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Cloud Analytics Architecture

7 posts · Aug 2023 – Dec 2025

What this trail covers

Hex has evolved its data analytics capabilities by detailing the architecture and benefits of cloud analytics. This includes explaining the separation of storage and compute layers, elastic scaling of compute resources on demand, and the role of the services layer in coordinating operations. The post emphasizes how this architecture enables faster workflows, scalability without infrastructure overhead, and enterprise adoption. It also guides users on choosing an approach based on existing data i

All Hex trails
  1. What is cloud analytics? Benefits, architecture & how to chooseThis post details the core architecture of cloud analytics, emphasizing the separation of storage and compute layers. It explains how elastic compute clusters scale on demand and how a services layer coordinates operations like authentication and query optimization. The post also provides guidance on choosing a cloud analytics approach, focusing on direct data warehouse connectivity, catering to diverse user skill sets (technical practitioners and business users), evaluating AI capabilities critically, and the importance of collaboration. It contrasts this with traditional on-premise setups and highlights the benefits of speed, scalability, and reduced infrastructure overhead.
  2. How to build a data warehouse architecture that scalesThis post details the architecture of a data warehouse, covering key components like staging, transformation, and presentation layers. It contrasts single-tier and multi-tier architectures, and cloud vs. on-prem hosting. The post also outlines essential steps for implementation, including requirement outlining, data modeling (star schema, OBT, data marts), ETL/ELT pipeline choices, and data modeling tools like dbt. It discusses choosing data warehouse platforms like Redshift, Snowflake, BigQuery, and Azure Synapse Analytics, emphasizing integration capabilities and the benefits of cloud-based solutions for scalability and reduced infrastructure management.
  3. A complete guide to cloud modern data architectureThis post details the core principles and implementation of modern data architecture, focusing on cloud-native approaches. It elaborates on batch and real-time data ingestion, data storage (data lakes, lakehouses, and storage zones), data catalogs, data transformation (SQL-based tools like dbt, Spark, Python, and stream processing engines), and security & governance. It outlines key implementation steps including establishing unified data storage, integrating real-time event pipelines (Kafka/Flink), enforcing federated governance, adopting cloud-native principles (separation of storage and compute), and incorporating AI for data quality and governance. The post also discusses bringing CI/CD to data products (DataOps) and the role of modern architecture in advanced analytics.
  4. Data platform architecture 2025: Scalable & collaborativeThis post details the essential layers of a modern data platform, including ingestion and storage, processing and transformation (emphasizing ELT and the separation of transformation workflows from metric logic), and analytics and consumption. It discusses data warehouse, data lake, and data lakehouse approaches, as well as data mesh and data fabric architectures. It also delves into batch vs. streaming processing, storage technologies (cloud data warehouses, data lakes, columnar formats), and cloud vs. hybrid deployments, providing a comprehensive overview of scalable data platform architecture.
  5. How to get the most out of your Snowflake dataThis post details how to maximize Snowflake investment by leveraging Snowpark for ML model training and faster Python code execution, implementing cost controls like resetting query timeouts and tightening warehouse auto-suspends, and utilizing Hex as an exploratory workbench. It highlights Hex's native integration with Snowflake, enabling interactive sharing, simplified Snowpark adoption, chained SQL workflows, and pushdown computation to Snowflake for handling large datasets. The post emphasizes the synergy between Snowflake's architecture and Hex's platform for efficient data analysis and ML development.
  7. Eutelsat OneWeb’s Journey with Snowflake and HexThis post details Eutelsat OneWeb's journey using Snowflake and Hex for data collaboration and processing. It highlights the challenges of handling 70 billion rows of data daily, the use of Snowflake as a centralized data warehouse with a data mesh architecture, and the secure processing of data within Snowflake using Snowpark and Hex, avoiding data export. The transition from isolated Jupyter notebooks to Hex for collaborative workflows is discussed, along with future plans to integrate Hex and Snowpark within their network using Snowpark Container Service for enhanced security.
  8. Hex + Google: Analytics and Data Science in the CloudThis post announces a partnership with Google Cloud and details new integrations with BigQuery and Serverless Spark. It highlights the ability to use BigQuery DataFrames for large dataset analysis with pandas-like syntax, pushing computations to the warehouse. It also introduces upcoming support for Serverless Spark for ETL, data science, and ML workloads within Hex. Additionally, Hex has expanded its database support to include Google Cloud SQL Server, PostgreSQL, and MySQL.