Full Refresh vs Incremental Refresh in ETL: How to Decide?

ETL (Extract, Transform, Load) tools are essential for modern organizational data management. These tools enable you to integrate large datasets with ease while minimizing the need for extensive manual intervention. However, to fully optimize your ETL process, it’s important to determine the best method to load data from source to destination.

If you want to reload everything from scratch, a full load would be a suitable choice. On the other hand, an incremental refresh is well-suited if you want to only focus on new or updated data.

This article will help you evaluate the workings of full refresh vs incremental refresh, when to implement them, and some real-world use case scenarios.

Understanding Data Refresh Strategies

A data refresh strategy involves updating or replacing the existing data in the target system with the latest data from a source. Choosing the right refresh strategy is important as it directly affects how updates are processed and reflected in your system.

The two common data refresh strategies are full refresh and incremental refresh. Each strategy has a specific use case, which depends on the frequency of updates, size, and system performance considerations.

While full refresh enables you to replace the entire dataset, incremental refresh involves updating only new or changed records. Adopting these strategies allows you to optimize large data volume deployment and synchronize workflows.

Full Refresh: Deep Dive

A full refresh strategy helps you update or replace all the target system's existing data with the source's latest data. It involves re-fetching every row and column without checking for data changes. Full refresh is also known as destructive load, as it necessitates truncating the target table before loading the new data.

‍When to Use Full Refresh

• Complete Data Accuracy Requirements: To ensure all records precisely match the source data for complete accuracy.‍
• Small Data Volumes: Full refresh is feasible for smaller datasets to minimize processing time and resource usage.‍
• Simple Source Systems: It is suitable for systems with straightforward structures and minimal dependencies, simplifying data extraction and reducing the risk of errors.‍
• Infrequent Updates: It works well when data updates are rare, reducing the overhead associated with frequent data loading.

‍Advantages of Full Refresh

• Data Consistency: With full refresh, you can ensure that your target system aligns with the source data. This reduces any chances of discrepancies, providing data consistency.‍
• Simple Implementation: Implementing a full refresh is straightforward, as it involves replacing the entire dataset without requiring any complex incremental logic.‍
• Reliable Audit Trails: After the completion of a full refresh, a timestamp is generated, marking the exact moment when the dataset was successfully loaded into the target system. These timestamps serve as a clear and traceable record, facilitating reliable audit trails.‍
• Error Recovery: Implementing a full refresh helps you avoid errors and incomplete data caused by partial updates. This approach simplifies troubleshooting and recovery, as it resets the target system to a reliable state directly from the source.

‍Disadvantages of Full Refresh

• Resource Intensive: A full refresh involves updating all the data. This requires more computational resources, including memory and storage, straining your system.‍
• Extended Processing Time: Replacing the entire dataset requires significant processing time, particularly with large volumes of data, which can lead to delays.‍
• Increased Network Load: Transferring extensive data during a full refresh can place a heavy load on the network, requiring more bandwidth. This can lead to network congestion or slow down other processes.

Incremental Refresh: Comprehensive Look

An incremental refresh strategy allows you to load only the updated or newly created data from the source into your destination system. This approach optimizes resource usage in ETL pipelines and is often time-based to identify the data changes accurately.

‍When to Choose Incremental Refresh

• Large Data Volumes: Incremental syncing of data is suitable for extensive datasets, as it minimizes the amount of data processed by updating only new or modified records.‍
• Frequent Updates: In environments with regular data changes, incremental refresh ensures only recent modifications are loaded, avoiding redundant reloads.‍
• Resource Constraints: Incremental refresh is appropriate when your data pipeline operates under resource constraints, as it reduces the computation and storage requirements.‍
• Real-Time Requirements: Choose incremental refresh when your process requires up-to-date information, as it enables faster synchronization by focusing only on changes.

‍Advantages of Incremental Refresh

• Faster Processing: Since the focus of incremental refresh is appending new records or updating existing ones in the target system, it speeds up operations. This is more beneficial than reprocessing the entire dataset.‍
• Lower Costs: The reduced processing and storage requirement eliminates the need for extensive computational resources. By optimizing resource allocation, you can reduce overall operational costs.‍
• Reduced Network Load: With the transfer of only updated or newly added data from source to target, incremental refresh decreases the load on network bandwidth and improves performance.

