Multi-Cloud, Cost Optimization, Talent Shortages for Kubernetes, Big Models for AI, Open Table Formats for Data Lakes and Simplified Data Access and Sharing Top the List

Alluxio’s Founder and CEO Haoyuan (H.Y.) Li forecasts major developments in cloud, data and analytics, AI and storage in 2023. Data strategies will continue to require solutions that enable enterprises to scale complex analytics workloads across hybrid and multi-cloud environments. Haoyuan Li outlines the following major trends that guide his predictions:

Trend 1: Multi cloud adoption is accelerating as organizations’ data strategies evolve

As more organizations evolve their data strategies in 2023, multi-cloud data infrastructure adoption is accelerating and will become the new norm. Organizations are expected to embrace this trend and ensure their cloud applications are portable regardless of cloud provider. More organizations will transform cloud computing into an undifferentiated commodity and ease application burden. They aim to realize flexibility, security, and agility while simplifying their operations.

Trend 2: Cloud adoption becomes heavily influenced by cost optimization

Cloud adoption is being influenced by a greater focus on cost optimization in 2023. Even though the public cloud has catalyzed the growth of countless companies, the global economic uncertainties will drive large organizations with data-intensive workloads to recalibrate their cloud strategies with a higher emphasis on cost optimization, such as reducing egress costs. The focus will be on the ROI and TCO of their infrastructure, either in the cloud, on-premises, or both.

Trend 3: More large-scale analytics and AI workloads will be containerized, but the talent pool is a bottleneck

In the cloud-native era, Kubernetes has become the de facto standard, with a variety of commercial platforms available on the market. Organizations are increasingly deploying large-scale analytics and AI workloads in containerized environments. While containers provide many benefits, the transition to containers is very complex. As a result, in 2023 the main bottleneck to container adoption will be the shortage of talent with the necessary skill set for tools like Kubernetes.

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Trend 4: Big models for AI are driving innovations in specialized infrastructure and solutions

Over the past few years, AI and deep learning have become mainstream and reached the same maturity level as data analytics. Big models, from OpenAI’s DALL-E 2 image generation model to Google’s LaMDA conversation agent, are expected to dominate the landscape in 2023. Billions of files will be used to train big models for longer periods of time, requiring more specialized infrastructure and solutions. Next-generation AI infrastructure will be developed to handle the scale.

Trend 5: From centralized Hive catalog to open table formats in data lakes

With data lakes becoming the primary destination for a growing volume and variety of data, having a table format for data stored in a data lake is a no-brainer. More organizations now have realized that Hive catalogs have become the central bottleneck. In the cloud-native era, decentralized open data table formats are popular, especially in large-scale data platforms. In 2023, we can expect to see more enterprise data being stored in open table formats as Apache Iceberg, Hudi and Delta Lake are rapidly adopted.

Trend 6: Demand for simplified data access and data sharing is on the rise

Data has become increasingly distributed as the amount of data grows. In 2023, organizations will have an ever-increasing need to manage their scattered data wherever it exists. Furthermore, data sharing across organizations and platforms will become more critical. It will be necessary for organizations to develop and implement a data strategy for managing and sharing distributed data across regions, organizations, clouds and platforms.

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