Indeed, judging from trends highlighted in DB-Engines January 2018 rankings, “avoidance” doesn’t remotely describe what’s happening with the top cloud database services. Risk-averse enterprises may shy away from doing business with small-fry NoSQL startups, but they simply can’t avoid doing business with AWS and Microsoft. Or there would be, if it weren’t for Amazon Web Services, Microsoft, and, increasingly, Google. There’s simply too much risk involved with changing databases. While NoSQL has started to change this ( MongoDB being the best example, thanks to its flexible schema document data store), databases remain the least likely enterprise infrastructure to change. Most data remains firmly ensconced in traditional RDBMSs like Oracle, MySQL, and Microsoft SQL Server. Cloud databases are where the (new) action is ![]() Meanwhile, with Cosmos DB, Microsoft seems to be heading in the opposite direction, with a one-size-fits-all approach to data that seems to be catching fire. AWS has introduced powerful options for familiar data needs: Amazon Redshift for data warehousing, Amazon Aurora/RDS for traditional relational workloads, and AWS DynamoDB for NoSQL. What’s emerging is a very different approach to data across competing cloud vendors. While everything was “as you were” for AWS database leader DynamoDB in 2017, according to DB Engines’ comprehensive ranking, Cosmos DB jumped 27 places, from 58 to 31. More specifically, the cumbersomely named Microsoft Azure Cosmos DB did not, rocketing past AWS Redshift, as Begin founder Brian Leroux first noticed. Also, thanks to its c-store extension, PostgreSQL can be turned into a columnar database, making it an affordable alternative to commercial OLAPs.įinally, if you are considering moving from OLTPs abused as OLAPs to “real” OLAPs like Redshift, I encourage you to learn how to use Redshift’s COPY Command so that you can start seeing your data inside Redshift.You might have gone into an alcohol-induced hibernation over the holidays, but cloud databases did not. This is a more legitimate choice than above for starting an analytics platform because of Postgres’s solid analytic User Defined Functions (UDFs). As there are multiple alternatives, avoid this “inexpensive” solution because you’ll be paying the price in other places eventually. MySQL is not optimized in any way for reading large ranges of data and its support for analytic functions is weak. Although this setup is extremely common, it is one of the least productive ways to approach analytics.
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