What is Change Data Capture (CDC) for ETL: Key Concepts Explained

In the ever-evolving landscape of data integration, Change Data Capture (CDC) is redefining how businesses can synchronize their increasing data volumes. According to a research report, the global data integration market size is projected to grow to $19.6 billion by 2026. The main driving factor for this growth is the need for real-time data analysis and insights.

CDC enables ETL tools to automatically identify and extract new changes made to the schema, and then replicate these changes to the data pipeline, which gets added to the destination. CDC is a game-changer, enabling companies to achieve efficiency, data consistency, and accurate decision-making during the ETL process. In this blog, we will provide a comprehensive guide to change data capture and how to enable it in your company’s ETL.

Let’s get started!

What Is Change Data Capture?

Change Data Capture (CDC) is a data integration technique that tracks and captures changes made to data within a database in real-time. This continuous stream of data updates provides a powerful foundation for a wide range of applications, particularly in the realm of Artificial Intelligence (AI) and Machine Learning (ML).

Key Benefits of CDC

• Real-Time Data Streaming – Enables continuous data updates, crucial for AI/ML models and real-time analytics.
• Enhanced Data Syncs: Syncs data changes in real-time, improving accuracy and enabling precise decision-making.
• Reduced Data Latency: Speeds up data processing for faster insights and response times.
• Cost-Effective Data Integration: Loads only new updates made to the schema, making it cost-efficient by minimizing pipeline and destination usage.
• Improved Data Quality & Integrity – Ensures consistency and reliability across multiple systems.

Why Use Change Data Capture?

CDC is a powerful technique that all companies must use for tracking and capturing real-time changes made to their database. It allows streaming data from your primary database to other destinations, be it data warehouses, data lakes, or analytics dashboards. Here’s how CDC will benefit organizations:

Real-Time Data Replication

CDC ensures that any changes made to your database are synced continuously through pipelines to multiple destinations such as data warehouses, data lakes, or any secondary databases that provide real-time data consistency and availability.

Cost-Effectiveness

Unlike traditional batch processing, which involves heavy provisioning on the network, CDC transfers data in continuous, smaller batches. This form of processing saves network costs and eradicates spiky load behavior. This results in smooth and cost-effective data handling with real-time insights.

With CDC, your data warehouse consistently captures the latest changes, enabling you to perform real-time analytics and gain a competitive advantage by making informed decisions with fresher data.

Higher Operational Efficiency

CDC performs efficiently with cache invalidation, automatically updating outdated entries, making sure you have access to the most up-to-date data.

Its ability also supports streaming updates to online data stores, where production systems such as machine learning models or search indexes are always current.

Compliance and Auditing

CDC enables the auditing process and promotes data governance principles by keeping an exhaustive change history.

Supplementary Applications

• Anomaly Detection & Streaming Analytics: Any anomalies detected in the database will be identified in real-time by CDC.
• Machine Learning Updates: Detects and uploads only new data to the destination, eliminating data duplication and ensuring up-to-date predictions.
• Search Index Refreshes: Ensure indexes are updated only with fresh data.

How Does Change Data Capture Work?

When you update a source database – typically a relational database, such as PostgreSQL, MySQL, Oracle, or Microsoft SQL Server – you may need to update various related resources, including a search index and a cache. With CDC, you can easily avoid issues like dual writes to, as opposed to updating resources concurrently and precisely.

It can execute this by tracking row-level changes in database-source tables categorized as update, insert, and delete events, and then making these change notifications available to any other services or systems that depend on the same data. The change notifications are sent in the same order as they were generated in the original database.

By doing this, CDC makes sure that all pertinent parties are aware of any changes to the data and are able to react suitably, whether that means updating their own copies of the data or starting relevant business procedures.

There are two methods which CDC uses

• Push: The source database pushes data downstream through a data pipeline to its destination.
• Pull: The destination and applications pull the data from the source database at fixed intervals to pull the changes made in the data.

How to Use Change Data Capture: 3 Easy Steps

Step 1: Extract the Data

Ensure your raw data is extracted from your sources and placed in the destination, which could be a data warehouse or data lake. This data can be formatted in:

• JSON – Social media (Facebook, etc.)
• XML – Third-party sources
• RDBMS – CRM

Step 2: Transform the Data

The transformation stage is where you have to apply your business rules and regulations to achieve.

• Standardization
• Deduplication
• Verification
• Sorting

Step 3: Load the Data

Load the extracted transformed data into a new destination by executing a task (job) from a CLI or GUI interface

Change Data Capture vs Change Tracking

Change Tracking is the lesser-known counterpart of change data capture. It’s a feature that allows the net changes made to data to be returned from a query easily. Change Tracking will let you know that a given row has changed since your last query. But, it fails to identify:

• The different value changes that have been made to the row, or
• The number of times it was changed.

Change Data Capture, as the name suggests, tracks when the complete data has changed. You can capture an entire table or a subset of columns through CDC.

