intermediate⏱70 minLesson 11 of 19

Domain 3.2 — Automated Data Ingestion

Learn to automate data ingestion with Snowpipe, Snowpipe Streaming, Streams, Tasks, and Dynamic Tables. Build continuous and event-driven data pipelines entirely within Snowflake.

Domain 3.2 — Automated Data Ingestion

Exam Weight

Domain 3.0 accounts for ~18% of the exam. Automated ingestion patterns are frequently tested.

ℹ️Note

This lesson maps to Exam Objective 3.2: Perform automated data ingestion, including Snowpipe, Snowpipe Streaming, Streams, Tasks, and Dynamic Tables.

Ingestion Patterns Overview

Tool	Trigger	Latency	Best For
COPY INTO	Manual / scheduled	Minutes to hours	Batch loads
Snowpipe	Cloud storage event	Near-real-time (seconds–minutes)	Continuous file-based ingestion
Snowpipe Streaming	SDK push	Sub-second	High-frequency row-level streaming
Streams + Tasks	Time or stream condition	Configurable	CDC-based pipeline orchestration
Dynamic Tables	Automatic (lag-based)	Configurable	Declarative incremental refresh

Snowpipe

Snowpipe is Snowflake's serverless, continuous ingestion service — it automatically loads files as they arrive in a stage.

How Snowpipe Works

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Creating a Pipe

sql

-- Create a pipe with auto-ingest (cloud event trigger)
CREATE PIPE orders_pipe
    AUTO_INGEST = TRUE
    COMMENT = 'Automatically ingest order files from S3'
AS
COPY INTO raw.orders (order_id, customer_id, amount, order_ts)
FROM (
    SELECT $1, $2, $3::DECIMAL(10,2), $4::TIMESTAMP
    FROM @s3_orders_stage/orders/
)
FILE_FORMAT = (FORMAT_NAME = csv_format);

-- View the pipe's SQS ARN (for S3 event notification setup)
SHOW PIPES LIKE 'orders_pipe';

Snowpipe with REST API

For cases where cloud events are not available, you can trigger Snowpipe via REST API:

python

import requests
from snowflake.ingest import SimpleIngestManager

# Initialize the ingest manager
manager = SimpleIngestManager(
    account='myaccount.us-east-1',
    host='myaccount.us-east-1.snowflakecomputing.com',
    user='my_user',
    pipe='my_database.my_schema.orders_pipe',
    private_key=private_key_bytes
)

# Notify Snowpipe about new files
response = manager.ingest_files([
    {'path': 'orders/orders_2025_01_15.csv'}
])

Snowpipe Properties and Billing

Property	Value
Warehouse needed?	No — serverless
Billing	Per-file compute credits (serverless rate)
Latency	Typically < 1 minute
Deduplication	14-day window (files not re-processed)
Error handling	Files with errors are skipped; check COPY_HISTORY

sql

-- Monitor Snowpipe load status
SELECT *
FROM TABLE(INFORMATION_SCHEMA.COPY_HISTORY(
    TABLE_NAME => 'ORDERS',
    START_TIME => DATEADD('hour', -24, CURRENT_TIMESTAMP)
))
ORDER BY LAST_LOAD_TIME DESC;

-- Snowpipe credit usage
SELECT *
FROM SNOWFLAKE.ACCOUNT_USAGE.PIPE_USAGE_HISTORY
WHERE PIPE_NAME = 'ORDERS_PIPE'
AND START_TIME > DATEADD('day', -7, CURRENT_TIMESTAMP);

-- Refresh Snowpipe (force re-scan of stage)
ALTER PIPE orders_pipe REFRESH;

Snowpipe Streaming

Snowpipe Streaming enables row-level, sub-second ingestion directly into Snowflake tables via the Snowflake Ingest SDK — without staging files first:

Application / Kafka / CDC tool
         │
         ▼ (Snowflake Ingest SDK or Kafka Connector)
Snowflake Streaming Channel
         │
         ▼
Snowflake Table (latency: milliseconds to seconds)

Key Differences: Snowpipe vs Snowpipe Streaming

Aspect	Snowpipe	Snowpipe Streaming
Data unit	Files	Rows
Latency	Seconds to minutes	Milliseconds to seconds
Staging required	Yes (files in stage)	No
SDK	Ingest SDK (file paths)	Ingest SDK (row-level)
Use case	File-based continuous load	Real-time event streaming
Kafka integration	Snowflake Kafka Connector v1	Snowflake Kafka Connector v2+

java

// Java example — Snowpipe Streaming
SnowflakeStreamingIngestClient client = SnowflakeStreamingIngestClientFactory
    .builder("myClient")
    .setProperties(props)
    .build();

OpenChannelRequest request = OpenChannelRequest.builder("MY_CHANNEL")
    .setDBName("MY_DB")
    .setSchemaName("PUBLIC")
    .setTableName("EVENTS")
    .setOnErrorOption(OpenChannelRequest.OnErrorOption.CONTINUE)
    .build();

SnowflakeStreamingIngestChannel channel = client.openChannel(request);

// Insert rows
Map<String, Object> row = new HashMap<>();
row.put("event_id", "12345");
row.put("event_type", "click");
row.put("event_ts", System.currentTimeMillis());
channel.insertRow(row, "offset_12345");

Streams — Change Data Capture

A Stream is a Snowflake object that captures DML changes (INSERT, UPDATE, DELETE) on a source table or view. It acts as a CDC (Change Data Capture) log.

