Companies must have the right data at the right time to compete in a 24/7 global economy.
But many teams still rely on delayed batch processing to sync databases. Batch processing does not sync data in real-time, and this is a serious problem in fast-moving markets. A recent study found that 75% of businesses still rely on this method.
But right now, across industries, a big shift is underway. Many businesses are starting to use change data capture (CDC) to sync databases more efficiently.
Change data capture empowers businesses to move at the speed of their data. Read on for an overview of what CDC is, and what it can do for your data operation.
Change Data Capture (CDC): What to Know, How it Works
Change data capture tracks changes in a source dataset and automatically transfers those changes to a target dataset.
Changes are synced instantly or near-instantly. In practice, CDC is often used to replicate data between databases in real-time. CDC instantly and automatically syncs databases as soon as the source data changes. Essentially, CDC eradicates the siloization of data.
Despite the introduction of CDC, most teams still use batch processing to sync data. With batch processing:
- data is not synced right away
- databases slow production to allocate resources for syncing
- data replication only occurs during specified “batch windows”
On the other hand, change data capture offers a new path forward. On a core level, change data capture:
- constantly tracks changes in a source database
- immediately updates the target database
- uses stream processing to ensure instant changes
With CDC, data sources include operational databases, applications, ERP mainframes, and other systems that record transactions or business occurrences.
Targets include data lakes and data warehouses, including cloud-based platforms such as Google BigQuery, Snowflake, Amazon Redshift, and Microsoft Azure.
Once the data is replicated on the target database, teams can perform data analysis without taxing the production database.
In today’s 24/7 marketplace, this kind of setup is becoming closer to mandatory, as businesses cannot afford to slow production for any amount of time. Different technologies power change data capture offerings in today’s marketplace. These technologies include:
- Timestamps – Tracks “LAST_UPDATED” and “DATE_MODIFIED” columns. This method only retrieves changed rows, and requires significant CPU resources to scan all the tables.
- Table Differencing – Executes a diff to compare source and target tables. This will only load the data that differs. This method is more comprehensive than timestamps, but still places a big burden on the CPU.
- Triggers – Triggers are set off before or after commands that indicate a change. This produces a change log. With this method, each table in the source database requires a trigger, straining the system.
- Log-Based – Database logs are constantly scanned to detect changes. The changes are captured without adding additional SQL loads to the system. This removes significant stress on the CPU.
Change data capture enables teams to replicate data instantly and incrementally. CDC records data changes piece-by-piece, instead of relying on massive, all-at-once transfers.
This allows teams to stop treating data migrations as big “projects,” but rather as a byproduct of change data capture.
With CDC, data is always up to date. The source database and target database are continuously synced. Bulk selecting is a thing of the past.
Only the modified data is synced with the cloud DWH. All other data remains static. This saves a tremendous amount of time, resources, and funding.
Download our new eBook, The Business Case for Change Data Capture (CDC), to learn why implementing CDC is the best option for your business.