Snowflake Data Engineering with DBT Training Online | Visualpath

Visualpath offers Snowflake Data Engineering with DBT Training Online to help you master modern data engineering workflows and cloud data pipelines. Our Snowflake Data Engineering with DBT Training provides real-time projects, expert guidance, and hands-on learning. Enroll today with Visualpath to accelerate your career in data engineering. Call [phone] to book your free demo.
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1. STEP-BY-STEP GUIDE TO SNOWFLAKE DATA ENGINEERING

2. Agenda What is Snowflake and why it matters Core architecture and components Data modeling & warehousing patterns Ingestion, ETL/ELT and pipelines Performance, security, and best practices

3. What is Snowflake? Cloud-native data platform for analytics and sharing Separates compute and storage for elasticity Multi-cluster, shared-data architecture Supports structured, semi-structured (JSON/Parquet) data Built-in features: time travel, cloning, data sharing

4. Snowflake Architecture (core components) Storage layer: centralized, compressed, columnar storage Compute layer: virtual warehouses (elastic clusters) Services layer: metadata, authentication, query optimization Zero-copy cloning and Time Travel for data versioning Multi-cloud availability (AWS, Azure, GCP)

5. Data Modeling & Warehousing Patterns Star and snowflake schema basics for analytics Use dimensional models for BI workloads Denormalization vs normalization trade-offs Leverage micro-partitioning and clustering keys Handling semi-structured data with VARIANT type

6. Ingestion & ETL / ELT Strategies Batch ingestion: Snowpipe , COPY INTO, external stages Streaming ingestion approaches and micro-batches ELT pattern: transform inside Snowflake using SQL Orchestrators: Airflow, dbt , Matillion , or native tasks Data validation and idempotent loads

7. Performance & Cost Optimization Size warehouses to workload and use auto-suspend Use result caching, query profiling, and pruning Apply clustering keys for selective queries Materialized views and search optimization service Monitor credit usage and right-size compute

8. Security, Governance & Compliance Role-based access control (RBAC) and least privilege Data encryption at rest and in transit (managed by Snowflake) Object-level privileges, masking policies, and row access Data lineage and catalog integration (e.g., Data Catalogs) Audit logging, compliance (SOC, ISO, HIPAA considerations)

9. Conclusion Start with small, well-defined data domains Use ELT and push transformations into Snowflake Automate testing, monitoring, and alerting Plan for disaster recovery and data retention policies

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