How to Implement Incremental Updates in Your Data Pipeline for Better Performance

How to Implement Incremental Updates in Your Data Pipeline for Better Performance

Data pipelines are a vital component of modern-day data processing systems. These pipelines enable the efficient and consistent flow of data from multiple sources to their respective destinations. 

 

One key feature of these data pipelines is the ability to Update incrementally. Essentially, it refers to the capability of a system to update or modify data in small, incremental steps rather than executing a full, time-consuming update of the entire dataset. 

 

In the context of data processing, incremental updates are especially beneficial when dealing with massive volumes of data constantly changing or evolving over time. Instead of processing the entire dataset each time an update is required, incremental updates allow for more efficient data processing by focusing on the modifications or additions that have taken place since the last update. 

 

This offers a myriad of benefits, including

  • Time efficiency
  • Resource optimization
  • Near real-time processing
  • Data consistency
  • Easy scalability

In today’s blog, we will discuss everything about building data pipelines with incremental updates functionality. We will talk about the different incremental update techniques that you can implement to ensure easy data integration and consistency.

 

Designing an Incremental Data Pipeline

Having an incremental data pipeline ensures that it runs much faster as it has lesser data to interact with. 

 

To effectively design and implement incremental updates in your data pipelines, you can choose from the following two methods:

  1. Source Change Detection
  2. Destination Change Comparison

Let’s learn about the two methods in detail. 

 

Source Change Detection

Source change detection or identification is a pattern in which we use specific selection criteria from the source system to retrieve only the modified or newly added data since the last load process occurred. This is an efficient method as it limits the amount of data that is pulled into the pipeline by only extracting the data that needs to be moved, leaving the unmodified data as is. This, in turn, reduces the time for data processing. 

 

Source change detection is typically implemented in one of the following ways:

  • Using row-level insert or update dates: This is often the quickest and easiest way to detect changes in the source. With this technique, there is a column for each table that notes the date when the data was last loaded and modified. Thus, it is easy to quickly identify each row when the data in it changes.¬†
  • Change data capture: This technique of source-side detecting is available in SQL Servers and other RDBMS systems. It helps to identify data that has been newly added, altered, or deleted.¬†
  • Change tracking: This is one of the most preferred techniques for handling change detection, thanks to its simplicity, dependability, and ease of use. It also helps to identify the rows of data that have been added, changed, or eliminated since the last load.¬†

 

Destination Change Comparison

If the source for a given data pipeline does not support source change identification, destination change comparison is the next best alternative. The technique helps to determine which data must be updated or inserted. 

 

Typically, the process involves a row-by-row analysis to differentiate the unaltered data from the one that has been recently modified or created. This is a slightly time-consuming process as compared to source change detection. To make it work, all the data must be transferred into the pipeline. 

 

Moving on, the destination change comparison technique can work on all kinds of data sources, including XML, text files, API result sets, and other non-relational data structures. 

 

Incremental Update Techniques

Change Data Capture (CDC) is the most popular technique used to identify and capture incremental changes in a data source. It enables the extraction and replication of only the modified data, reducing the need to process the entire dataset. CDC plays a vital role in incremental updates by efficiently tracking changes in the source data and applying those changes to the destination system.

 

There are many different CDC mechanisms. Enterprises can choose to implement the one that best matches their business needs. 

  • Log-based CDC: In log-based CDC, the transaction logs or redo logs of the source database are monitored. These logs contain a record of all changes made to the database, including inserts, updates, and deletes. By analyzing the log files, CDC tools can capture the changes and propagate them to the destination system. Log-based CDC is efficient and provides low-latency updates.
  • Trigger-based CDC: Trigger-based CDC involves creating triggers on the source database tables to capture changes. These triggers are executed when data modifications occur, allowing the CDC process to identify the affected rows and capture the changes.
  • Timestamp-based CDC: Timestamp-based CDC relies on comparing timestamps associated with each data record to determine if it has been modified since the last update. This technique requires the source data to have timestamp columns indicating the last modification time. By comparing the timestamps, the CDC process identifies the updated data and captures the changes. Timestamp-based CDC is simple to implement but may not be suitable for all scenarios, especially when there is a need to capture fine-grained changes.
  • Query-based CDC: Query-based CDC involves periodically querying the source database to retrieve data changes. This technique typically involves comparing the source data against a reference point (e.g., a previous snapshot) to identify the changes. Query-based CDC is versatile and can work with various data sources, but it can introduce additional load on the source database.