‍Disadvantages of Incremental Refresh

• Complex Implementation: Setting up incremental refresh involves technical complexities and configuration of change detection mechanisms to ensure correct data synchronization.‍
• Potential Data Inconsistencies: Data inconsistencies may arise because of concurrent updates, errors during transformation, or incomplete change tracking. These factors can lead to incomplete or incorrect data.‍
• Change Tracking Requirements: To implement efficient and accurate incremental refresh, you must thoroughly monitor and track changes in the data sources. This process can become tedious and error-prone with frequent data loads.‍
• Error Handling Complexity: Managing errors is more complex in the incremental refresh, as they may only affect parts of the dataset. This makes it difficult to identify the source of the issue, increasing the complexity of maintaining integrity throughout the refresh process.

Full Refresh Vs. Incremental Refresh: Difference Table

The main difference between Full Refresh and Incremental Refresh is that Full Refresh reloads the entire dataset from scratch, ensuring data consistency, while Incremental Refresh updates only new or changed data, reducing processing time and resource usage.

Technical Implementation Guide

The ETL process consists of three significant steps: extraction, transformation, and loading. During the extraction stage, a refresh strategy—either full or incremental—is selected depending on how data is fetched from the source and loaded to the destination.

Here is a detailed explanation of the difference between implementing a full refresh vs incremental refresh.

Full Refresh Implementation

Following are the basic steps involved in implementing a full data refresh within the ETL process:

1. Define the Target Table: Set up the source and target systems. Ensure the incoming data schema matches the target table, including column names, data types, and constraints like primary keys.‍
2. Extract Data: To extract the data from the source system, you can use methods like SQL queries, API calls, or file extraction.‍
3. Transform Data: Transform the extracted data to ensure its compatibility with the target system.‍
4. Delete Existing Data: Before loading, you must remove any old data from the target system using commands like TRUNCATE and DELETE to ensure no data is left behind.
5. ‍Load Data: Load the transformed data into the destination system to complete the refresh.

Code Example

Setting up ETL using Python

• Install the required Python modules.
• Set up the ETL Directory.
• Download the source file and extract the contents using the Extract function.

def extract_from_csv(file_to_process):
    dataframe = pd.read_csv(file_to_process)
    return dataframe

• Implement transformations to convert data in a format that matches the target table.

def transform(data):
       data['price'] = round(data[‘price’], 2)
       return data

• Before loading the data into the target system, delete the existing file to clear the old data. You can use a delete function to do that.
• The last step is to load the data into the destination file.

def load(targetfile,data_to_load):
    data_to_load.to_csv(targetfile)

Error Handling

Handling errors ensures that your ETL process doesn’t fail silently or leave incomplete or corrupt data in the target system. To maintain data integrity, you can implement validation measures during the transformation process. These measures can include data quality checks, checking database connectivity, and implementing fail-safe mechanisms like rollback.

Performance Optimization

You can use various techniques to optimize your ETL pipeline. These include batch processing, indexing, parallel processing, efficient logging, data compression, and more. By implementing these techniques, you can enable your ETL pipeline to handle large datasets effectively without challenges like excessive resource usage or runtime constraints.

Incremental Refresh Implementation

Define Target Table Schema: The first step is the same as a full refresh; you must define the target table's schema.‍

Delta Identification: It is the process of identifying and processing only the data that has changed or updated since the last load or refresh. Change tracking methods can help determine the change data. Common approaches include:

• Last Updated Timestamps: A timestamp field in the source system records the previous update time for each row.‍
• Control Table: Maintain a control table that stores metadata such as the last extraction timestamp and the number of records processed. This method helps track changes without relying only on timestamps in the source data.‍

Watermark Management: In incremental refresh, a watermark tracks the point up to which data was successfully extracted in the previous cycle. This watermark can be a timestamp or a unique identifier. During the next ETL cycle, the tool fetches records that have been updated or added after the timestamp or identifier, ensuring only changed data is processed.‍

Handling Late-Arriving Data: Late-arriving data is data that should’ve been included in a batch but arrives after the initial processing window. This can cause inconsistencies and inaccuracies. To address these issues, you can change the data capture and reconciliation processes and implement data quality checks.

