Mastering Incremental Data Ingestion: Key Strategies for Accuracy and Efficiency

We discussed data ingestion and ingestion patterns in our last blog. Today, we will focus on the incremental data ingestion pattern and highlight key aspects for designing effective incremental ingestion pipelines.

In the realm of data engineering, ingesting large datasets with completeness (no data loss), minimal duplication, and efficiency is paramount for maintaining scalability and cost-effectiveness of ingestion pipelines. Bandwidth between the data source and the big data analytics platform also needs to be considered. Pulling a full load of data every time to refresh the target may not be feasible due to source system constraints. Moreover, it is both costly and inefficient.

Hence, incremental data ingestion becomes essential, wherein only new or updated records are pulled from the data source. This approach reduces the amount of data transferred, lowers the load on source systems, shortens ingestion time, and optimizes storage. Collectively, these factors positively impact overall ingestion costs while ensuring the data remains fresh and complete. Imagine reloading gigabytes or terabytes of data for every refresh, most of which already exists in the target system, with only a few new records added—clearly inefficient.

Figure 1: Incremental load

While the benefits are obvious, implementing incremental ingestion is not straightforward and requires careful upfront planning. The following are key considerations:

· Checkpoint logic: Ensure a reliable checkpointing mechanism is in place.

· Merging at the target: Design effective strategies to merge updates and insert new records . correctly.

· Identifying keys: Define the primary/composite key for merge logic and incremental . column(s) for checkpointing.

· Deduplication: Prevent duplicate records.

· End-to-end strategies: Plan for complex and challenging scenarios.

Tip: Always validate your checkpoint logic and keys with the DBA and key stakeholders. Any deviation on the data source side will require coordinated changes at the target, and identifying potential data loss later can be time-consuming.

This blog provides an overview of considerations, actionable insights, and common pitfalls to avoid data inconsistencies and performance bottlenecks. Let’s explore the core elements.

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Importance of An Initial Full Load

Before starting with incremental data ingestion, we need a baseline—this comes from an initial full load.

In this step, all historical records from the source are brought into the target. Depending on your requirements, you may load the entire history or only a subset. Since most big data platforms offer inexpensive storage, it’s generally recommended to pull in all historical data.

Example: Imagine ingesting sales data from an ERP system into a data lake. If historical transactions are needed for trend analysis, a one-time full extract ensures the target is populated comprehensively.

Why it matters:

· Without a baseline, incremental logic could miss older records.

· It ensures downstream systems and reports have complete historical context (important . since some source systems truncate data to save cost, unlike big data platforms).

· It provides a reference point for validating incremental data later.

· It helps test how source systems behave under load (response time, throughput, etc.), so you . can fine-tune your pipelines.

· It’s especially critical when migrating data systems.

Tips:.  ·  ·  ·  · Coordinate the full load with the DBA—it can put heavy load on source systems.  ·  · If possible, schedule it after hours or on weekends.   · · Use this step to test optimizations, like applying parallelism or splitting data into     partitions for faster ingestion.

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Selection and Utilization of Incremental Columns

The backbone of incremental data ingestion is the incremental column(s)—usually a datetime column or a sequence (like an auto-incrementing ID). The purpose of this column is to identify new, updated, or deleted records since the last checkpoint.

Common examples (not a complete list):

· Timestamp-based:

o   insert_date / update_date

o   create_date / modified_date

o   last_modified_date

o   last_update_date

· Sequence-based:

o   auto_increment ID

o   transaction_id

o   lsn (log sequence number)

Timestamp columns are versatile for change data capture (CDC)—you simply filter records where last_modified > previous_checkpoint. They’re especially useful in databases like PostgreSQL that natively handle timestamps. Sequence columns, common in relational systems, guarantee monotonic increases and simplify queries like WHERE id > last_id.

Tip:  Always validate that your chosen incremental column: · Never resets for any reason · Has an index (for query efficiency in RDBMS · Can’t be manually updated · Reliably changes on inserts/updates

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Understanding the Checkpoint Logic

A checkpoint is the last recorded value of the incremental column(s) up to which data was ingested into the target. In the next pipeline run, data is pulled where inc_col > checkpoint – X minutes. This small reduction creates a safe overlap between already ingested data and new data, acting as a buffer for late-arriving records.

Example: If your pipeline runs every 30 minutes, you might set the checkpoint back by 5 minutes to ensure no late records are missed.

Of course, this buffer introduces some duplication—but it’s necessary. The right overlap depends on the source system and should be validated. I’ve worked with systems requiring a 15-minute overlap for a 30-minute run to prevent data loss, and others where simple >= logic was enough. Regardless, you’ll need to deduplicate on the target (using primary/composite keys) and merge records correctly based on the incremental column(s).

Tip: For large datasets, split the initial full load into batches or partitions. Design your pipeline to accept start_date and end_date as parameters so ingestion can run in chunks. This same pipeline can double as an auto-heal pipeline—useful if your incremental process fails for some time. Instead of scrambling to recover missing data, you can simply rerun the pipeline for the affected dates. This approach can save countless hours for your data engineering team. (We’ll cover auto-heal pipelines in detail in an upcoming blog.)

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Managing Checkpoint

The checkpoint value determines which increments are ingested in the next pipeline run. How this value is calculated and stored is crucial, as it directly impacts performance. Checkpoint can be calculated in two ways:

Source Based Checkpoint

Before starting ingestion, query the source system to get the latest value of the incremental column (e.g., SELECT MAX(last_modified) FROM source_table). This fetches the checkpoint upfront, avoiding target-side computations. It’s lighter on your data platform but assumes the source can handle frequent metadata queries without performance issues.

