Articles

Decision Making: Relational or NoSQL

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For some time, products have been leaning towards NoSQL databases because of the number of advantages that they provide compared to Relational databases (RDBMS), especially in today’s distributed systems. There is always a pressure of delivering things faster to make it live to end users. But does that mean relational databases won’t be able to compete with NoSQL databases, provided Relational Databases are still best-known for adhering to the ACID property?

Here, I am going to explore the possibilities from Relational Databases perspective and how they come a long way to compete with NoSQL Databases. I am going to compare two managed databases from AWS, DynamoDB and Aurora, to see if it’s really worth giving credit to one type of database over another.

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Distributed SQL Essentials

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Distributed SQL databases combine the resilience and scalability of a NoSQL database with the full functionality of a relational database. In this Refcard, we explore the essentials to building a distributed SQL architecture, including key concepts, techniques, and operational metrics.
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Getting Started With Distributed SQL

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In recent years, NoSQL distributed databases have become common, as they are built from the ground up to be distributed. Yet they force difficult design choices such as choosing availability over consistency, data integrity, and ease of query to meet their applications’ need for scale. This Refcard serves as a reference to the key characteristics of distributed SQL databases and provides information on the benefits of these databases, as well as insights into query design and execution.
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Analyze This: MongoDB and Couchbase Analytics

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The purpose of computing is insight, not numbers.  — Richard Hamming

The spiral of running the business, analyzing what to change & what to change to, and then changing the business is an eternal one. Do the right analysis, your spiral will get larger.  Else, you’ll spiral down.

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SQL vs NoSQL: That Is the Query

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SQL vs NoSQL: Time for Some Introspection

SQL vs NoSQL
Does the SQL vs NoSLQ Question Wrench Your Soul?

In this post, we look at the long and storied history of the SQL vs NoSQL debate. The articles included stretch way back to the ancient days of the early 2010s and go through 2019  – each exploring a different aspect of the SQL vs NoSQL quandry. 

After all these years duking it out, is either solution winning the battle for developers’ hearts and minds? 

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CouchDB REST API for Document CRUD Operations — Examples With Postman

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In this tutorial, we will learn how to perform CRUD operations with the help of HTTP requests firing from Postman.

Assumptions

  1. The CouchDB Server is running at http://127.0.0.1:5789/
  2. Database name is tutorialkart.
  3. The database contains the following two documents (viewed in table view).

Documents in CouchDB Database

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ArangoDB: One Core/Query Language and Multiple Data Models: Part 2

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ArangoDB is a multi-model NoSQL database. NoSQL databases have four types: key-value, column, document, and graph, every kind with specific persistence structures to solve particular problems. ArangoDB covers three NoSQL types: key-value, document, and graph. There is a post that talks about the key-value and document, but this post will explain how to connect with Java and Jakarta EE technology.

The graph has a unique structure that makes it more natural to do a deeper relationship, even more than the relational database technology. The NoSQL Graph database has success cases within the recommendation system, such as that does exist on Social Media and Netflix. This post talks about the graph structure more deeply.

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Searching JSON: Comparing Text Search in Couchbase and MongoDB

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"Apps without search is like Google homepage without the search bar."

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It’s hard to design an app without a good search. These days, it’s also hard to find a database without a built-in search. MySQL to NoSQL, Sybase to Couchbase, every database has text search support — built-in like Couchbase or via integration to Elastic — as is the case in Cassandra. Unlike SQL, text search functionality isn’t standardized. Every application needs best of the breed search, but not every database provides the same text search functionality. It’s important to understand the available feature, performance of each text search implementation and choose what fits your application need. After motivating text search, you’ll learn about the text search features you’d need for an effective, compare and contrast those features in MongoDB and Couchbase with examples.

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NoSQL vs. NewSQL: Evaluating Database Technologies for 2019

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NoSQL and NewSQL databases are popular solutions in the data management space. Both technologies offer their own unique benefits, but depending on the use case or application, one option may be a better fit than the other. And as you plan for next year, it’s important to have a clear understanding on which database technology is best suited to power your initiatives in 2019 and beyond.

WATCH NOW

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It’s the Workload, Stupid!

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As an application architect, eventually, you’d choose the database or database service to power your newest application or a microservice. Selecting one of the databases among relational databases was easier. The use cases were roughly divided into OLTP and OLAP (decision support). The workload differences between OLTP and OLAP were well known. OLTP workloads consist of short transactions on few random rows, expecting millisecond responses on pre-compiled queries; OLAP workloads consist of data loads, long-running queries scanning millions of rows of a fact table of a star/snowflake schema. Each had the performance benchmark and TCO well defined, measured and audited via TPC benchmarks. You can make use of these numbers, approximate your workload, understand the needs and capabilities match on other fronts like administration.

Then, there are NoSQL databases. NoSQL databases were invented to handle the webscale performance of operational applications. It had to be elastic to handle the scale and tolerate nodes going down (aka partition tolerance). That sparked the innovation to create databases on a variety of data models and use cases. There are databases for JSON, graphs, time-series and more. From Azure databases to ZODB, from Couchbase to Cassandra. MongoDB to TiDB, spatial to JSON databases — so many different kinds of databases. In fact, NoSQL-databases.org lists 225 databases as of November 2018.

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