Stream Types

Type	Captures	Source
Standard	INSERT, UPDATE, DELETE	Tables, directory tables
Append-only	INSERT only (no updates/deletes)	Tables, views
Insert-only	INSERT only	External tables

sql

-- Create a standard stream
CREATE STREAM orders_stream ON TABLE raw.orders;

-- Create an append-only stream (lighter weight for insert-heavy tables)
CREATE STREAM events_stream ON TABLE raw.events
    APPEND_ONLY = TRUE;

-- Query the stream
SELECT
    *,
    METADATA$ACTION,       -- 'INSERT' or 'DELETE'
    METADATA$ISUPDATE,     -- TRUE if this DELETE is the old row of an UPDATE
    METADATA$ROW_ID        -- unique row identifier
FROM orders_stream;

Stream Offset and Consumption

A stream tracks changes from its offset (its last consumed position). After consuming (via a DML inside a Task or explicit statement), the offset advances:

sql

-- Consume the stream in a MERGE (most common pattern)
MERGE INTO analytics.orders tgt
USING orders_stream src
ON tgt.order_id = src.order_id
WHEN MATCHED AND src.METADATA$ACTION = 'DELETE' THEN DELETE
WHEN MATCHED AND src.METADATA$ACTION = 'INSERT' THEN UPDATE SET
    tgt.amount = src.amount,
    tgt.status = src.status
WHEN NOT MATCHED AND src.METADATA$ACTION = 'INSERT' THEN INSERT
    (order_id, amount, status) VALUES (src.order_id, src.amount, src.status);

⚠️Warning

Streams consume Time Travel to track their offset. If the Time Travel period expires before the stream is consumed, the stream becomes stale. Always consume streams within the retention window.

Tasks — Scheduled Execution

A Task schedules SQL execution — it is Snowflake's native job scheduler.

Task Types

Property	Warehouse-Based Task	Serverless Task
Compute	Uses a named virtual warehouse	Snowflake-managed serverless
Cost control	Warehouse Resource Monitor	Per-credit serverless billing
Best for	Predictable, heavy workloads	Lightweight, frequent tasks

sql

-- Warehouse-based task (every 5 minutes)
CREATE TASK refresh_staging
    WAREHOUSE = WH_TRANSFORM
    SCHEDULE = '5 MINUTE'
AS
INSERT INTO staging.orders_processed
SELECT * FROM raw.orders
WHERE processed = FALSE;

-- Serverless task
CREATE TASK serverless_task
    USER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'SMALL'
    SCHEDULE = 'USING CRON 0 3 * * * UTC'  -- every day at 3am UTC
AS
CALL my_stored_procedure();

-- Stream-triggered task (only runs when stream has data)
CREATE TASK process_orders_task
    WAREHOUSE = WH_TRANSFORM
    WHEN SYSTEM$STREAM_HAS_DATA('orders_stream')
    SCHEDULE = '1 MINUTE'  -- check every minute
AS
MERGE INTO analytics.orders tgt
USING orders_stream src ON tgt.id = src.id
WHEN NOT MATCHED THEN INSERT ...;

-- Tasks start SUSPENDED — must be resumed
ALTER TASK refresh_staging RESUME;
ALTER TASK serverless_task RESUME;
ALTER TASK process_orders_task RESUME;

Task DAGs (Directed Acyclic Graphs)

Tasks can be chained into a DAG where downstream tasks run after upstream tasks complete:

sql

-- Root task (scheduled)
CREATE TASK root_task
    WAREHOUSE = WH_TRANSFORM
    SCHEDULE = '1 HOUR'
AS
TRUNCATE TABLE staging.work;

-- Child task 1 (depends on root)
CREATE TASK child_task_1
    WAREHOUSE = WH_TRANSFORM
    AFTER root_task
AS
INSERT INTO staging.work SELECT * FROM raw.orders;

-- Child task 2 (depends on root)
CREATE TASK child_task_2
    WAREHOUSE = WH_TRANSFORM
    AFTER root_task
AS
INSERT INTO staging.work SELECT * FROM raw.events;

-- Grandchild task (depends on both children)
CREATE TASK final_task
    WAREHOUSE = WH_TRANSFORM
    AFTER child_task_1, child_task_2
AS
CALL process_final_data();

