GHANGE BATA CAPTURE
Move only what changed, the moment it changes
Sparkflows CDC captures inserts, updates and deletes from databases, Saas apps and document stores, and keeps Snowflake, Databricks and Redshift in sync, with no full relcads.
THE BASICS
What is Change Data Capture?
Change Data Capture (CDC) identifies and captures the changes made to data in a database every insert, update and delete. Instead of re-copying everything, it passes on just those changes, 80 systems stay synchronized and teams act on the most current information.
Inserts
Anew record appears at the source and is camied to the target.
New order placed > now row in the warehouse
Updates
An existing record modified, and only that change moves downstream
Status set to stripped > one feld updated
Deletes
Join, filter, clean, and reshape data visually — the transformations you'd otherwise hand-write in PySpark.
Order cancelled > now removed Downstream
Why it matters
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Decisions on current data
Tinsely decisions depend on systems that reflect the latest Cats
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No full reloads
Only records new or modified since the last extraction nove improving performance
Traceable and auditable
Extraction history and metadats are stored automatically
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Beyond databases
Also works with SaaS and API-besed sources like Salesforce, NetSuibe and Workday
HOW IT WORKS
From source change to Bl-ready table
The Sparkflows CDC Engine runs on Apache Spark for fast, parallel processing, in real time or in batches.
TWO WAYS TO CAPTURE
Stream it, or query for it
Both methods apply the captured changes to your target system. Choose per source.
STREAMING
Log Based
Sparkflows listens to changes on source tables and applies them to the target as they happen
Table change captured applied to target
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QUERY
Query Based
Sparkflows queries the source table for the latest updates, then applies those changes to the target.
Query Latest updates applied to target
KEY CAPABILITIES
Everything a CDC pipeline needs, built in
Automated Change Detection
Detects charges on its own cutting manual work and errors
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Schema Evolution
Handles changing source structures without Interrupting Operations
In-Pipeline Transformation
Enrich and transform date before it reaches downstream Systems
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Event-Driven
Changes can trigger alerts downstream workflows or
automated transformation
Change Tracking
See what changed, when and its impact, for easier auditing.
Real-Time and Batch
Near real-time capture or batch COC on one Spank engine.
Keep every system current, without the reloads.
Capture what changed, transform it on the way and deliver it where your teams work