Articles

Data Management Patterns for Microservices

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This is an article from DZone’s 2022 Database Systems Trend Report.

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One of the key components of microservices is how to manage and access data. The means to do that are different compared to traditional monolithic or three-tier applications. Some patterns are quite common, but others are specific and need to be evaluated before being incorporated into a solution. We will briefly go over some of these common database patterns for microservices before exploring CQRS (including how it differs from CRUD) and, finally, look at how it can be combined with event sourcing.

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Introduction to Couchbase for Oracle Developers and Experts: Part 5: Statements and Features

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SQL is the only 22nd century tool available in 21st century

Here are the previous articles comparing architecture, database objects, data types, and data modeling of Oracle with Couchbase. This will focus on SQL support.  

Oracle was the first and staunch supporter of SQL. Oracle’s SQL implementation beat IBM to market by two years.  That changed the fortune of one or two people. :-) All of the modern relational databases implement SQL. So much so, the relational databases are sometimes called SQL databases, much to the chagrin of C. J. Date.  Nations are known by their languages… English, French, and American(!). It’s not a stretch for a class of database systems to be known by their languages as well.  SQL has been so effective, many big data and non-relational systems have picked up SQL as the lingua franca. SQL is here to stay, even for NoSQL systems. 

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Debunking Four Metadata Myths

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Data is being accumulated at an alarming rate due to the growth of unstructured data. Within that is metadata, which allows us to quickly find data files by identifying certain properties. It wasn’t until recently that developers had to pay closer attention due to the overwhelming effect it was having on our data engines. Metadata used to just be a thing that could be stored in memory behind the scenes without any worry, but its expansion presents the growing concern of where to store it and how to manage it.

Every database system, whether SQL or NoSQL, uses a data engine (aka storage engine), embedded or not, to manage how data is stored. Our ever-growing volume of data has outgrown our data engines’ infrastructures and with that, they are stretched to their limits and forced to make painful trade-offs necessary to support the scale and performance requirements of modern datasets.

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