Loading…
Data Warehousing
4 posts about Data Warehousing. Every summary links to the original.
The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents
The post presents data-agent-mnist, an open harness for measuring analytical agents on correctness, cost, and speed against a warehouse-shaped workload rather than a single text-to-SQL query. The authors extracted 201 production analytics questions from DWAINE, curated and anonymized them, reconstructed a deterministic synthetic warehouse with 18 tables and 865 columns, and evaluated 28 proprietary and open-weight models. The benchmark preserves schema discovery, multi-turn querying, planning, and multi-hop joins across curated marts and a dimensional CRM layer, with provider-diverse ground truth and judging described as part of the workflow. On the authors’ warehouse, Claude Fable 5.1 ranked first for correctness at 76.6%, while the full run cost $1 with DeepSeek V4 Flash versus $52 with Fable 5.1, an 11-percentage-point correctness difference. The harness is intended to identify the best production fit for a specific warehouse, not a universal winner.
Practical Data Warehouse Design and Architecture Guide
The guide presents data warehouse design as a business-aligned process for building, modernizing, or scaling analytics infrastructure, rather than merely storing data. It recommends defining analytics use cases and stakeholder needs first, then organizing a three-tier architecture of source, storage, and semantic output layers; cloud designs can decouple compute and storage and use open formats. A Bronze–Silver–Gold medallion flow preserves raw lineage, applies cleansing and deduplication, and produces consumption-ready dimensional models, while retention and archival policies control sprawl. For modeling, it favors star schemas for user-facing BI, uses snowflake normalization when redundancy is material, and stresses explicit fact-table granularity, domain-owned data marts, and workload-specific refresh cadences. Governance and operations include Unity Catalog, access controls, masking, lineage, multi-region deployment, disaster recovery, and CI/CD, followed by phased rollout through high-value domains.
Databricks StaffBI Serving Pointers; Maximizing for Performance and TCO
BI dashboards can become slow and expensive when teams respond to latency with separate aggregate tables, refresh pipelines, extracts, and tool-specific semantic layers. The post presents Databricks’ BI serving stack from physical storage through Unity Catalog’s governed semantic layer, recommending Gold-layer star schemas, managed tables, liquid clustering, and Predictive Optimization to reduce scanned data and improve query plans. Metric Views centralize KPI definitions and semantic metadata for dashboards, Genie, SQL notebooks, third-party BI tools, and AI agents, while materialization automatically maintains incremental pre-aggregations and routes queries transparently. Additional TCO guidance covers serverless SQL warehouse autoscaling, DBSQL disk and query-result caching, direct lakehouse connections, and system-table monitoring. The stated outcome is compounded lower latency and compute cost, including an observed average 22% performance improvement from Predictive Optimization and sub-second performance from materialized metrics.
Chris KoesterTop Data Warehouse Tools For Modern Data Analytics
Data warehouse tools are presented as a strategic choice for analytics and ML teams facing fragmented estates of warehouses, lakes, and standalone ML systems. The guide proposes evaluating platforms across query performance, scalability, data integration, BI connectivity, total cost of ownership, and governance and security, with attention to MPP, columnar storage, and decoupled compute and storage. It contrasts traditional warehouses, data lakes, and lakehouses: warehouses favor structured SQL analytics, lakes offer native-format flexibility but weaker quality and performance guarantees, and lakehouses combine these capabilities using open formats such as Delta Lake and Apache Iceberg. The stated conclusion is that a lakehouse can provide a single governed foundation for SQL, BI, streaming, ML, and AI workloads, while teams should select tools according to workload, scale, budget, and future AI needs.
Databricks Staff
