy into it.