Thanks Chris, for highlighting these not-widely-known capabilities of #elasticsearch. Relevance is an extremely powerful capability and possibilities are endless once you start using that. Another capability that we often forget is the capability to do analytics. I have worked with a complete, full-stack analytics platform that uses #elasticsearch at its core. Elastic is the storage engine and Kibana is the visualization layer, while Beats are used for specific capabilities as needed.
On Elastic's website, there are case studies talking about Petabytes of data. Companies like Uber and Fireeye have used Elastic's product to address use cases that are far beyond the classical 'enterprise search' use case. And that too at scale. Assuming there's been some use case-specific, intelligent modeling and capable hardware, Elastic is very, very fast. Plus, there are ways to make it work together with your preferred analytics/ML/DS toolset -- like R or Spark -- to provide customers with storage, analysis and visualization capabilities at the same time, especially when we are talking about an experimental, innovation platform within an enterprise.
Thank you for your comments. Absolutely spot on. I must admit, it's quite a gaping omission and I should have probably explained why in the article (it was intentional!).
The analytics abilities of ES are awesome as you say. The article was aimed at developers who may have had a misconception that ES is just another data store. The handful of features (a massive subset of those available!) were picked to highlight where ES may assist these developers to get certain functionality off the ground in a project where they may have previously sought far more complex solutions using databases.
I considered the analytics side to be a more complex feature not at the introductory level.
It's definitely worth mentioning though along with the uses and use cases you highlight. Thank you.
Thanks Chris, for highlighting these not-widely-known capabilities of #elasticsearch. Relevance is an extremely powerful capability and possibilities are endless once you start using that. Another capability that we often forget is the capability to do analytics. I have worked with a complete, full-stack analytics platform that uses #elasticsearch at its core. Elastic is the storage engine and Kibana is the visualization layer, while Beats are used for specific capabilities as needed.
On Elastic's website, there are case studies talking about Petabytes of data. Companies like Uber and Fireeye have used Elastic's product to address use cases that are far beyond the classical 'enterprise search' use case. And that too at scale. Assuming there's been some use case-specific, intelligent modeling and capable hardware, Elastic is very, very fast. Plus, there are ways to make it work together with your preferred analytics/ML/DS toolset -- like R or Spark -- to provide customers with storage, analysis and visualization capabilities at the same time, especially when we are talking about an experimental, innovation platform within an enterprise.
Thank you for your comments. Absolutely spot on. I must admit, it's quite a gaping omission and I should have probably explained why in the article (it was intentional!).
The analytics abilities of ES are awesome as you say. The article was aimed at developers who may have had a misconception that ES is just another data store. The handful of features (a massive subset of those available!) were picked to highlight where ES may assist these developers to get certain functionality off the ground in a project where they may have previously sought far more complex solutions using databases.
I considered the analytics side to be a more complex feature not at the introductory level.
It's definitely worth mentioning though along with the uses and use cases you highlight. Thank you.
Thanks. Will look forward to more such informative articles from you, Chris. Cheers!