- Hadoop was software done wrong. The tests took days to run and never passed. It was documented horribly.
- The open source companies all knee capped other.
- Hadoop destroyed Apache's rep.
cloudera taught me a lot about how not to build a team and a company. Although Mike Olson was the greatest ceo I ever worked for (as opposed to founding a company with) the rest of the company became horribly political. We brought in managers from Oracle. We had crazy personal projects that went out of control like kudu and impalla. It breaks my heart
I ended up talking to one of the original investors afterwords and found out cloudera was a boy band startup. By the time I was in the way out the cto was playing counterstrike every day as I was heading home.
To this day there's some amazing engineers there and I just don't understand why
> cloudera taught me a lot about how not to build a team and a company. ...
It would be interesting if you are willing to share more about this. Are managers from Oracle play the reason? Did they bring you unnecessary disciplines against engineers? Any examples why you describe cloudera then "horribly political"?
"Did they bring you unnecessary disciplines against engineers" -> What's this mean?
Any examples why you describe Cloudera then "horribly political" -> Not off the top of my head actually i just remembered it burning me
Of course sometimes things fade away just because they are obviated by something clearly superior. For the map/reduce part of Hadoop that may well be Spark. But even now, in my experience, most Spark set ups are running on HDFS and using Yarn. shrug
Maybe we've also gotten over the "big data" hype wave, and there's more understanding that in more cases it might be cheaper & more performant to spend the budget on a single node with lots of ram rather than standing up and maintaining a distributed system.
Just saw this and might explain why Hadoop is out of the spotlight. In summary, Spark and Kafka seem to be better? I'm not sure as I'm just starting to enter this field.
Have grown to really appreciate Spark in the Hadoop space. Started with plans to go with Impala, then went to Hive due to stability concerns, and finally to Spark due to speed / flexibility. You can write code against a data frame, or write Spark SQL, so you still have SQL.
HDFS has benefits over other storage approaches, if you are running Spark in the same cluster you get data proximity. But you can go with a different storage back-end. That costs in performance. "Performance of multiple query and enrichment jobs concurrently executed resulted in 90% longer execution times."
Unless you really have BIG data, you're invoking a lot of maintenance overhead to support a cluster when you may do just fine without.
Haven't had the freedom to explore other possibilities until recently, very interested in how Spark on k8s is working out. (Same comment could be made here as above and elsewhere - do you really need k8s? But I want to play with k8s and learn more about it, so... for that purpose I 'need' it.)
And there's always the cloud route. You can run an EMR job that uses files in s3. There is a cost, but you do not need to support a cluster in the same way. Or if you're feeling adventurous, use Lambda.
And Spark isn't the only option. Have started learning about Dask, also looks very interesting for performing some of the same tasks.