What are the emerging trends and technologies for data backup and recovery in application support?

1 Cloud-based backup and recovery

One of the most prominent trends in data backup and recovery is the adoption of cloud-based solutions, which offer scalability, flexibility, and cost-efficiency. Cloud-based backup and recovery enable you to store and access your data from anywhere, anytime, and on any device, as well as leverage the cloud provider's infrastructure, security, and expertise. However, cloud-based backup and recovery also come with some risks and limitations, such as data sovereignty, bandwidth, latency, and vendor lock-in. Therefore, you need to carefully evaluate your data backup and recovery requirements, policies, and SLAs before choosing a cloud-based solution.

2 Continuous backup and recovery

Another trend in data backup and recovery is the shift from periodic or scheduled backups to continuous or real-time backups, which capture every change or transaction in your data as it happens. Continuous backup and recovery enable you to minimize data loss and downtime, as well as recover your data to any point in time. However, continuous backup and recovery also require more storage space, network bandwidth, and processing power, as well as more sophisticated tools and techniques to manage and monitor your data backup and recovery processes. Therefore, you need to balance your data backup and recovery frequency, granularity, and performance according to your business needs and priorities.

3 AI-powered backup and recovery

Another trend in data backup and recovery is the integration of artificial intelligence (AI) and machine learning (ML) capabilities, which enhance the automation, efficiency, and intelligence of your data backup and recovery operations. AI-powered backup and recovery can help you to optimize your data backup and recovery policies, schedules, and resources, as well as detect and prevent potential issues, anomalies, or threats. AI-powered backup and recovery can also help you to analyze and extract insights from your data backup and recovery activities, such as usage patterns, trends, and performance metrics. However, AI-powered backup and recovery also require more data quality, governance, and security measures, as well as more skills and knowledge to leverage the AI and ML technologies.

4 Data protection as a service

Another trend in data backup and recovery is the emergence of data protection as a service (DPaaS), which is a cloud-based model that provides end-to-end data protection solutions, including backup, recovery, replication, archiving, and encryption. DPaaS allows you to outsource your data protection tasks to a third-party provider, who manages and maintains the data protection infrastructure, software, and processes on your behalf. DPaaS can help you to reduce your data protection costs, complexity, and risks, as well as access the latest data protection technologies and best practices. However, DPaaS also entails some challenges and trade-offs, such as data privacy, compliance, and control, as well as vendor reliability and compatibility.

5 Data backup and recovery for containers

Another trend in data backup and recovery is the adaptation of data protection solutions for containerized applications, which are increasingly popular in modern application development and deployment. Containers are lightweight, portable, and isolated units of software that run on a shared operating system, which enable faster, easier, and more scalable application delivery. However, containers also pose some data backup and recovery challenges, such as data persistence, consistency, and portability, as well as container orchestration, management, and security. Therefore, you need to adopt data protection solutions that are designed for or compatible with containers, such as container-native or container-aware backup and recovery tools.

6 Data backup and recovery for edge computing

Another trend in data backup and recovery is the consideration of data protection solutions for edge computing, which is a distributed computing paradigm that brings computation and data storage closer to the source of data generation or consumption. Edge computing can improve the performance, latency, and bandwidth of data-intensive applications, such as IoT, AR/VR, and AI. However, edge computing also introduces some data backup and recovery challenges, such as data fragmentation, synchronization, and availability, as well as edge device reliability, security, and scalability. Therefore, you need to implement data protection solutions that can cope with the heterogeneity, volatility, and complexity of edge computing environments, such as edge-to-edge or edge-to-cloud backup and recovery mechanisms.

7 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?