Feature	Change Data Capture (CDC)	Change Tracking
Data Changes Tracked	Captures full data changes, including previous values	Only tracks that a row has changed, not what changed
Historical Changes	Maintains a history of all changes	Does not track past changes
Scope	Can capture entire tables or specific columns	Provides only net changes since the last query

TL;DR – Change tracking is a subset of change data capture.

Change Data Capture in ETL

In the era of big data, data has become vital for Business Intelligence and enterprise data analytics. Data plays an important role in all business operations. For your data to be valuable, you need a way to gather data from an unlimited number of sources, organize it, and centralize it in a single repository. This means you need ETL or data integration processes. To understand the difference between the two, refer to this resource.

Traditionally, data warehouses do not contain up-to-date data. Up-to-date data often resides in operational systems and is then loaded into the data warehouse at a set frequency.

Discover the top CDC tools to efficiently capture and replicate real-time data changes across systems, ensuring seamless data integration and synchronization. Learn more at CDC Tools.

Change Data Capture in a Distributed System

Although the CDC captures changes in a database, it still requires a messaging service to send those change notifications to the applicable systems. One method to accomplish this is by considering the changes as individual events and sending them asynchronously. The technical term for such an architecture would be event-driven architecture (EDA).

Apache Kafka is the ideal way to provide communication between the database and data consumers that require a replayable, high-volume consumption pattern. Kafka has been designed to handle data streams from many sources and deliver the data to many destinations, with high scalability and throughput.

Change data capture would ensure that the events transmitted by Kafka are aligned with the changes in the original database or source system. Events are segregated from consumers in Kafka, enabling asynchronous communication and more robust changes in delivery.

Business Benefits of Change Data Capture

CDC Generates More Savings

90% of the world’s data was generated in just the past 2 years. The infrastructure, most of which was built a few decades ago, lacks the bandwidth to move humongous volumes of data instantaneously. This poses a serious problem for businesses that want to undertake projects with high volumes of data, for instance, database migrations. These data transfers can severely overwhelm network traffic. This might lead to cloud migrations that are costlier and slower.

As opposed to this approach, the CDC loads the data incrementally. Every time a data point is modified in the source system, it gets updated on the target, needing minimal bandwidth. So, businesses never worry about large data transfers that impact the network bandwidth.

CDC Generates More Revenue

Teams are provided with updated data that enables them to make informed decisions and take actions based on the best available information from the CDC. It plays a crucial role in enhancing the accuracy and speed of the data, ensuring that it is both accurate and faster. With the CDC, you can act on opportunities sooner. It allows you to beat your competitors to deals while tackling a higher volume of opportunities.

CDC Protects Business Assets

CDC plays a pivotal role in protecting data from destruction and deletion. By tracking changes to metadata and data, CDC offers companies that face data loss a chance to rejuvenate affected datasets. Once data is gone, it can’t be regenerated. However, by protecting the data, CDC ensures that companies can recover their integral data to propel business growth.

CDC Gets Rid of Opportunity Costs

Change Data Capture, specifically, the log-based type, never burdens a production database’s CPU. Log-based CDC is modified directly from the database logs and does not add any additional SQL loads to the system. Apart from this, incremental loading ensures that data transfers have minimal impact on performance. In business terms, CDC eliminates the opportunity costs that arise when a business is forced to slow down its vital tech infrastructure.

Change Data Capture Techniques

At a high level, there are several techniques and technologies for handling the CDC processes.

We have provided the Top 4 implementation techniques that you can adopt in your company:

1) Timestamp-Based Technique

This technique depends on a timestamp field in the source to identify and extract the changed data sets.

This example shows a snapshot of the table when a new record is created and a snapshot when the record with ID=101 is updated.

2) Triggers Based Technique

This involves creating database triggers to identify the changes that have occurred in the source system and then capture those changes into the target database.

The trigger functions are stored procedures that are automatically executed when a specific event, such as an INSERT, UPDATE, or DELETE operation occurs on a table. To capture any data changes, one trigger is required for each operation per table. The data changes are stored in a separate table referred to as the event table or shadow table.

The implementation of this technique is specific to the database on which the triggers need to be created.

3) Snapshot-Based Technique

This technique involves creating a complete extract of data from the source table in the target staging area.

Therefore, the most recent snapshot of the source table is compared to the original when loading incremental data again in order to detect any changes.

4) Log-Based Technique

Almost all database management systems have a transaction log file that records all changes and modifications in the database made by each transaction. 

In general, every DML operation such as CREATE, UPDATE, DELETE is captured in a log file in the database, along with the time-stamp or a database-specific unique identifier indicating when each of these operations was incurred.

This log-based technique relies on log information to identify changes and perform CDC operations.

When it comes to choosing the right CDC method, there isn’t clear-cut answer. It all boils down to the requirements for capturing changes and what the data in the target system will be used for..

If the use cases for the target system require data to be up-to-date at all times, then you should implement a push-based CDC solution. If a push-based solution isn’t possible, you can always look to implement pull-based solutions. However, pull-based solutions are dependent on a large number of factors. First, if you can amend the source schema before adding update timestamps or row versions, it should be relatively straightforward by generating some database triggers. The overhead of managing an update timestamp system is significantly less than that of a row-versioning system, so using update timestamps is the preferred approach.

