azure - Choosing a long-term storage/analytic system? -
a brief summary of project i'm working on:
i hired web dev intern @ small company (part of larger corporation) close state college attend. past couple months, myself , 2 other interns have been working on front-end back-end. company prototyping adding sensors products (oil/gas industry); tasked building portal customers login to see data machines if they're not near them.
basically, we're collecting sensor data (~ten sensors/machine) , it's sent us. we're stuck determining best way store , analyze long term data. have redis cache set fast access front-end, lastest set of data each machine stored. historical data, (and coworkers) having tough time deciding best route go. our whole project based in vs (c#/razor) azure integration (which amazing way), i'd keep long term storage there well. far can tell, hdinsight + data in blob seems best option, i'm green when comes backend solutions. input older developers may have more experience in area, developers here besides couple older members more involved in engineering side of things vs. development.
so, professionals of stack overflow, recommendation long-term data storage , analytics?
ps: apologize if have hdinsight confused. understand, maps data in blob storage hbase easier analytics? hadoop/hbase confuses me.
my first recommendation azure table storage. provides highly scalable , low cost data archival solution. if designed properly, can decent query performance. refer azure storage table design guide more details.
my second choice azure documentdb service nosql document database. costs bit more querying data more flexible.
you should go hdinsight when have specific need it's resource-intensive , expensive service. once identify specific requirement big-data analysis that's when import data , process hdinsight.
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