Latest Updates on Data Replication and Best Practices

Top 8 real-time CDC data replication tools in 2026 include Popsink for hardened replication, Fivetran for automated ELT, Airbyte's open-source connectors, Qlik Replicate for heterogeneous replication, Informatica for enterprise governance, Debezium on Kafka, and Striim for in-flight processing. Log-based CDC minimizes source impact. Select based on latency, scale, and resources for flawless AI insights and analytics.

Change Data Capture (CDC) captures database inserts, updates, deletes via log-based methods for real-time syncing with zero source impact. Enterprises save $15M yearly from delayed decisions by avoiding batch ETL latency. Implement in 7 steps: enable logs, connect tools, map data, test. Prefer log-based over trigger/query methods; monitor lag, handle schema drift. Delivers fresh data for analytics, AI, fraud detection.

Popsink launches its IBMi-DB2 Connector for Snowflake, enabling industrial and logistics teams to replicate mission-critical AS400 data into Snowflake without downtime, scripts, or changes to production systems. A native, non-intrusive connector designed to modernize legacy data with fast setup, full autonomy, and enterprise-grade reliability.

It's a big step.

Oracle CDC can be implemented in several ways, each with trade-offs in cost, performance, and system impact. XStream offers low latency with GoldenGate licensing, LogMiner provides license-free options with higher overhead, and direct redo log parsing delivers high-throughput replication with minimal production load while the connector ensures version compatibility.

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.

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.

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.

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.

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.‍

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.

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.

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.

CDC is a method to increase the efficiency of data movements by capturing changes of data instead of periodic snapshots. The benefits: greater speed, less processing, less system resources.

Discover the comprehensive and insightful Ultimate Guide to Change Data Capture, packed with expert insights, practical tips, and valuable resources to help you understand and implement this crucial data integration technique effectively.

Looking for some solid data principles and tenets to guide your organization’s data decisions and the tradeoffs they have to make? Here they are, copy at will!

Many DBAs resist enabling Supplemental Logs due to performance concerns. This article explains the benefits and addresses common objections.

Simon is the architect behind the Popsink user experience and product interface. Let's learn a bit from him.

After over a year of intensive development and feedback, we are proud to announce that Popsink is now publicly available 🥳.

Earlier this month Romain joined the Popsink team as Software Developer!

Earlier today, one of my favourite follows on LinkedIn raised an awesome question. I was going to just answer there but couldn't stop typing so here's a blog post instead.

Popsink bridges the gap between insights and actions by providing data teams with the missing piece in their Modern Data Stack.

With all those kick-ass tools in place, why then, has it been so hard to turn daily data into actual services? Let’s drill into that.

Let's have a current state of data platforms and services, including architectures, integrations, transformations, stores, and dashboards.

Earlier this month Mikael joined the Popsink team as Software Developer but not only... he is also the very first permanent hire in the team! Welcome Mikael!

We're proud to announce that Popsink has raised a pre-Seed round of EUR 880k with Seedcamp, XAnge, Kima Ventures and business angels to accelerate its development.