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Based on our record, Apache Cassandra should be more popular than Google Cloud DNS. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Google Cloud DNS: This is Google Cloud's offering, designed to provide high-performance and premium networking. - Source: dev.to / 8 months ago
Google's enterprise-grade DNS is "Google Cloud DNS" [1]. It's not going anywhere. Google Domains is a consumer-grade product, in the sense that it is lacking most of the features (access control, bulk management) that a large company needs, though it was not lacking in stability / availability. And you could easily hook Google Domains up to Google Workspace to light up email for a small business. Feels like a good... - Source: Hacker News / 11 months ago
Why not use Cloud DNS and Cloud Storage to host a static website? Source: over 1 year ago
Another solution similar to DNS stubs is to use a managed DNS product. In the case of GCP there is the Cloud DNS product, which handles replicating local DNS entries up to the VPC level for resolution by outside clusters, or even virtual machines within the same VPC. This option offers a lot of benefits, including:. - Source: dev.to / over 2 years ago
You are 100% right that the domain is the keys to the kingdom. Definitely only use registrars and DNS providers that have 2FA. Google has a registrar now, as well as DNS in GCP https://cloud.google.com/domains/docs/register-domain and https://cloud.google.com/dns. By using those you can leverage your Google account's security (use separate accounts for admin level access on GCP and enforce hardware 2FA), and... - Source: Hacker News / about 2 years ago
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / 24 days ago
HBase and Cassandra: Both cater to non-structured Big Data. Cassandra is geared towards scenarios requiring high availability with eventual consistency, while HBase offers strong consistency and is better suited for read-heavy applications where data consistency is paramount. - Source: dev.to / 2 months ago
Dear r/python, we are happy to present you with our first open-source project. We have managed to implement a new driver for Python that works with Apache Cassandra, ScyllaDB and AWS Keyspaces. Source: 8 months ago
NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and... - Source: dev.to / 8 months ago
To use NoSQL databases with code, you first need to choose a NoSQL database that suits your requirements. Some popular examples of NoSQL databases are MongoDB, Cassandra, Redis, and DynamoDB. Each of these databases has its own set of APIs and drivers that can be used to interact with them. Here, I'll use MongoDB as an example and explain how to perform CRUD operations using Python and its PyMongo package. - Source: dev.to / about 1 year ago
Amazon Route 53 - Amazon Route 53 is a highly available and scalable DNS web service.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
ClouDNS - ClouDNS is a platform that allows users to keep their websites, data, and network security all the time.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Cloudflare DNS - Install the free app that makes your phone’s Internet more fast, private, and reliable.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.