Big Data, Data Warehouse, Data Lakes, Big Data Lake, LakeHouse, Snowflake - Explain in simple words - Page 3 of 4

How Snowflake DW is used? it is replacement for?

Snowflake is not a direct replacement for a single tool — it combines and replaces the functions of several traditional systems used in data warehousing and analytics. Here’s a breakdown:

❄️ Snowflake Can Replace or Consolidate:

Traditional Tool / Tech	Snowflake Can Replace or Act As
On-prem Data Warehouse (like Teradata, Netezza, Oracle Exadata)	✅ Fully replaces with cloud-native data warehouse
BigQuery, Redshift, Synapse	✅ Competes directly (alternative cloud data warehouses)
Hadoop/HDFS (for analytics)	✅ Replaces for analytical workloads; simpler architecture
ETL Staging Layers (Staging DBs)	✅ Reduces need for external staging with features like Streams, Tasks, Snowpipe
OLAP Cubes / OLAP Engines (e.g., SSAS)	✅ Replaces for most BI analytical workloads with fast SQL + MPP
Data Lake (in some cases)	✅ Works as a hybrid (data warehouse + lake) via Snowflake’s Native Storage & External Tables on S3
Data Mart	✅ Can host multiple virtual data marts using zero-copy cloning & multi-schema
Data Sharing tools (APIs, File Transfers)	✅ Replaces with Secure Data Sharing (instant, governed sharing)

🔁 What Snowflake Does Not Replace (Directly):

Tool/Tech	Reason
OLTP Databases (e.g., MySQL, PostgreSQL)	❌ Snowflake is not optimized for high-speed transactional apps
Airflow, dbt, or ETL Orchestration Tools	❌ These still manage pipeline logic — Snowflake provides features like Tasks, but not a full replacement
BI Tools (Power BI, Tableau)	❌ Snowflake is the backend; still needs a visualization tool
Kafka / Event Streaming Platforms	❌ Not a message broker, though it can ingest from Kafka
Data Lake for Raw Files (e.g., Data Science Feature Store)	❌ Snowflake works best for structured/semi-structured data; not built for image/video/file-heavy storage

💡 Summary

Snowflake = Cloud-native Data Platform
It can replace your data warehouse, simplify your data lake, and unify analytics, but it’s not a replacement for transactional DBs, orchestration, or visualization tools.

Yes, Snowflake integrates very well with both Azure and Databricks — they’re commonly used together in modern data platforms.

✅ 1. Snowflake + Azure Integration

Snowflake is natively available on Azure. It runs as a fully managed service in your Azure region and integrates with most Azure services:

Azure Service	Integration with Snowflake
Azure Blob Storage / ADLS Gen2	✅ External Tables, Stages, Snowpipe
Azure Data Factory (ADF)	✅ Can load/unload data to/from Snowflake
Azure Functions / Logic Apps	✅ Trigger Snowflake Tasks or load events
Azure Active Directory	✅ SSO authentication for Snowflake users
Azure Key Vault	✅ Secure external secrets for Snowflake access
Azure Synapse Pipelines	✅ Connects to Snowflake via ODBC/JDBC
Power BI (Azure-native)	✅ Direct Query or Import via Snowflake connector

✅ 2. Snowflake + Databricks Integration

While both Snowflake and Databricks are powerful in their own right, they complement each other in these ways:

Use Case	Integration
Reading/Writing Data	✅ Databricks can read from and write to Snowflake using the Snowflake Spark Connector
Data Science / ML	✅ Databricks for ML + Feature Engineering, Snowflake for storage & serving
Data Sharing	✅ Share curated Snowflake data with Databricks users (or vice versa)
Orchestration	✅ Use Databricks Notebooks to trigger Snowflake queries or tasks
Delta Lake + Snowflake	✅ Use external tables or COPY INTO from cloud storage to bridge Delta and Snowflake

📌 Spark connector config in Databricks:

sfOptions = {
  "sfURL": "<account>.snowflakecomputing.com",
  "sfUser": "<username>",
  "sfPassword": "<password>",
  "sfDatabase": "<database>",
  "sfSchema": "<schema>",
  "sfWarehouse": "<warehouse>"
}

df.write \
  .format("snowflake") \
  .options(**sfOptions) \
  .option("dbtable", "my_table") \
  .mode("overwrite") \
  .save()

