You have a large amount of data, but it is not effectively connected to decision-making or business results.
You want to implement real-time analytics or advanced AI utilization, but your infrastructure and operations cannot keep up.
Your initiatives stop at PoC and have not been fully utilized in production operations.
Multiple platforms and tools have been introduced for different purposes, making the overall picture difficult to grasp.
Operational load and costs increase every time you process or analyze data.
It is difficult to handle streaming processing or advanced analytics with your existing infrastructure.
Data analytics, BI, machine learning, and AI development are siloed.
Starting new analytics or AI initiatives requires significant time and coordination costs.
You want to start small, but you are concerned about future scalability.
（01）
Combines the flexibility of a data lake with the reliability of a data warehouse.
Structured, semi-structured, and unstructured data can all be handled on a single platform, enabling seamless processes from analytics to AI utilization.
（02）
Equipped with a distributed processing engine capable of handling petabyte-scale data.
Processes both batch and streaming workloads with high performance to accelerate business decision-making.
（03）
Improves the efficiency of building and operating highly reliable data pipelines.
Standardizes data quality management and schema management to establish a stable data utilization platform.
（04）
Supports intuitive SQL-based analysis as well as multiple programming languages including Python, R, and Scala.
Easily integrates with BI tools, enabling use by a wide range of users—from business teams to data scientists.
（05）
Integrates feature engineering, experiment management, model registry, and production deployment.
Prevents AI projects from becoming dependent on specific individuals and enables continuous improvement and scalability.
（06）
Supports AWS, Azure, and Google Cloud.
Build analytics platforms that leverage your existing environment while maintaining flexibility for future expansion.
01
We provide comprehensive support from defining the objectives of Databricks implementation to architecture design and environment setup with implementation in mind.
Starting from business requirements such as DX promotion and advanced analytics, we clarify the functions, performance, and scalability required for the data platform.
We define the scope of target data, create a roadmap, and organize a feasible schedule and estimated cost to establish a design policy that smoothly connects to the implementation phase.
We build secure infrastructure on AWS, Azure, or GCP, including network design (VPC/VNet), IAM design, and audit log configuration.
Architectures are designed with availability, scalability, and governance in mind, ensuring readiness for future expansion.
02
We integrate distributed data assets and build a data platform designed for analytics and AI utilization.
We design and implement data integration processes from core systems and external services.
This includes automation of ETL/ELT processes, data quality management, update cycle design, and building a multi-layered data model based on the Medallion Architecture.
Using tools such as Unity Catalog, we implement access control, data classification, and audit log management.
Permission control and masking are designed based on personal data protection and internal compliance requirements, establishing governance suitable for enterprise use.
03
We establish development and analytics environments that enable practical data utilization.
We design workspaces based on departments and use cases, cluster configurations, and resource management policies.
Execution environments are optimized for both performance and cost efficiency, enabling continuous improvement.
We design and implement model version management, deployment workflows, accuracy monitoring, and retraining environments.
By building operational frameworks, we enable AI utilization that goes beyond PoC and supports real-world operations.