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Why Kubernetes Is the Best Technology for Running a Cloud-Native Database

We’ve been talking about migrating workloads to the cloud for a long time, but a look at the application portfolios of many IT organizations demonstrates that there’s still a lot of work to be done. In many cases, challenges with persisting and moving data in clouds continue to be the key limiting factor slowing cloud adoption, despite the fact that databases in the cloud have been available for years. 

For this reason, there has been a surge of recent interest in data infrastructure that is designed to take maximum advantage of the benefits that cloud computing provides. A cloud-native database achieves the goals of scalability, elasticity, resiliency, observability, and automation; the K8ssandra project is a great example. It packages Apache Cassandra and supporting tools into a production-ready Kubernetes deployment.

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Why a Cloud-Native Database Must Run on K8s

We’ve been talking about migrating workloads to the cloud for a long time, but a look at the application portfolios of many IT organizations demonstrates that there’s still a lot of work to be done. In many cases, challenges with persisting and moving data in clouds continue to be the key limiting factor slowing cloud adoption, despite the fact that databases in the cloud have been available for years.

For this reason, there has been a surge of recent interest in a data infrastructure that is designed to take maximum advantage of the benefits that cloud computing provides. A cloud-native database is one that achieves the goals of scalability, elasticity, resiliency, observability, and automation; the K8ssandra project is a great example. It packages Apache Cassandra and supporting tools into a production-ready Kubernetes deployment.

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DynamoDB Autoscaling Dissected: When a Calculator Beats a Robot

TLDR; Choosing the Right Mode for DynamoDB Scaling

Making sense of the multitude of scaling options available for DynamoDB can be quite confusing, but running a short checklist with a calculator can go a long way to help.

  1. Follow the flowchart below to decide which mode to use.
  2. If you have historical data of your database load (or an estimate of load pattern), create a histogram or a percentile curve of the load (aggregate on hours used) – this is the easiest way to observe how many reserved units to pre-purchase. As a rule of thumb purchase reservations for units used over 32% of the time when accounting for partial usage and 46% of the time when not accounting for partial usage.
  3. When in doubt, opt for static provisioning unless your top priority is avoiding being out of capacity – even at extreme costs.
  4. Configure scaling limits (both upper and lower) for provisioned autoscaling. You want to avoid out-of-capacity during outages and extreme costs in case of rogue overload (DDOS anyone?)
  5. Remember that there is no upper limit on DynamoDB on-demand billing other than the table’s scaling limit (which you may have requested raising for performance reasons). Make sure to configure billing alerts and respond quickly when they fire.

The Long Version: Configuring DynamoDB Tables

Before we dive in, it’s useful to be reminded of DynamoDB different service models and their scaling characteristics: DynamoDB tables can be configured to be either “provisioned capacity” or “on-demand”, and there’s a cooldown period of 24 hours before you can change again.

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MySQL to Redshift: 4 Ways to Replicate Your Data

MySQL is the most popular open source cloud database in the world, and for good reason. It’s powerful, flexible, and extremely reliable. Tens of thousands of companies use MySQL to power their web-based applications and services every day.

But when it comes to data analytics, it’s a different story. MySQL is quickly bogged down by even the smallest analytical queries, putting your entire application at risk of crashing. As one FlyData customer said to us, “I have nightmares about our MySQL production database going down.”

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Ensuring SQL Server High Availability in the Cloud

Theoretically, the cloud seems tailor-made for ensuring high availability (HA) and disaster recovery (DR) solutions in mission critical SQL Server deployments. Azure, AWS, and Google have distributed, state-of-the-art data centers throughout the world. They offer a variety of SLAs that can guarantee virtual machine (VM) availability levels of 99.95% and higher.

But deploying SQL Server for HA or DR has always posed a challenge that goes beyond geographic dispersion of data centers and deep levels of hardware redundancy. Configuring your SQL Server for HA or DR involves building a Windows Server Failover Cluster (WSFC) that ensures not only the availability of different machines running SQL Server itself but also — and most importantly — the availability of storage holding the data in which SQL Server is interacting.

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Are You Taking the Right Approach to Cloud Databases?

Trends in cloud data storage continue to accelerate at a rapid pace. Now more than ever, organizations must evaluate their current and future data storage needs to find solutions that align with business goals. While cloud databases are relatively new to the scene, they show tremendous prospect in securing and managing data.

In selecting our topic for this Trend Report, we found the amount of promise and advancement in the space to be unparalleled. This Report highlights DZone’s original research on cloud databases and contributions from the community, as well as introduces new offerings within DZone Trend Reports.