With modern data increasing dramatically, so is the need to gain insights from data in real time.
Businesses need solutions to keep their database adaptable to real-time requirements, where change data capture comes into play. This article will discuss the basics of the CDC and why it is important.
Importance of identifying and capturing changes made in a database
Data is generated not only in high volume but in high velocity as well. This means a large amount of data is now generated at high speed.
Identifying and capturing data change is important for user-facing applications and enterprise reporting tools to make sure all the system-related data are in sync. It will help businesses to make faster and more accurate decisions with real-time data movement.
What is Change Data Capture?
Change Data Capture, CDC is a technology to identify and track data changes in databases and source tables in real-time. In simple terms, CDC records every time it finds any shifts in a database. It helps businesses with faster data integration and analysis using limited resources.
How does it work?
Whenever the source database is changed or updated, all the related resources must also be updated. Change data capture provides solutions to update those resources without issues like dual write continuously.
It is performed by tracking the changes in the source database and then notifying related systems that depend on the data about those changes.
It sends the notifications in the same order as the changes made in the source database. In this way, CDC helps businesses to keep their systems updated and informed of the changes and to react accordingly.
Why is it important?
Identifying and capturing every data change from transactions in the source database and loading them to the target system in real-time help businesses keep their systems related to the data in sync. It helps in reliable data replication and cloud migrations with zero downtime. Due to its efficiency in moving data across a wide area network, CDC is the perfect solution for modern cloud architectures.
What are ETL and ELT?
ETL (Extract, Transform, Load)
ETL is the process of extracting data from source systems, then transforming the data on a secondary processing server, and then loading the data into a data warehouse system.
In this process, the data flow from source to target, and the transformation engine takes care of all the changes. This process is performed on relational, on-premises, and structured data. ETL is easy to implement comparatively.
ELT (Extract, Load, Transform)
ELT loads the source/raw data directly to the target database without any changes. The target system is responsible for doing the transformation.
ELT processes are performed on cloud-structured and unstructured data sources. This process requires niche skills for its implementation and maintenance.
Change Data Capture in ETL
In the ETL data integration process, data can be extracted using a change data capture solution from the source database, then transformed and delivered to the destination data warehouse. CDC helps to minimize the resources required to perform ETL using log-based or trigger-based methods.
Methods of the CDC
There are different methods to capture changes in data; the following are a few important and most common methods of CDC:
#1. Script-based CDC
The script-based method requires application-level coding to add a field to the existing table to identify whenever the updated data.
This method identifies and retrieves only the rows that have been modified since the last extraction. This method does not need external tools and can be built with native application logic. Script-based CDC adds additional overhead to the database.
#2. Trigger-based CDC
Trigger-based CDC captures insert, update, and delete operations performed on the tables or databases, generating a trigger that catches the data manipulation (DML) statement.
This method requires more work as the database should be able to create triggers, and the changes should be written in another table. All this work requires manual processes and can sometimes become costly to implement and manage.
#3. Log-based CDC
With this method, the CDC tracks and identifies the transaction logs of a database. This method captures the list of data changes in the correct order of their application. Implementation of log-based CDC requires technical effort to push transactions into DML statements.
The DML statements then need to be written into the target system. This method generates a lot of metadata as compared to other methods. This method also offers a solution to run without being installed on the database server, making it run at total capacity without any extra overhead.
How does change data capture benefit businesses?
Following are some reasons why your business needs change data capture (CDC) solutions:
- It allows businesses to transfer data among various systems quickly and efficiently, resulting in timely reporting and improved business intelligence.
- It helps mid-large organizations with multiple database systems to complete real-time data loading into the data warehouse seamlessly.
- It helps businesses push data to multiple lines of business, minimizing disruptions to production workloads.
- With CDC, businesses can draw data from multiple sources and update their master data management system continuously.
- CDC helps organizations to keep their data safe and updated.
- It provides freedom to choose and deploy applications without considering their database compatibility.
- Change data capture can reduce stress on the operational database by transferring heavy user traffic to a secondary database.
- Businesses can also use CDC as their backup plan to maintain a standup copy of their data in case of disaster.
#1. Change Data Capture
This guide will help you understand Change Data Capture, uncover its challenges and generate better solutions to solve those. This self-assessment will help you ask the right questions to use the change data capture technology.
|Change data capture Third Edition||$81.40||Buy on Amazon|
You will be introduced to all the tools required for the self-assessment. The change data capture guide features new and updated case-based questions to help you identify areas where you can improve change data capture in your business.
#2. Change Data Capture A Complete Guide
This change data capture self-assessment will help you become an expert in identifying and solving any CDC challenge. It will help you learn how to reduce the effort in CDC methods to get problems solved.
|Change Data Capture A Complete Guide – 2020 Edition||$81.44||Buy on Amazon|
This guide covers all the change data capture essentials and helps you clarify the required processes and activities to achieve the CDC outcomes.
#3. ETL Framework for Data Warehouse Environments
This Udemy course will help you implement the ETL framework with a high-level and practical approach. It includes complete guidelines, standards, and a checklist to design and implement ETM solutions which can be reused with various data loading strategies, error/exception handling, control handling, and audit balance.
The course provides ETL design principles and solutions based on Oracle 11g and Informatica 10x, which can be implemented in any ETL tool.
Businesses need CDC solutions to increase data reliability and accuracy. This blog introduced you to CDC, why it is important for businesses, and its various methods. If you want to implement this technology in your business, make sure you go through the resources mentioned in the article to help you understand it on a deeper level.
You may also explore some best ETL tools for SMBs.