On the other hand, if modifying the source system isn’t possible then the only options at your disposal are: change scanning or leveraging any in-built change log capabilities of the source database.

When it comes to choosing the right CDC method, there isn’t clear-cut answer. It all boils down to the requirements for capturing changes and what the data in your target system will be used for.

Change Data Capture Real-World Use Cases

1) Transactional Analysis

• Fraud Detection
  You want to analyze transactions in a batch manner to determine if credit cards are being used from multiple locations simultaneously.
• Kafka Pipeline
  You would need an analysis done at the transaction level, not an aggregated level.

2) Data Duplication

• Database Mirroring
  A technique commonly used in disaster recovery (DR) and high availability (HA) scenarios is database mirroring. Two or three SQL Server instances are typically involved: one functions as the primary (principal), another as the mirror, and a third instance, which is optional, operates as the witness that helps manage automated failover.
• Database Replication
  Database replication is essentially copying data from one database on one server to a database on another server, allowing all users to share the same level of information without any inconsistency. It can be a one-time operation or an ongoing process.

You can explore the detailed guide on MySQL High Availability (HA) and replication.

At this point, you might be contemplating if it might be better to buy a ready-made tool that manages the CDC infrastructure automatically or to hand-code it for ETL.

Hand coding comes with many challenges:

• Managing, supporting, and reusing code is complex.
• Having many coders onboard results in high maintenance costs.
• Custom-code developers are scarce.

Therefore, the opportunities become endless when you empower yourself and your team with the right data pipeline platform.

Apart from these, here are a few more real-world use cases of Change Data Capture:

• Microservices Integration: You can enable CDC to sync microservices with traditional applications. This allows smooth transfer of data changes from legacy and source systems to microservices-based applications.
• Compliance and Auditing: You must save a history of data changes to follow the strict data compliance requirements. You can also use CDC to save data changes for archiving or auditing requirements.
• Analytics Dashboards: By utilizing CDC to stream data modifications into analytics dashboards, corporate intelligence decision-making can become more immediate and informed
• Cache Invalidation: CDC can also be used to invalidate caches. This ensures that the outdated entries in a cache are removed or replaced to make way for the latest versions.
• Full-text Search: Full-text Search: You can use CDC to maintain an automatic full-text search index aligned with the database.
• CQRS Model Updates: CDC can be used to keep Command Query Responsibility Separation (CQRS) read models aligned with the primary models.

Why Is CDC Essential For Data Warehouses?

• Supporting Real-Time Analytics: Enables real-time insights by transferring changes instantly.
• Keeping Systems in Sync: Maintains consistency between source systems and data warehouses.
• Enabling Cloud Migrations: Efficiently moves data across networks for seamless transitions.
• Optimizing Data Resources: Reduces operational overhead and improves resource efficiency.

CDC transforms data warehouses into dynamic hubs for real-time insights and seamless integration.

Change Data Capture Tools

Here are a few popular CDC tools that you can consider for your workforce:

• Hevo: Hevo Data is a zero-maintenance platform that allows you to perform CDC in near real-time from 150+ data sources to the destination of your choice, including Snowflake, BigQuery, Redshift, and Databricks, without writing a single line of code. You can focus on important things and opportunities while we take care of your data for you. With Hevo as your CDC tool, maintenance is one less thing to worry about.
• Maxwell:  Maxwell reads MySQL binlogs and delivers row-level updates to Kafka and Kinesis systems in JSON format. Maxwell is a solid option worth considering due to its low operating overhead, requiring only MySQL and a destination to write to.
• Debezium: Debezium is an open-source CDC (Change Data Capture) platform built on top of Apache Kafka. It has connectors to pull a change stream from databases like MySQL, PostgreSQL, and MongoDB and send it to Kafka. Kafka Connect is used for change propagation and detection in this scenario. You can either use Debezium as a standalone server or embed it into your application code as a library.

Change Data Capture Best Practices

• Make sure that it supports asynchronous pub/sub style change propagation to consumers.
• The order of changes for change data capture needs to be preserved. This allows the data to be propagated to the target system in the same format.
• Ensure that it supports lightweight message transformations, as the event payload must match the input format of the target systems.

Conclusion

In this blog, we’ve introduced you to Change Data Capture (CDC) and explained the steps to implement it in your company. Moreover, we have also discussed the relationship between the CDC and ETL process, while listing the various use cases of both. Additionally, we have highlighted the limitations that may arise when performing the CDC process manually using the ETL method. Here’s another blog that can help you understand the benefits of CDC.

Learn about slowly changing dimensions and how to manage changes in your data with our in-depth guide on the subject.

Ready to streamline your data integration? Try Hevo Data today and experience seamless CDC with real-time insights and effortless integration.

Sign up for a 14-day free trial and streamline your data migration with Hevo!

You can also have a look at our unbeatable Hevo pricing that will help you choose the right plan for your business needs! 

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