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What are the most common use cases and scenarios for using cloud databases?

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Cloud databases are databases that run on cloud computing platforms, such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure. They offer many benefits over traditional on-premises databases, such as scalability, availability, cost-effectiveness, and security. But how do you know when to use a cloud database and what type of cloud database suits your needs? In this article, we will explore some of the most common use cases and scenarios for using cloud databases, and what features and factors to consider when choosing a cloud database solution.

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Pavan Belagatti

GenAI Evangelist | Developer Advocate | 40k Newsletter Subscribers | Tech Content Creator | Empowering AI/ML/Data…
Stephen Bramer

Application Manager, Full Stack Software Engineer

1 Data analytics

One of the most popular use cases for cloud databases is data analytics. Data analytics involves processing large volumes of data to extract insights, patterns, and trends that can help businesses make better decisions. Cloud databases can provide fast and flexible data storage and processing capabilities, as well as integration with various data sources and tools. For example, you can use a cloud database to store and query data from web logs, social media, sensors, or other sources, and then use a cloud service like Google BigQuery or Amazon Redshift to perform advanced analytics and visualization.

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Santhanakrishnan Ramaswamy

AI and Analytics at KLA
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In my experience, I have seen on-premise database servers being bottlenecks during month-end/quarter-end periods due to the inability to scale up/scale out is a major drawback. It is expensive for IT organizations to invest in beefed up servers to handle month-end/quarter-end demand and let these servers lay unused during low demand times. Cloud databases are the way forward to handle these use cases.

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2 Web and mobile applications

Another common use case for cloud databases is web and mobile applications. Web and mobile applications often require dynamic and interactive data access, as well as high availability and scalability to handle user demand. Cloud databases can offer these features by providing distributed and replicated data storage, auto-scaling, load balancing, and backup and recovery options. For example, you can use a cloud database like MongoDB or Firebase to store and sync data for your web or mobile app, and then use a cloud platform like AWS Lambda or Google App Engine to run your app logic.

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3 Internet of Things

A third common use case for cloud databases is Internet of Things (IoT). IoT refers to the network of devices and sensors that collect and exchange data over the internet. IoT applications often generate massive amounts of data that need to be stored, processed, and analyzed in real time or near real time. Cloud databases can provide these capabilities by offering low-latency, high-throughput, and scalable data storage and processing solutions. For example, you can use a cloud database like Apache Cassandra or Amazon DynamoDB to store and stream data from your IoT devices, and then use a cloud service like AWS IoT or Google Cloud IoT to manage and monitor your devices.

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4 Machine learning

A fourth common use case for cloud databases is machine learning. Machine learning involves using algorithms and models to learn from data and make predictions or recommendations. Machine learning applications often require large and complex data sets, as well as high-performance and parallel computing resources. Cloud databases can provide these features by offering scalable and flexible data storage and processing solutions, as well as integration with various machine learning frameworks and tools. For example, you can use a cloud database like Google Cloud Storage or Amazon S3 to store and access your data sets, and then use a cloud service like Google Cloud AI Platform or Amazon SageMaker to train and deploy your machine learning models.

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5 Hybrid and multi-cloud

A fifth common use case for cloud databases is hybrid and multi-cloud. Hybrid and multi-cloud refer to the scenarios where you use more than one cloud provider or combine cloud and on-premises resources to meet your data needs. Hybrid and multi-cloud can offer advantages such as cost optimization, performance improvement, risk mitigation, and regulatory compliance. Cloud databases can support these scenarios by offering cross-cloud and cross-region data replication, migration, and synchronization solutions. For example, you can use a cloud database like Google Cloud Spanner or Azure Cosmos DB to store and access your data across multiple cloud regions or providers, and then use a cloud service like Google Cloud Anthos or Azure Arc to manage and orchestrate your hybrid and multi-cloud environment.

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Stephen Bramer

Application Manager, Full Stack Software Engineer
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This example is going to be most practical for many organizations because it incorporates all of the advantages of the previous use cases in addition to two key, unique advantages. The ability to mix databases, and potentially combine cloud with on-premises databases gives the broadest set of opportunities for economizing and applying investment to need in the most pinpoint fashion. In particular, organizations new to cloud technologies, and just beginning to move into that space, might find this approach best solves a lot of the initial challenges.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Pavan Belagatti

GenAI Evangelist | Developer Advocate | 40k Newsletter Subscribers | Tech Content Creator | Empowering AI/ML/Data Startups ??
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One more important point of Cloud databases is that they offer a cost-effective solution for establishing and overseeing test and development environments. Developers can swiftly generate database instances without the need for dedicated hardware, thereby eliminating upfront infrastructure costs.

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What are the most common use cases and scenarios for using cloud databases?

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1 Data analytics

2 Web and mobile applications

3 Internet of Things

4 Machine learning

5 Hybrid and multi-cloud

6 Here’s what else to consider

Database Engineering

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