Archives: FAQs

A: Unlike WebSockets, Kafka is a distributed, fault-tolerant message platform that scales horizontally and handles downtime gracefully—ensuring minimal data loss even when services fail.

Streams provide things like: METADATA$ACTION (whether a change was an insert, delete, etc.) METADATA$ROWID (to identify rows across changes) METADATA$ISUPDATE flag or similar to check if the changed row is an update vs just a change in value. This metadata helps in merging efficiently into downstream tables and applying logic depending on type of change.

 A: Since only incremental changes are processed (via streams + merges), you avoid re-processing the whole table on every run. That reduces compute and data transfer. Also, less storage/io overhead for frequent full loads. (Implicitly discussed in the blog through the stream + merge pattern.)

 A: The blog outlines: Create a source (OLTP) table Use Python (and libraries like snowflake-connector-python, sqlalchemy, pandas) to load data into Snowflake Create a Snowflake Stream object on that table to capture changes (captures metadata such as METADATA$ACTION, METADATA$ROWID, etc.) Use a SQL MERGE into a final target table to apply inserts/updates/deletes based on captured […]

 A: Using CDC with Snowflake enables analytics or downstream systems to stay up to date with minimal lag. Snowflake streams specifically let you capture table changes (with metadata like which rows changed, the type of change, etc.) and then allow efficient querying or merging of just the changed data rather than full table scans.

 A: CDC is a method to detect and capture changes (inserts, updates, deletes) made in a source database and propagate them to another system, often in near-real time.

 A: This is tailored for backend developers, architects, and microservices engineers keen on implementing event-driven workflows or scaling real-time systems efficiently.

A:  It uses a taxi app example—where real-time updates from drivers and riders need to be synchronized across clients with low latency—demonstrating Kafka’s ability to serve timely, reliable data streams.

 A: The blog introduces Kafka’s unified, high-throughput streaming architecture, its low-latency data delivery, and its suitability for unifying data flow across services.

 A: Unlike WebSockets, Kafka is a distributed, fault-tolerant message platform that scales horizontally and handles downtime gracefully—ensuring minimal data loss even when services fail.