Pros:

· Avoids scanning large target datasets.

· Faster to compute in most cases

Cons: 

· Adds extra queries on the source (may be an issue if the source is sensitive to load)

Example (using ADF):
1. Retrieve the previous checkpoint value from your metadata config/files.
2. Query the source for the latest checkpoint value (e.g.,

 SELECT MAX(incremental_column) FROM your_table).

3. Compare the two:
    o  If the stored checkpoint < source checkpoint → new records exist → run copy activity.
    o  Within the copy activity, use a query like:

        SELECT *
          FROM your_table
           WHERE incremental_column > checkpoint_value - N minutes

Target Based Checkpoint

You can also calculate the checkpoint from the target system (e.g., taking the max value of the incremental column from the last ingested dataset). This method is accurate but requires compute on the target, which may extend pipeline runtime. It’s a good fit when the source cannot handle additional queries.

Pros:

· No extra queries on the source

· Works even if the source is offline during ingestion

Cons:

· Can be compute-heavy for large datasets

· Increases overall pipeline runtime

Example (using ADF): 
If you choose a target-based checkpoint, your pipeline will look like this:

1.   As before, retrieve the checkpoint value from the last pipeline run (stored in metadata . configs/files).

2.   Instead of querying the source for the next checkpoint, directly execute the copy activity.

3.   After ingestion, compute the next checkpoint value using a processing engine (e.g., Synapse, . Databricks). For example, I’ve used a Databricks notebook activity that reads the ingested data . path, extracts MAX(incremental_column), and passes it to the next activity.

4.   Update the checkpoint value.

5.    Perform logging.

From experience, source-based checkpointing is preferable for high-volume scenarios as it decouples checkpointing from load operations and reduces compute on the target. Always store checkpoints externally (in a metadata table or config file) to persist across runs. Automate checkpoint updates only after successful ingestion, and include error handling—for example, if a load fails, rollback to the prior checkpoint to prevent gaps.

For highly critical data loads, consider a hybrid approach: validate the source checkpoint before ingestion and confirm it against the target checkpoint after ingestion.

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Handling Scenarios If there’s No True Incremental Column

Some systems don’t maintain a reliable timestamp or sequence for change tracking, and pulling full data is not feasible for reasons mentioned earlier.

From experience, here are some alternative strategies:

Partition-based Incremental Data Ingestion Loads

If the data is partitioned (e.g., by date, region, or ID range), ingest only the relevant partitions.

For example, if data is stored in daily folders like /data/2025-08-14/, you can ingest just today’s and yesterday’s partitions.

· Use metadata to determine the last refreshed partition.

· Ingest only new or updated partitions.

Tip:  If you’re using Azure Data Factory or Fabric Data Factory, you can leverage built-in partition options to improve throughput by ingesting multiple partitions in parallel (refer link) (we’ll cover this in detail in another blog).

Rolling Window Incremental Data Ingestion Loads

Pull data for the last N days (or another time window), even without an incremental column.

Example: Fetch the last 7 days of data daily
(e.g., WHERE modified_date >= CURRENT_DATE – 7).

· Benefits: Captures late-arriving records with a large overlap window.

· Drawback: The wider the window, the more data you unnecessarily reprocess for . deduplication.

Periodic Full Loads

Run incremental loads daily but schedule a periodic full load to reset and correct missed changes.

Example: Load the last 7 days daily, and at month-end run a full historical load.

· Consideration: Not practical for extremely high-volume datasets due to load times and . resource costs.

Note: Rolling window and periodic full loads should be fallback options. Both generate large volumes of duplicate data that must be deduplicated at the target. For very high historical volumes, this approach won’t scale—reloading weeks of data daily can overwhelm resources.

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How to Track Deletion at Source

Soft Deletes at Source

Soft deletes mark a record as inactive (deleted) via a flag column while updating the incremental column. At the target, you ingest based on the incremental column and check this flag to update the record’s status. This requires compute and configuration on the target.

Hard Deletes at Source

Hard deletes remove the record entirely

(e.g., DELETE FROM your_table WHERE record_id = 'x'). 

These are harder to track since no record remains. In this case:

· Identify how far back deletes may occur (e.g., 60 days).

· Pull primary/composite keys for that window and compare them with the target.

· Mark missing records as deleted in the target.

At the target, deletes are usually implemented as soft deletes so historical analysis on removed records remains possible.

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Validation

Initial Load

After the initial load, validate row counts and sample records against the source system to ensure data integrity.

Incremental Loads

Without thorough testing, incremental loads may silently introduce data gaps. Such issues can remain undetected for long periods unless regular validation checks are in place.

Always perform these checks:

· Row counts – Compare expected vs. actual rows.

· Sample records – Verify that recent updates appear in the target.

· Date range validation – Ensure the incremental column in the target matches the expected min/max for the load period.

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Conclusion

Incremental ingestion and consolidation can save enormous compute and storage costs while keeping pipelines lean and responsive.

However, success depends on balancing efficiency, accuracy, and resilience through careful planning:

· Start with a complete historical load.

· Choose a reliable incremental column.

· Design a checkpointing system that balances freshness with duplicate avoidance.

· Have a fallback strategy for sources without incremental columns.

· Build deduplication and validation into downstream processes.

· Monitor pipelines—track load times, duplicate rates, and completeness via audits.

. Remember: what works at gigabyte scale may break at terabyte scale.

When done right, these practices keep your data accurate and timely—fueling analytics, reports, and decision-making without overwhelming your systems.

Ready to refine your pipelines? Start by assessing your sources, prototype in a sandbox, and then scale confidently. Share your incremental ingestion challenges in the comments—what strategies have worked for you?

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