 

Each CDC mechanism has its advantages and considerations, and the choice depends on factors, such as the database system, data volume, latency requirements, and the nature of the updates.

 

The experts at Algoscale have been leveraging their expertise in data engineering and cloud technologies to assist companies in implementing CDC techniques. For instance, they have been using AWS Data Migration Service (DMS) to support CDC techniques by capturing changes from the source database’s transaction logs and duplicating them to the target database, which can include Amazon Redshift or AWS S3.

 

Our experts also have wide-ranging expertise in cloud platforms such as AWS Lamba, Amazon Redshift, and Azure Data Factory that support CDC. Azure Data Factory, for instance, can be configured to monitor changes in the source data and perform incremental data loads or transformations to the target data store, including Azure SQL Data Warehouse.

 

Complications with Incremental Updates

Incremental updates are a brilliant feature that trumps full data syncs. However, enterprises can still encounter major challenges using it. 

 

One of the foremost challenges associated with incremental updates is scalability. For instance, if a data source grows at an extremely fast pace, incremental syncs can become very large and difficult to manage. And if the duration of sync exceeds the predetermined interval for an update, it will create a form of growing data sync debt. 

 

Let’s take an example. 

 

Let’s say that a data connector is programmed to sync incrementally after every 10 minutes. Now, if the size of the sync increases, it may take more than 10 minutes to complete. If the sync takes 12 minutes to complete, the next sync will begin two minutes late. During these two minutes, additional data accumulates at the source, making the next sync longer. This delay can snowball as more data accumulates.

 

Another notable challenge with incremental updates is the granularity of timestamps. If the ‚Äúlast modified‚ÄĚ field in an API feed only provides timestamps accurate to the second, there may be ambiguity about which records were updated within a specific second. If the sync uses a ‚Äúgreater than‚ÄĚ approach, it may miss records updated within that second. To address this, the sync logic should use a ‚Äúgreater than or equal to‚ÄĚ approach to ensure all relevant records are captured. However, this can introduce duplicate records, necessitating the use of idempotence techniques to handle them.

 

Additionally, some APIs may have records that are altered at a certain timestamp but are not available to read until after that particular timestamp has passed. This can result in missing records during the sync. In such cases, a decision must be made regarding how many records to re-sync to capture any late arrivals.

 

Monitoring and Error Handling

When it comes to incremental updates, constant monitoring is very important as it helps to maintain the accuracy and integrity of the updates. By consistently tracking the data source, you can identify any discrepancies, errors, or inconsistencies in the extracted changes. Monitoring allows you to detect and address issues promptly, minimizing the risk of disseminating incorrect or incomplete data to the destination system. Additionally, you can initiate troubleshooting steps when necessary. 

 

Monitoring also allows you to optimize the performance of the pipeline, identify areas of inefficiency, and make necessary adjustments to enhance the overall speed and efficiency of the synchronisation process. You can assess key metrics such as sync duration, resource utilization, and latency to identify potential bottlenecks or areas for improvement.

Case Study: Data Warehouse for Leading Conversational Messaging Platform

One of Algoscale’s clients is a leader in conversational texting and operates a carrier-grade messaging platform that leverages AI-driven chatbots across various channels. The client faced the challenge of extracting valuable insights from millions of rows of chatbot data.

To address this issue, Algoscale offered a solution by developing a data warehouse using Amazon Redshift. An automated data pipeline was created to import all bot data from sources such as S3, MySQL, and log files to Redshift. Additionally, a Python script was created that runs on Kubernetes to manage the data-loading process.

Our solution helped the client gain the ability to analyze their chatbot data using tools like Tableau. They could derive useful insights from over 6 billion messages per month and perform complicated queries rapidly, without the need for any manual data warehouse management.

Conclusion

Implementing incremental updates in your data pipelines is an excellent way to abridge the time needed to move your data to the desired destination. It can save processing time by eliminating the need to process the entire dataset repeatedly. Additionally, it can prove to be particularly beneficial when dealing with streaming data. 

 

Our experts at Algoscale hold wide-ranging expertise in building data pipelines and providing data engineering services. We begin by assessing your specific requirements, data sources, and destination systems to determine the optimal pipeline architecture to support incremental updates. We then help you choose the most suitable techniques, such as Change Data Capture (CDC) like log-based extraction, or timestamp-based approaches based on your data sources. Our experts develop custom data connectors to efficiently extract data and transform it to meet the specific requirements of the destination system. Finally, we also assist in setting up monitoring and maintenance processes for your data pipeline. You can schedule an online consultation to get started. 

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