Full Reload Versus Incremental Strategy

Who this is for

ETL Developers, Data Engineers, and Analysts who design or maintain pipelines and need to choose between loading entire datasets or just the changes.

Prerequisites

• Basic SQL: SELECT, JOIN, INSERT, UPDATE
• Familiarity with ETL/ELT concepts
• Understanding of primary keys and timestamps

Why this matters

Choosing full reload vs incremental determines cost, speed, and reliability of your pipelines. In real projects you will:

• Load product catalogs nightly without downtime
• Refresh large fact tables hourly without missing late records
• Keep dimensions accurate using slowly changing dimension (SCD) patterns
• Recover gracefully from failures with idempotent, restartable jobs

Concept explained simply

Full reload: Drop or truncate the target and reload the entire dataset. Simple, consistent, but expensive and slow at scale.

Incremental load: Load only what changed since the last run (the delta). Fast, cost-efficient, but needs careful change detection and deduplication.

Mental model

Think of a bookshelf:

• Full reload: Replace the whole shelf with a fresh copy
• Incremental: Only add/replace the books that changed

How to choose between full and incremental

• Data volume and SLA: Big tables + tight windows favor incremental
• Source capabilities: If you have CDC or reliable timestamps, incremental is easier
• Cost and compute: Full reload can be costly at scale
• Schema volatility: Frequent schema changes may favor a simpler full reload until stable
• Late-arriving data: If common, plan incremental with a sliding window and upserts
• Deletions: Full reload handles naturally; incremental needs explicit delete logic
• Data quality: Full reload guarantees a consistent snapshot; incremental needs idempotency checks

Fast decision checklist

• Is the table small (under a few million rows) and load window is generous? Consider full reload
• Do you have last_updated or CDC? Choose incremental
• Need near real-time data? Incremental
• Do deletes matter and are tracked? Incremental with delete handling
• Frequent schema changes with limited engineering time? Temporary full reload

Worked examples

Implementation patterns

Full reload patterns

• Truncate-Load: TRUNCATE target then INSERT SELECT from source
• Swap Tables: Load into temp table, then atomic rename/swap to minimize downtime
• Partition Overwrite: Overwrite all partitions (or selected partitions)

Incremental patterns

• Watermark: last_updated > last_checkpoint
• CDC Stream: Apply inserts, updates, deletes in order
• Hash-Diff: Compare hash of business columns to detect changes
• Sliding Window: Reprocess a recent time window to catch late data

Idempotency and dedup

• Use MERGE/UPSERT for deterministic outcomes
• Deduplicate staging by primary key and latest timestamp
• Store checkpoints (e.g., max processed timestamp)

SQL snippets

-- Target: dim_country
TRUNCATE TABLE dim_country;
INSERT INTO dim_country (country_id, country_name, iso_code)
SELECT id, name, iso FROM stg_country;

-- Load changed rows to staging_incremental first
-- SELECT * FROM source WHERE last_updated > :watermark

MERGE INTO tgt_customer t
USING stg_customer_changes s
  ON t.customer_id = s.customer_id
WHEN MATCHED AND t.hash_diff <> s.hash_diff THEN
  UPDATE SET
    t.first_name = s.first_name,
    t.last_name  = s.last_name,
    t.email      = s.email,
    t.hash_diff  = s.hash_diff,
    t.last_updated = s.last_updated
WHEN NOT MATCHED THEN
  INSERT (customer_id, first_name, last_name, email, hash_diff, last_updated)
  VALUES (s.customer_id, s.first_name, s.last_name, s.email, s.hash_diff, s.last_updated);

-- Assume stg_customer_cdc has op in ('I','U','D')
MERGE INTO tgt_customer t
USING stg_customer_cdc s
  ON t.customer_id = s.customer_id
WHEN MATCHED AND s.op = 'D' THEN DELETE
WHEN MATCHED AND s.op = 'U' THEN UPDATE SET ...
WHEN NOT MATCHED AND s.op = 'I' THEN INSERT (...)
VALUES (...);

-- Example: reload last 2 days of partitions
DELETE FROM fact_sales WHERE sale_date >= current_date - INTERVAL '2' DAY;
INSERT INTO fact_sales
SELECT * FROM stg_sales
WHERE sale_date >= current_date - INTERVAL '2' DAY;

Exercises

These exercises mirror the tasks below. After completing them, open the Quick Test to check your understanding. Anyone can take the test; only logged-in users will have their progress saved.

Self-check checklist

• I can state two pros and two cons for full reload
• I can state two pros and two cons for incremental
• I can explain watermark vs CDC
• I can write a MERGE that is idempotent
• I know how to handle deletes and late-arriving data

Common mistakes and how to self-check

• No dedup in staging: Fix by selecting latest record per key before MERGE
• Trusting timestamps that are not updated on change: Use CDC or hash-diff
• Ignoring deletes: Incorporate delete logic (soft/hard) in incremental
• Too-narrow window for late data: Add sliding window reprocessing
• Non-idempotent loads: Use MERGE or overwrite-by-partition to allow safe retries

Practical projects

• Build a pipeline that fully reloads two small dimensions nightly using swap tables
• Create an incremental load for a large fact with watermark, MERGE, and a 48h sliding window
• Implement CDC-driven upsert+delete for a customer table, including a soft delete flag and audit counts

Learning path

1. Master full reload patterns (truncate-load, swap)
2. Learn watermark-based incremental with MERGE
3. Add CDC and delete handling
4. Implement sliding windows and partition strategies
5. Introduce audits, checkpoints, and monitoring

Mini challenge

You have a 500M-row orders table updated throughout the day. The source has reliable last_updated and occasional deletes via CDC. Design a daily job that:

• Runs hourly
• Captures updates/inserts
• Handles deletes
• Corrects late-arriving rows within 24h

Next steps

• Do the Quick Test below to validate your understanding
• Revisit your last pipeline and label each table as full or incremental with justification
• If you’re logged in, your test results and progress will be saved automatically; otherwise, you can still take the test for free