Lakehouse Architecture
Industrial companies generate large volumes of time-series, event-based, and contextual data from historians, SCADA systems, ERP platforms, and IoT devices. To support advanced analytics and AI workloads, this data must be structured, governed, and scalable.
Lakehouse architectures combine the flexibility of data lakes with the performance and governance of data warehouses, allowing organizations to unify operational, business, and contextual data into a single analytics platform. At MetaFactor, we design and implement lakehouse solutions that integrate historian and operational data with enterprise systems, enabling scalable analytics, AI development, secure governance, and real-time processing.
Cloud Lakehouse Platforms
Below are leading cloud platforms used to implement modern lakehouse architectures, combining scalable storage, high-performance processing, and enterprise governance to support advanced analytics and AI workloads while unifying operational and business data in secure, production-ready environments.
Databricks
Databricks is a cloud-based data and AI platform built around the lakehouse architecture. It combines scalable data engineering, analytics, and machine learning on top of open storage using Delta Lake. Databricks supports batch and streaming workloads through Apache Spark and provides enterprise governance features such as Unity Catalog, making it well suited for building secure, production-grade lakehouse and AI solutions.
Snowflake
Snowflake is a cloud-native data platform that supports a hybrid lakehouse approach by combining data warehousing performance with flexible access to data lake storage. It enables secure data sharing, scalable compute separation, and support for structured and semi-structured data. Snowflake is commonly used for enterprise analytics, governed data collaboration, and AI-ready data environments.
Amazon Redshift
Amazon Redshift is AWS’s cloud data warehousing platform that supports modern lakehouse architectures through Redshift Spectrum, which enables querying data directly in Amazon S3 data lakes using standard SQL. This integration allows organizations to combine warehouse performance with scalable cloud storage, supporting high-performance analytics, governed data access, and AI-ready data platforms.
Microsoft Fabric
Microsoft Fabric is Microsoft’s unified analytics platform that integrates lakehouse storage, data engineering, data science, and business intelligence into a single environment. Built on OneLake and powered by Spark and SQL engines, Fabric enables organizations to consolidate operational and enterprise data while supporting governed analytics and AI workloads at scale.
Google BigQuery
Google BigQuery is Google Cloud’s fully managed, serverless analytics platform that supports large-scale data processing and hybrid lakehouse architectures. It enables high-performance SQL analytics across structured and semi-structured data stored in cloud object storage, making it well suited for scalable reporting, machine learning, and AI-driven analytics environments.
Azure Data Lake Storage
Azure Data Lake Storage is Microsoft’s scalable cloud object storage service designed for big data analytics and lakehouse architectures. It provides high-throughput access to structured and unstructured data, integrates natively with Azure analytics services such as Microsoft Fabric and Synapse, and supports open table formats like Delta Lake. Azure Data Lake serves as the foundational storage layer for secure and scalable cloud data platforms.
How Can We Help?
Lakehouse architectures require more than selecting a platform. They demand secure data integration, scalable engineering patterns, and governance-ready foundations that support analytics and AI. MetaFactor helps organizations design, implement, and operationalize modern lakehouse environments that unify operational and enterprise data in production-ready cloud platforms.
Build Lakehouse Pipelines
We design and implement ingestion and transformation pipelines that move data from historians, SCADA systems, enterprise platforms, and IoT sources into scalable lakehouse environments. Using modern ETL and ELT patterns, we ensure your data is structured, reliable, and ready for analytics and AI workloads.
Lakehouse Data Foundations
We architect secure and scalable storage layers using cloud object storage and open table formats to enable governed, high-performance analytics. Our approach ensures ACID compliance, schema enforcement, and optimized access patterns for both batch and streaming workloads.
Architect Enterprise Lakehouse Solutions
We assess your operational and analytical requirements to design production-grade lakehouse architectures aligned with enterprise standards. From platform selection to governance, security, and AI readiness, we build solutions that scale with your organization.