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Don't really get this article. SQL never went away or got less popular. Always been in the background doing its awesome thing.

d4nt 7y ago
I agree, NoSQL is still working it’s way along the hype curve but a huge amount of business data still lives in relational databases. The fact that there’s money to be made supporting this market is not big news.
mnky9800n 7y ago
In data science it has been an uphill battle between the people who only think csv exists and those who switch to a new nosql flavor every 6 months
- overcast 7y ago
  CSV/Excel has run the world since the beginning, that's going to be a never ending battle. Large orgs are still entrenched into SQL when their enterprise ERP/MRP systems run off Oracle, and their analysts are extracting reports to Excel.
  - tartoran 7y ago
    Right after flat files which are still in use on mainframes.
- prepend 7y ago
  I think it’s artificially presented as an exclusive option between sql and csv/nosql/whatever in data science. I know plenty of people who are comfortable in both. But I don’t know any “data science” people as good at sql as a good dba. But that’s ok. I think a good analysis requires both sql and some flat file, if only for reproducibility. I’ve encountered many point in time analyses that can’t be recreated because they were just sql against db and there’s no way to reproduce the result months later. Are their methods accurate? Or auditable? It’s much harder because the source data is changed. So I like talking about only-sqlers about the importantance of incorporating the data management of extracts and archives into the overall solution. And doing so in a way that doesn’t try to redesign the database trying to avoid the need for users to file away point in time snapshots of data.
angry_cactus 7y ago
Google Trends confirms, even MongoDB is only a fraction of SQL.
- collyw 7y ago
  Thank god. I am working with it now and its crap in comparison. I wrote a fairly complex mango query a while back, and trying to understand it now is a nightmare. Compare that to SQL, which is one of the most readable languages out there in my opinion.
  - edgyquant 7y ago
    To me Mongo is like Redis: it has its place in the pipeline but is not a proper tool to replace a rdb nor should anyone pretend it is.
reilly3000 7y ago
I think the big contender has been dataframe processing using Pandas, R and others. These have their own methods for doing the type of data munging and analysis that has been the traditional territory of SQL. I won’t soon forget the half day I plowed into getting the right Pandas pipeline dialed in only to realize I could have written a better SQL query to fetch the correct data to begin with.
- prepend 7y ago
  I’m sure there will be an xkcd about this soon. I’ve also tried to do things in sql only to realize that I could have done it easier in pandas. I think it’s usually a mix of both for me, but I try to at least get a decent “tidy” dataset out of sql and then do all the manipulation in pandas because a python pipeline is more portable than a sql pipeline, I think.

d4nt 7y ago

I agree, NoSQL is still working it’s way along the hype curve but a huge amount of business data still lives in relational databases. The fact that there’s money to be made supporting this market is not big news.

mnky9800n 7y ago

In data science it has been an uphill battle between the people who only think csv exists and those who switch to a new nosql flavor every 6 months

overcast 7y ago
CSV/Excel has run the world since the beginning, that's going to be a never ending battle. Large orgs are still entrenched into SQL when their enterprise ERP/MRP systems run off Oracle, and their analysts are extracting reports to Excel.
- tartoran 7y ago
  Right after flat files which are still in use on mainframes.
prepend 7y ago
I think it’s artificially presented as an exclusive option between sql and csv/nosql/whatever in data science. I know plenty of people who are comfortable in both. But I don’t know any “data science” people as good at sql as a good dba. But that’s ok. I think a good analysis requires both sql and some flat file, if only for reproducibility. I’ve encountered many point in time analyses that can’t be recreated because they were just sql against db and there’s no way to reproduce the result months later. Are their methods accurate? Or auditable? It’s much harder because the source data is changed. So I like talking about only-sqlers about the importantance of incorporating the data management of extracts and archives into the overall solution. And doing so in a way that doesn’t try to redesign the database trying to avoid the need for users to file away point in time snapshots of data.

angry_cactus 7y ago

Google Trends confirms, even MongoDB is only a fraction of SQL.

collyw 7y ago
Thank god. I am working with it now and its crap in comparison. I wrote a fairly complex mango query a while back, and trying to understand it now is a nightmare. Compare that to SQL, which is one of the most readable languages out there in my opinion.
- edgyquant 7y ago
  To me Mongo is like Redis: it has its place in the pipeline but is not a proper tool to replace a rdb nor should anyone pretend it is.

reilly3000 7y ago

I think the big contender has been dataframe processing using Pandas, R and others. These have their own methods for doing the type of data munging and analysis that has been the traditional territory of SQL. I won’t soon forget the half day I plowed into getting the right Pandas pipeline dialed in only to realize I could have written a better SQL query to fetch the correct data to begin with.

prepend 7y ago
I’m sure there will be an xkcd about this soon. I’ve also tried to do things in sql only to realize that I could have done it easier in pandas. I think it’s usually a mix of both for me, but I try to at least get a decent “tidy” dataset out of sql and then do all the manipulation in pandas because a python pipeline is more portable than a sql pipeline, I think.