🔄 Typical Architecture: Snowflake + Azure + Databricks

+--------------------+     +------------------+     +-------------------+
| Azure Data Sources | --> | Databricks (Spark)| --> | Snowflake (Storage, BI)|
+--------------------+     +------------------+     +-------------------+
                                  ↑                            ↓
                       ML Models / Feature Store       Power BI / ADF

✅ Summary

Question	Answer
Can Snowflake run on Azure?	✅ Yes, it’s cloud-native on Azure
Can Databricks connect to Snowflake?	✅ Yes, using native connectors
Can they all work together?	✅ Perfectly. Used together in real-world architectures

Great question. Let’s break it down step by step:

🏢 What Is an On-Prem Data Warehouse?

An on-premises (on-prem) data warehouse is a centralized system for storing and analyzing structured data, physically hosted in your organization’s own data center (not in the cloud).

These systems are used to:

Consolidate data from various sources (transactional DBs, files, logs)
Run reporting, BI, and analytics
Support decision-making across departments

🧱 Common On-Prem Data Warehouse Platforms

Platform	Description
Teradata	Enterprise-grade data warehouse used for large-scale analytics, often in banking, retail, telecom
Netezza (IBM PDA)	Appliance-based warehouse focused on performance and simplicity
Oracle Exadata	High-performance Oracle DB system optimized for DW/OLAP
SQL Server Data Warehouse (SSAS)	Microsoft’s DW & analytics layer
SAP BW (Business Warehouse)	SAP’s data warehousing solution tightly integrated with SAP ERP

🏦 What Banking Data Is Stored in a Data Warehouse?

Banks collect massive amounts of structured data from daily operations. Data warehouses store:

Domain	Example Data
Core Banking	Account balances, transactions, loan data
Credit Risk	Credit score, risk assessments, approvals
Compliance & Audit	KYC data, AML flags, transaction history
Customer Analytics	Demographics, behavior, churn predictions
Treasury & Markets	Forex transactions, trading positions
Branch/ATM Data	Usage metrics, cash flow, downtime
Finance/Accounting	GL data, reconciliations, cost reporting
Fraud Detection	Patterns, alerts, anomaly flags

These are periodically ETL’d (Extracted, Transformed, Loaded) into the warehouse for historical analysis.

🛠️ Tools & Technologies Used With On-Prem DWs

1. Query Languages

Tool	Language Used
Teradata	Teradata SQL (T-SQL like)
Oracle Exadata	PL/SQL
Netezza	Netezza SQL
SQL Server	T-SQL
SAP BW	ABAP + SQL for backend, SAP HANA SQL if upgraded

2. ETL / Data Integration Tools

Tool	Description
Informatica	Widely used for enterprise ETL
IBM DataStage	Often used with Netezza
Talend	Open-source ETL platform
SSIS (SQL Server Integration Services)	ETL for Microsoft ecosystems
SAP Data Services	For SAP BW and ERP pipelines
Pentaho / ODI	Used in some legacy environments

3. BI & Reporting Tools

Tool	Description
Tableau / Power BI	Modern dashboards and analytics
SAP BO / Crystal Reports	SAP-specific reports
MicroStrategy	Used widely in banking/finance
QlikView / QlikSense	Interactive analytics
Cognos (IBM)	Often paired with IBM DWs

🧠 Example Use Case in Banking

Credit Card Division – Fraud Detection DW Use Case:

Raw data from POS, online, ATM transactions go to OLTP systems.
ETL pipelines clean, join, and load this into Teradata DW nightly.
Analysts run reports to find patterns.
BI dashboards flag anomalies.
Models (SAS/Python) may run externally and push results back to the DW.

🧾 Summary Table

Area	Example
Data Warehouse	Teradata, Oracle Exadata, Netezza
Data Stored	Core banking, credit, compliance, fraud, customer data
Tools	Informatica, DataStage, Power BI, Tableau
Query Languages	SQL, PL/SQL, T-SQL, Netezza SQL
Role in Banking	Reporting, BI, regulatory compliance, risk analysis

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