Snowflake Mastery 2026

The problem Snowflake solved, cloud data platform vs. legacy warehouses, separation of compute and storage.

Where Snowflake fits, Snowflake vs. Databricks vs. BigQuery vs. Redshift, market context.

Editions, credits, warehouses, storage pricing, consumption model, cost estimation.

Centralized storage, virtual warehouses, cloud services — the full journey of a query.

What micro-partitions are, pruning, clustering keys, columnar metadata.

ACID on cloud storage, MVCC, Time Travel internals, zero-copy cloning.

Trial signup, Snowsight, worksheets, object hierarchy, role system.

Database → Schema → Objects, fully qualified names, transient/temporary/permanent, Information Schema.

DDL, DML, SELECT, JOINs, subqueries, CTEs, QUALIFY, PIVOT, FLATTEN, result caching.

ROW_NUMBER, RANK, LAG, LEAD, SUM OVER, QUALIFY, window frames — the skill that separates intermediate from advanced.

VARIANT, FLATTEN, CLONE, SWAP, UNDROP, SAMPLE, GENERATOR, EXPLAIN, INFORMATION_SCHEMA.

What stages are, internal vs. external, CREATE STAGE, PUT/GET, choosing the right stage.

COPY INTO syntax, file formats, ON_ERROR, Purge, Force, validation, load history.

Snowpipe with auto-ingest, Snowpipe REST API, pipe objects, monitoring, streaming API.

COPY INTO LOCATION, formats, compression, single vs. multiple files, GET command, data sharing as alternative.

Fact tables, dimension tables, star schema, snowflake schema, SCD Type 1/2/3, surrogate keys.

One Big Table, Data Vault 2.0, activity schema, choosing the right model, dbt integration.

Micro-partition structure, columnar storage, metadata per partition, pruning, load order, pruning ratio.

What clustering keys are, automatic clustering, when to use them, choosing the right key, clustering depth, SYSTEM$CLUSTERING_INFORMATION.

Query Profile in Snowsight, operators, spilling, execution time breakdown, EXPLAIN, common anti-patterns.

VARIANT, OBJECT, ARRAY types, loading JSON, path notation, FLATTEN, type casting.

Deeply nested structures, schema-on-read views, missing keys, Avro/ORC/Parquet loading, XML, clustering on VARIANT.

What streams are, METADATA columns, standard vs. append-only streams, consuming streams, staleness.

Task scheduling, task trees, Stream + Task pattern, serverless tasks, error handling.

DECLARE/BEGIN/END, variables, IF/LOOP, cursors, exception handling, dynamic SQL, stored procedures.

DATA_RETENTION_TIME_IN_DAYS, AT/BEFORE/OFFSET clauses, cloning past states, auditing, cost considerations.

7-day non-configurable retention, transient vs. permanent tables, when Fail-Safe applies.

CLONE syntax, instant copies, storage implications, use cases, clone + streams/pipes behavior.

Warehouse sizes XS–6XL, auto-suspend, auto-resume, MCW, scaling policy, concurrency slots, resource monitors, QAS.

Top cost drivers, separate warehouses by workload, right-sizing, credit tracking, storage optimization, resource monitors.

System roles, custom roles, GRANT/REVOKE, future grants, functional role hierarchy, access history.

Dynamic data masking, row access policies, object tagging, tag-based masking, classification, data lineage.

Network policies, private connectivity, authentication methods, SCIM, encryption at rest/transit, Tri-Secret Secure.

Share object, reader accounts, how shares work internally, cross-cloud sharing, revoking shares.

Finding, subscribing, publishing data products, listing types, monetization, data freshness.

Privacy-preserving collaboration, advertising attribution, healthcare research, differential privacy, provider vs. consumer roles.

Snowpark SDK, DataFrame API, pushdown, serverless vs. warehouse, when to use Snowpark vs. SQL.

Session creation, DataFrame operations, column expressions, UDFs, vectorized UDFs, stored procedures, Pandas integration.

Snowpark ML library, model registry, training with scikit-learn API, feature engineering, model scoring, MLOps patterns.

Cortex ML functions (FORECAST, ANOMALY_DETECTION, CLASSIFICATION, etc.), Cortex LLM functions (COMPLETE, SUMMARIZE, SENTIMENT, EMBED_TEXT).

Semantic search, natural language analytics, semantic models, Cortex Search vs. Cortex Analyst, building RAG.

Vector data types, embedding generation, vector similarity search, document chunking, full RAG pipeline, Snowflake Notebooks, Streamlit chat UI.

Target lag, DAG dependencies, incremental vs. full refresh, medallion architecture, monitoring.

Apache Iceberg open format, Snowflake as catalog, internally vs. externally managed tables, querying, writing, time travel, cost model.

Snowflake Notebooks for exploratory analysis and ML prototyping. Streamlit in Snowflake (SiS) for building interactive data applications and AI-powered UIs.

Combining Dynamic Tables, Iceberg, Notebooks, and Streamlit for end-to-end data applications.

Version controlling Snowflake objects, Schemachange, dbt, Terraform, Flyway, blue-green deployment with CLONE+SWAP, testing strategies.

ACCOUNT_USAGE views, Snowsight dashboards, CREATE ALERT, notification integrations, common troubleshooting patterns.

SnowPro Core, Advanced Data Engineer, Advanced Architect, Advanced Administrator — the full certification path with costs, study time, and what each tests.

Top interview questions, system design frameworks, hands-on challenges, behavioral questions, portfolio ideas.

What AI agents are, agentic vs. non-agentic AI, the agent landscape in 2026, why Snowflake is uniquely positioned for enterprise AI agents.

Creating and configuring Cortex Agents, tool-calling, multi-step reasoning, Cortex Search + Cortex Analyst as tools, API patterns, production deployment.

Snowflake Intelligence as the orchestration layer, connecting agents to data, governance guardrails, Cortex Guard, grounding in enterprise data.

Running custom containers, ML models, and APIs on Snowflake compute, GPU support, when to use Container Services vs. Cortex functions, deployment patterns.

Intelligent document processing, extracting structured data from unstructured documents, invoices, contracts, healthcare forms, custom LLM models.

What Openflow is, connectors, real-time and batch ingestion, CDC, Kafka integration, comparing Openflow to Fivetran/Matillion/Airbyte.

Using Openflow to feed Iceberg tables, open format strategies, Iceberg as your lakehouse foundation, cross-platform interoperability.

Building and distributing applications on Snowflake, Native Apps Framework, listing on Snowflake Marketplace, monetizing data applications, governance.

Deep dive into SiS: components, session state, charting, st.chat, connecting to Cortex functions, building a customer support AI app.

Deep dive into Notebooks: kernel selection, cell types, SQL + Python mixing, ML experiment tracking, feature engineering pipelines, model training.

Reference architectures for RAG apps, agent apps, document processing apps, real-time analytics apps — combining all 2026 features.

Three capstone projects: (1) Customer Support RAG Agent, (2) Document Processing Pipeline, (3) Real-time Analytics Dashboard with Cortex AI — full implementations.