Blog

Discover the latest news and tips from Popsink. You’ll learn about data stream and best practices.

Popsink vs Qlik Replicate for IBM i (AS/400) replication

IBM i is still the backbone of critical operations in many enterprises, but not all Change Data Capture methods are equal. This post compares Popsink’s direct journal API approach with Qlik Replicate’s SQL-based method, highlighting how the choice of capture technique impacts performance, CPU load, and reliability at scale.

Latest articles

Data Movement Strategies: Choosing Between SFTP, ETL, and CDC

Modernization succeeds or fails on how you move data - we compare SFTP, batch ETL/ELT, and CDC across ownership, source load, TCO, and risk. See why the 'safe' options quietly create bottlenecks and costs, and what actually scales without punishing production systems.

Using Apache NiFi for Change Data Capture (CDC)

Apache NiFi is a powerful data flow engine, but not a CDC solution. In this post, we explore how NiFi handles CDC today, why polling falls short, and how pairing NiFi with Popsink’s log-based CDC unlocks real-time, low-latency pipelines. With Popsink’s Kafka-compatible API, NiFi can seamlessly consume change events and deliver them anywhere in your stack.

Popsink vs Qlik Replicate for IBM i (AS/400) replication

IBM i is still the backbone of critical operations in many enterprises, but not all Change Data Capture methods are equal. This post compares Popsink’s direct journal API approach with Qlik Replicate’s SQL-based method, highlighting how the choice of capture technique impacts performance, CPU load, and reliability at scale.

Popsink vs Fivetran HVR for IBM i (AS/400) replication

In modern enterprises, IBM i remains the backbone for mission-critical operations - finance, insurance, logistics, manufacturing transactions. These systems are incredibly stable, but the value of their data is unlocked when it can flow continuously and reliably into analytics, AI models, operational dashboards, and downstream systems.

Why AWS DMS fails at Replication and Change Data Capture

In theory, AWS Database Migration Service promises to deliver seamless, real-time replication from on-premise to AWS. In practice, data engineers have learnt the hard way that DMS falls short. If you’ve struggled with interrupted pipelines, incomplete/duplicate data, or opaque operational errors, you’re not alone.‍

Popsink Source Connector for Snowflake

We’re excited to share a new connector in Popsink: Source Connector for Snowflake. This new connector enables Change Data Capture (CDC) directly from Snowflake as a data source. For data teams, this means automated, continuous and consistent data replication from Snowflake.

Managing Change Data Capture (CDC) in Snowflake

Change Data Capture (CDC) empowers Snowflake by enabling highly efficient, scalable, and cost-effective data pipelines tailored to its architecture. It enhances Snowflake's performance and resource utilization, minimizing compute overhead and storage costs.

Preparing for Change Data Capture with DB2 on IBM i (AS/400)

Learn how to implement Change Data Capture (CDC) on DB2 for IBM i (AS/400) with minimal impact on source systems. This blog covers setup, how to estimate usage, and practical scripts for managing journal lifecycle for you to be able to replicate data safely and reliably.