-- Resume all tasks (must resume leaf → root or use SYSTEM$TASK_DEPENDENTS_ENABLE)
SELECT SYSTEM$TASK_DEPENDENTS_ENABLE('root_task');

Dynamic Tables

Dynamic Tables provide a declarative approach to incremental data pipelines — define the transformation as a SELECT, set a TARGET_LAG, and Snowflake automatically refreshes the table:

sql

-- Source table
CREATE TABLE orders (order_id NUMBER, customer_id NUMBER, amount DECIMAL, status STRING);

-- Dynamic table (automatically refreshed)
CREATE DYNAMIC TABLE customer_metrics
    TARGET_LAG = '1 hour'        -- max acceptable lag from source
    WAREHOUSE = WH_TRANSFORM
AS
SELECT
    customer_id,
    count(*) AS order_count,
    sum(amount) AS total_spent,
    max(order_id) AS latest_order_id
FROM orders
WHERE status = 'COMPLETED'
GROUP BY customer_id;

-- Check dynamic table status
SHOW DYNAMIC TABLES LIKE 'customer_metrics';

-- Manually trigger refresh
ALTER DYNAMIC TABLE customer_metrics REFRESH;

Dynamic Tables vs Streams + Tasks

Aspect	Dynamic Tables	Streams + Tasks
Complexity	Low (declarative)	Higher (imperative)
Lag control	`TARGET_LAG` parameter	Task schedule frequency
Custom merge logic	Limited	Full control
Dependency tracking	Automatic	Manual
Monitoring	Built-in DYNAMIC_TABLE views	TASK_HISTORY

Practice Questions

Q1. A company receives CSV files in an S3 bucket continuously throughout the day. Which feature provides the lowest-latency automatic loading without requiring a running virtual warehouse?

A) COPY INTO with a scheduled task
B) Snowpipe with AUTO_INGEST = TRUE ✅
C) Snowpipe Streaming SDK
D) Dynamic Table with TARGET_LAG = '5 MINUTE'

Q2. A stream becomes stale. What caused this?

A) The stream was consumed too many times
B) The stream was not consumed before the Time Travel period expired ✅
C) The source table was dropped and recreated
D) The task consuming the stream was suspended

Q3. Which Snowpipe feature enables row-level data streaming with sub-second latency, without first staging files?

A) AUTO_INGEST = TRUE
B) Snowpipe REST API
C) Snowpipe Streaming SDK ✅
D) Append-only Stream

Q4. A Task is created but no data is being processed. What is the most likely cause?

A) The warehouse is too small
B) The task is in SUSPENDED state and needs to be RESUMED ✅
C) The task's schedule is too infrequent
D) The stream has already been consumed

Q5. A Dynamic Table is configured with TARGET_LAG = '30 minutes'. What does this guarantee?

A) The table refreshes every 30 minutes on the clock
B) Data in the table is never more than 30 minutes behind the source ✅
C) The refresh job runs for at most 30 minutes
D) Results are cached for 30 minutes

Q6. When using SYSTEM$STREAM_HAS_DATA() in a Task's WHEN clause, what happens if the stream is empty?

A) The Task runs and executes an empty merge
B) The Task is skipped for that schedule interval ✅
C) The Task fails with an error
D) The Task suspends itself

✅Success

Key Takeaways for Exam Day:

Snowpipe = file-based, serverless, near-real-time (seconds to minutes)
Snowpipe Streaming = row-based, sub-second, no staging required
Streams go stale if not consumed within the Time Travel window
Tasks start SUSPENDED — always resume them explicitly
Dynamic Tables = declarative TARGET_LAG approach — simpler than Streams + Tasks
Task DAGs: child tasks defined with AFTER <parent_task>

Progress58%

Domain 3.1 — Data Loading and Unloading

Domain 3.3 — Snowflake Connectors and Integrations

Course Content

1. Domain 1.1 — Snowflake Architecture 2. Domain 1.2 — Snowflake Interfaces and Tools 3. Domain 1.3 — Snowflake Object Hierarchy and Types 4. Domain 1.4 — Configuring Virtual Warehouses 5. Domain 1.5 — Snowflake Storage Concepts 6. Domain 1.6 — AI/ML and Application Development Features 7. Domain 2.1 — Snowflake Security Model and Principles 8. Domain 2.2 — Data Governance Features 9. Domain 2.3 — Monitoring and Cost Management 10. Domain 3.1 — Data Loading and Unloading 11. Domain 3.2 — Automated Data Ingestion 12. Domain 3.3 — Snowflake Connectors and Integrations 13. Domain 4.1 — Evaluating Query Performance 14. Domain 4.2 — Optimizing Query Performance 15. Domain 4.3 — Snowflake Caching 16. Domain 4.4 — Data Transformation in Snowflake 17. Domain 5.1 — Data Collaboration, Replication, and Business Continuity 18. Domain 5.2 — Secure Data Sharing and Data Clean Rooms 19. Domain 5.3 — Snowflake Marketplace and Native Apps