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Use Case Decision Framework
Apply a rigorous decision matrix to choose the right data platform for specific workloads, including SQL Analytics, Data Engineering, and ML, complete with industry-specific recommendations.
Use Case Decision Framework
Bottom Line: Platform selection is not a global decision — it's a per-workload decision. The best data organisations make deliberate choices about which platform handles which job. This framework gives you the tools to make those choices rigorously.
10.1 The Decision Tree
10.2 SQL Analytics & BI Serving
Decision Factors
Question 1: How many concurrent users are there?
< 50 users → Any platform works; choose by team skill
50-500 users → Snowflake (multi-cluster auto-scaling) or Redshift Serverless
500+ users → Snowflake (proven at massive concurrency) or Redshift Serverless
Question 2: Is cross-organisation data sharing required?
YES → Snowflake (clear winner — live sharing, Clean Rooms, Marketplace)
NO → Continue to next question
Question 3: What is the team's primary skill set?
SQL-first, minimal code → Snowflake
AWS-native, DevOps-oriented → Redshift
Python/notebook-oriented → Databricks SQL Warehouses
Question 4: Is this on AWS, and deeply integrated with other AWS services?
YES, and using Glue/Kinesis/Lake Formation extensively → Redshift
YES, but data platform is relatively standalone → Snowflake or Databricks
NO (multi-cloud or GCP/Azure primary) → Snowflake or Databricks
Question 5: Is operational simplicity the top priority?
YES → Snowflake (minimal DBA overhead)
NO, team has deep DB expertise → Redshift (more control, more tuning required)
Scenario-Based Recommendations
| Scenario | Recommended Platform | Why |
|---|---|---|
| Financial services, 200 analysts, strict data segregation | Snowflake | Row/column policies, mature RBAC, audit logs |
| Retail analytics, 500+ BI users on Tableau/PowerBI | Snowflake | Multi-cluster concurrency, BI tool optimisation |
| AWS-native company, Redshift already in use, cost-conscious | Redshift | Lower cost than Snowflake at scale, RI savings |
| E-commerce, daily reporting with seasonal spikes | Snowflake | Auto-scaling handles peak without pre-provisioning |
| Ad-hoc exploration on S3 data, low frequency | AWS Athena | No warehouse needed, $5/TB pay-per-query |
| Unified SQL + notebook analytics for data science teams | Databricks SQL WH | One platform for SQL and notebook workflows |
10.3 Data Engineering & ETL/ELT
Decision Factors
Question 1: Is the primary transformation paradigm SQL or code (Python/Scala)?
SQL-first ELT → Snowflake (Streams + Tasks + dbt) or Redshift
Python/Spark → Databricks or AWS EMR/Glue
Question 2: Is real-time streaming required?
YES, sub-second latency → AWS Kinesis + Lambda (or Flink) — Databricks Structured Streaming
YES, minute-level latency → Databricks Structured Streaming (best unified batch+stream)
NO, hourly/daily batch → Any platform
Question 3: What is the data scale?
< 100GB/day → Snowflake tasks, dbt on any platform, Glue
100GB-1TB/day → Databricks (better price/performance for large Spark jobs)
> 1TB/day → Databricks or EMR (Snowflake can do this but cost is high)
Question 4: Are there complex custom transformations (ML features, graph, NLP)?
YES → Databricks (Python UDFs, MLlib, custom libraries)
NO → Snowflake Streams/Tasks or AWS Glue
Question 5: Do you need CDC from operational databases?
YES → AWS DMS + Databricks MERGE (most mature) or Snowflake Streams + Tasks
ETL Tool Integration
| Tool | AWS | Snowflake | Databricks |
|---|---|---|---|
| dbt | ✅ Redshift + Athena adapters | ✅ Best-in-class integration | ✅ Databricks adapter |
| Fivetran | ✅ 300+ connectors → S3/Redshift | ✅ 300+ connectors → Snowflake | ✅ 300+ connectors → Databricks |
| Airbyte | ✅ Open source, self-hosted | ✅ Strong Snowflake support | ✅ Good support |
| Apache Airflow | ✅ MWAA (managed) | ✅ Snowflake operators | ✅ Databricks operators |
| Prefect / Dagster | ✅ | ✅ | ✅ |
| Kafka / MSK | ⚡ Native ecosystem | ✅ Kafka Connector | ⚡ Native Spark |
| Spark | ✅ EMR | ⚠️ Via Snowpark (limited) | ⚡ Native runtime |
| dbt + Orchestration | Airflow (MWAA) | dbt Cloud + Airflow | Databricks Workflows |
10.4 Machine Learning & AI
Decision Factors
Question 1: Are you primarily using pre-built LLMs or training custom models?
Pre-built LLMs → AWS Bedrock (most model choice) or Snowflake Cortex (SQL-native)
Custom training → Databricks (GPU clusters, MLflow, full Spark pipeline)
Question 2: Who are the primary users?
Data Scientists (Python, Jupyter) → Databricks (notebooks, MLflow, AutoML)
Data Analysts (SQL) → Snowflake Cortex (SQL-native LLM functions)
ML Engineers (MLOps focus) → Databricks (model registry, serving, monitoring)
Question 3: Is feature engineering complex (joins across large tables, time-series)?
YES → Databricks Feature Store (Spark-native, point-in-time joins)
NO → Snowflake or SageMaker Feature Store
Question 4: Is real-time model serving required?
YES, <100ms latency → AWS SageMaker Endpoints (most mature, auto-scaling)
YES, seconds → Databricks Model Serving
NO, batch inference → Any platform
Question 5: Do you need GPU clusters for deep learning?