Real-World Examples: Full Refresh vs Incremental Refresh

Financial Transaction Processing: Uber

Uber, a transportation company, connects riders to drivers and also offers food delivery and courier services. The company’s financial transaction processing involves handling critical datasets such as driver and courier earnings. Sometimes, riders may choose to tip drivers hours after their ride, resulting in late-arriving data that adds to the initial record of base earnings.

‍Challenge‍

In a traditional ETL approach, Uber has to process multiple months of data to capture updates, which can be difficult.

‍Solution‍

Uber leverages Apache Hudi’s incremental processing primitives to capture and process late-arriving data.‍

Implementation‍

Incremental ETL pipelines are built using a combination of Apache Hudi, Spark, and the workflow management system Piper. Uber’s framework utilizes Apache Hudi’s DeltaStream to author and manage pipelines with minimal configuration.

Results‍

By using an incremental framework across its applications, Uber achieved the following benefits:

• 50% reduction in pipeline runtime by processing only incremental changes.
• 60% improvement in SLA adherence by 60%, ensuring faster data availability.
• Eliminated the need for constant reprocessing of old data.

Analytics Data Pipeline

Spotify, a well-known music streaming service, offers access to millions of songs and podcasts. It curates playlists, such as Discover Weekly, which is updated every week with 30 songs based on user listening habits and preferences.

‍Goal‍

Develop an ETL pipeline to extract, transform, and load data from the Spotify Discover Weekly playlist, ensuring it updates automatically.

‍Solution‍

A full refresh is implemented weekly using Python and AWS services to automate the process.

‍Implementation

• Data Extraction: Spotify uses its library to authenticate and interact with the Spotify API, which stores the necessary credentials in AWS Secrets Manager. Spotify then sets up a Lambda function to extract data from the playlist, which is stored in an S3 bucket as JSON raw files. To ensure regular updates, an EventBridge rule is configured to trigger the Lambda function once a week. This helps align the weekly update for the Spotify playlist.
• ‍Transformation: When the new files are added to the process folder in S3, the bucket invokes another Lambda function responsible for transformation. The transformed data is stored in subfolders. Files in the to-process folder are moved to the processed folder, and the files in the to-process folder are deleted.
• ‍Data Loading: AWS Glue Crawler is set up to infer the schema when new data arrives in the subfolders. The Glue catalog holds the metadata. Amazon Athena is used to interact with the dataset using SQL queries to gain insights into the top songs, most streamed artists, and frequent artists.

‍Results

• The ETL pipeline runs automatically weekly without manual intervention.
• The data is consistently available in the S3 bucket for querying and analysis.
• AWS serverless architecture minimizes costs by charging only for actual resource usage.

Integrating Airbyte Into Your ETL Workflow

Whether you choose an incremental refresh or full refresh, Airbyte provides the reliability needed to keep your data integration process smooth and scalable. It is a robust, AI-powered data integration tool that supports both ELT and ETL. With Airbyte, you can easily build and manage data pipelines, ensuring efficient data transfer and transformation.

Key Features: 

• Extensive Connector Library: It offers a library of 550+ pre-built connectors, including databases, APIs, and analytical platforms. Using these connectors, you can build a no-code data pipeline within minutes.
• ‍Flexible Sync Modes: Airbyte allows you to choose from different synchronization modes, which determine how data will be read from the source and written into the destination. A sync mode is a combination of source and destination modes.

Synchronization modes in Airbyte: 

• Incremental Append: Syncs only new or updated records from the source and writes them into the destination tables.
• ‍Incremental Append + Deduped: Syncs the latest records, writes them in the destination, and also provides a de-duplicated view.
• ‍Full Refresh Append: Sync all the data from the source and write in the destination without deleting existing records.‍
• Full Refresh Overwrite: Syncs the data from the source and replaces the already existing data in the destination by overwriting it.

For more details about the data sync modes in Airbyte, refer to the Airbyte documentation. 

Conclusion

Full refresh and incremental refresh strategies are critical in transferring the data accurately between a source and a destination. The key difference between full refresh vs incremental refresh lies in their approach to synchronizing data. While both strategies have their advantages, the choice depends on your data processing needs. The right strategy can improve the efficiency and performance of your workflows.