Webinar: 'Are Your Cloud Analytics Platform Costs Swelling with Every Analysis?'
CelerData, Inc. will host a webinar addressing the challenges of increasing costs and complexity in cloud analytics platforms. It will introduce a new approach to achieve real-time analytics and AI utilization at lower costs, without relying on data processing or copying, thereby promoting corporate data utilization.
📋 Article Processing Timeline
- 📰 Published: April 10, 2026 at 18:00
- 🔍 Collected: April 10, 2026 at 09:01
- 🤖 AI Analyzed: April 20, 2026 at 07:43 (238h 42m after Collected)
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**■ The billing structure of cloud analytics platforms is hindering data utilization**
Data platforms leveraging cloud DWHs and lakehouses are being increasingly adopted and utilized by many companies. However, many companies face situations where "costs increase with every analysis" due to billing structures like query-based and usage-based fees. As data utilization progresses, costs swell, leading to challenges such as inability to freely execute queries and brakes on expanding user departments. What should be a foundation to accelerate decision-making is instead being constrained by cost limitations—an increasing number of companies are feeling challenged by this situation.
**■ What is the structure that causes costs to swell and prevents free analysis with every analysis?**
Why do costs increase and operations become more complex as data utilization advances? In many environments, pre-aggregation of data, creation of wide tables, and data copying are performed to compensate for limitations in query performance and concurrency. As a result, ETL and data pipelines increase, configurations become complex, and operational load also rises. Furthermore, to achieve near real-time analysis and AI utilization, more processing and data updates are required, further increasing costs. Many companies are facing this structural problem: "We have data, but we cannot use it freely due to cost and configuration constraints."
**■ How to build a platform for real-time analysis and AI utilization without relying on data processing or copying**
In this seminar, we will first organize the underlying structure of these challenges—"why costs keep increasing" and "why configurations become complex"—and then explain the concept of a simple analytics platform that does not depend on data processing or copying. We will introduce a new approach that enables real-time analysis and AI utilization while suppressing costs, by directly utilizing data on data lakes and achieving high-speed data joining and high concurrency. This content will provide attendees with criteria for how to improve their own analytics platforms, not just through performance improvement, but by simultaneously reviewing "cost, configuration, and operational load."
**■ Recommended for:**
・Those who are concerned about increasing costs of cloud analytics platforms
・Those considering the operation and review of Snowflake / BigQuery / Databricks, etc.
・Those who want to reduce the burden of data processing and wide table design
・Those considering platform design for real-time analysis and AI utilization
・Those who are struggling with the decision of extending the life of existing platforms or reviewing their configuration
**■ Organizer/Co-organizer**
CelerData, Inc.
**■ Cooperation**
Open Source Utilization Research Institute Co., Ltd.
Majisemi Co., Ltd.
**■ The billing structure of cloud analytics platforms is hindering data utilization**
Data platforms leveraging cloud DWHs and lakehouses are being increasingly adopted and utilized by many companies. However, many companies face situations where "costs increase with every analysis" due to billing structures like query-based and usage-based fees. As data utilization progresses, costs swell, leading to challenges such as inability to freely execute queries and brakes on expanding user departments. What should be a foundation to accelerate decision-making is instead being constrained by cost limitations—an increasing number of companies are feeling challenged by this situation.
**■ What is the structure that causes costs to swell and prevents free analysis with every analysis?**
Why do costs increase and operations become more complex as data utilization advances? In many environments, pre-aggregation of data, creation of wide tables, and data copying are performed to compensate for limitations in query performance and concurrency. As a result, ETL and data pipelines increase, configurations become complex, and operational load also rises. Furthermore, to achieve near real-time analysis and AI utilization, more processing and data updates are required, further increasing costs. Many companies are facing this structural problem: "We have data, but we cannot use it freely due to cost and configuration constraints."
**■ How to build a platform for real-time analysis and AI utilization without relying on data processing or copying**
In this seminar, we will first organize the underlying structure of these challenges—"why costs keep increasing" and "why configurations become complex"—and then explain the concept of a simple analytics platform that does not depend on data processing or copying. We will introduce a new approach that enables real-time analysis and AI utilization while suppressing costs, by directly utilizing data on data lakes and achieving high-speed data joining and high concurrency. This content will provide attendees with criteria for how to improve their own analytics platforms, not just through performance improvement, but by simultaneously reviewing "cost, configuration, and operational load."
**■ Recommended for:**
・Those who are concerned about increasing costs of cloud analytics platforms
・Those considering the operation and review of Snowflake / BigQuery / Databricks, etc.
・Those who want to reduce the burden of data processing and wide table design
・Those considering platform design for real-time analysis and AI utilization
・Those who are struggling with the decision of extending the life of existing platforms or reviewing their configuration
**■ Organizer/Co-organizer**
CelerData, Inc.
**■ Cooperation**
Open Source Utilization Research Institute Co., Ltd.
Majisemi Co., Ltd.