Google Data Warehouse Conversion Tool
AI-powered automated SQL translation and schema migration for petabyte-scale modernization.
Automated cloud data warehouse and ELT for instant business intelligence without the infrastructure overhead.
Panoply, now a core part of the SQream ecosystem, represents the evolution of the managed data warehouse for the 2026 market. It operates as a serverless, end-to-end data management platform that collapses the traditional ELT (Extract, Load, Transform) stack into a single, automated environment. Technically, it leverages a high-performance metadata-driven architecture that automatically discovers schemas and flattens nested JSON objects into relational tables, making semi-structured data immediately queryable via standard SQL. By abstracting the complexities of cloud infrastructure management—including vacuuming, indexing, and scaling—Panoply allows data analysts to focus on derivation rather than ingestion. In 2026, its positioning is centered on 'Instant Data Availability,' utilizing AI-driven query optimization and seamless integration with the SQream acceleration engine for massive datasets. It is specifically architected for SMBs and mid-market enterprises that require an enterprise-grade data stack (similar to Snowflake or BigQuery) but lack a dedicated 24/7 data engineering team. The platform supports over 100 native connectors and provides a unified interface for data storage, transformation, and BI tool connectivity, ensuring a single source of truth with minimal latency.
Uses machine learning to infer schemas from semi-structured data and automatically updates tables when source schemas change.
AI-powered automated SQL translation and schema migration for petabyte-scale modernization.
Petabyte-scale data warehouse with integrated GenAI and machine learning at the core.
Serverless analytics at the speed of DuckDB, scaled for the cloud.
Automated, zero-maintenance data movement for the modern AI data stack.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Recursively parses nested JSON objects into flattened relational tables.
Leverages the SQream DB engine for massive parallel processing (MPP) of complex queries.
Persistent SQL views that allow for non-destructive data modeling within the warehouse.
Data ingestion logic that only fetches new or updated records since the last sync.
Pre-computed result sets stored for high-speed retrieval by BI dashboards.
Visual mapping of data flow from source ingestion to final destination tables.
Siloed data in Shopify, Amazon, and Facebook Ads makes ROAS calculation difficult.
Registry Updated:2/7/2026
Pipe the unified table into PowerBI for a real-time ROAS dashboard.
App logs in MongoDB need to be joined with CRM data in Salesforce for churn prediction.
Manual aggregation of bank transaction CSVs and ERP data is error-prone.