YES → AWS SageMaker (most GPU instance types) or Databricks (A100/H100 clusters)
NO → Snowflake Snowpark ML is sufficient for classical ML
10.5 Master Decision Matrix
Score each dimension 1-5 based on your requirements, multiply by weight, compare totals:
| Dimension | Weight | AWS | Snowflake | Databricks |
|---|---|---|---|---|
| SQL Analytics Performance | 20% | 4 | 5 | 4 |
| Concurrent BI Users (>100) | 15% | 3 | 5 | 4 |
| Python/Spark Flexibility | 15% | 4 | 2 | 5 |
| ML/AI Capability | 15% | 5 | 3 | 5 |
| Operational Simplicity | 10% | 2 | 5 | 3 |
| Data Sharing | 10% | 2 | 5 | 4 |
| Cost at Scale (Batch) | 10% | 4 | 2 | 4 |
| Open Format / Vendor Lock-in | 5% | 4 | 2 | 5 |
| Streaming Native | 5% | 5 | 1 | 4 |
Weighted Score:
| AWS | Snowflake | Databricks | |
|---|---|---|---|
| SQL Analytics | 0.80 | 1.00 | 0.80 |
| Concurrency | 0.45 | 0.75 | 0.60 |
| Python/Spark | 0.60 | 0.30 | 0.75 |
| ML/AI | 0.75 | 0.45 | 0.75 |
| Ops Simplicity | 0.20 | 0.50 | 0.30 |
| Data Sharing | 0.20 | 0.50 | 0.40 |
| Cost (Batch) | 0.40 | 0.20 | 0.40 |
| Open Format | 0.20 | 0.10 | 0.25 |
| Streaming | 0.25 | 0.05 | 0.20 |
| TOTAL | 3.85 | 3.85 | 4.45 |
Note: This weighting reflects a data engineering + ML-heavy organisation. For a SQL/BI-heavy org, Snowflake would score highest. Adjust weights to match your context.
10.6 Industry-Specific Recommendations
Financial Services
Primary requirements: Security, compliance (SOC2, PCI, HIPAA), audit logs, data governance
Recommended: Snowflake (Business Critical edition) + Databricks (Premium)
- Snowflake: Data serving, regulatory reporting, RBAC/ABAC, masking policies
- Databricks: Risk model training, fraud detection ML, Alt-data processing
- AWS: Infrastructure backbone, DMS for CDC, Kinesis for market data streaming
- Key feature: Snowflake Tri-Secret Secure for key management compliance
Healthcare & Life Sciences
Primary requirements: HIPAA compliance, PHI protection, clinical trial data, genomics compute
Recommended: AWS (HIPAA-eligible services) + Databricks
- AWS: HIPAA-eligible S3, Redshift (BAA available), VPC isolation
- Databricks: Genomics pipelines (DRAGEN on EMR), clinical analytics
- Snowflake: Also HIPAA-eligible, great for clinical reporting
- Key: Always verify BAA availability for your specific use case
Retail & E-Commerce
Primary requirements: Seasonality handling, real-time inventory, 360 customer view, ML recommendations
Recommended: Snowflake + Databricks hybrid
- Snowflake: BI serving, finance reporting, data sharing with brand partners
- Databricks: ML recommendations engine, real-time streaming, customer 360
- Kinesis/MSK → Databricks → Delta Lake → Snowflake (serving)
Media & Advertising
Primary requirements: Data marketplace, audience segments, privacy (clean rooms), real-time bidding
Recommended: Snowflake (strong winner for this vertical)
- Snowflake Clean Rooms for privacy-preserving audience matching
- Snowflake Marketplace for data monetisation
- Databricks for ML model training (lookalike models, propensity scoring)
Manufacturing & IoT
Primary requirements: Time-series data, predictive maintenance, high-volume sensor data, streaming
Recommended: AWS (streaming) + Databricks (processing)
- Kinesis / MSK for real-time sensor ingestion
- AWS IoT Greengrass for edge processing
- Databricks: Time-series analytics, anomaly detection, predictive maintenance ML
- Timestream (AWS): Purpose-built time-series DB for operational metrics
- Snowflake: Business reporting and KPI dashboards
10.7 The "Rightful Role" Framework
Use this to assign each platform its natural role in a multi-platform architecture:
┌─────────────────────────────────────────────────────────────┐
│ PLATFORM RIGHTFUL ROLES │
│ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ INGEST & STREAM │ AWS Kinesis / MSK │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ STORE (LAKE) │ AWS S3 (Open, any engine) │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ TRANSFORM & ENRICH │ Databricks Delta Lake │ │
│ │ │ (Complex, Python/Spark) │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ TRAIN MODELS │ Databricks Mosaic AI │ │
│ │ │ (or AWS SageMaker) │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ SERVE & SHARE │ Snowflake │ │
│ │ │ (SQL, BI, data sharing) │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
This pattern — Kinesis/MSK → S3 → Databricks → Snowflake — is arguably the most common "best-of-breed" architecture in large enterprises today, combining the streaming depth of AWS, the processing power of Databricks, and the serving elegance of Snowflake.
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Contenido del Curso
1. Platform Overview & Philosophy2. Core Architecture Deep Dive3. Storage & Data Lake4. Compute & Processing5. Data Ingestion & Integration6. Governance, Security & Compliance7. AI & Machine Learning Capabilities8. Data Sharing & Collaboration9. Cost Structure & Optimization10. Use Case Decision Framework11. Migration Strategies12. Reference Architecture Patterns