In a meeting earlier today, discussing the clients IBM i data lake connection, someone asked me “Why have I heard we use DTSHARE but some people call it CDC and others call it DISUITE? What is the difference?” I was proud of myself, because I was able to bite my lip and stop my immediate retort of “its probably the same people that still call the IBM i system an AS400!!” and instead gave a reasonably cogent answer. Here is the same answer with a little more granular information, courtesy of my friend Mr Grok.

Me

To understand the differences between DTSHARE, Connect CDC, and DISUITE (Precisely Data Integration Suite), we need to clarify their roles and relationships within the context of Precisely’s data integration offerings.

Connect CDC, part of the Precisely Data Integration Suite (formerly known as Precisely Connect), is a change data capture (CDC) solution designed to capture and replicate data changes from various source systems, including IBM i, in real time or near real time. It enables seamless integration of data from large storage systems like IBM i Power Systems (formerly AS/400 or iSeries) into alternate data platforms, such as cloud environments, data lakes, or data warehouses, without disrupting existing operations.

Key Differences

Put Simply — and this isn’t strictly accurate, but it works for me 😉

• DTSHARE is the old version of the software that mirrors a host database to a data warehouse system
• CONNECT CDC is the upgraded version of the old DTSHARE product
• DISUITE is the software suite, and CDC is a part of this suite.

So, what does Connect CDC do?

Connect CDC is a component of the Precisely Data Integration Suite that focuses on identifying and capturing changes (inserts, updates, deletes) in source databases as they occur. It then delivers these changes to target systems for downstream processing, such as analytics, reporting, or application synchronization. Unlike traditional batch processing, which can introduce latency, Connect CDC ensures low-latency data movement, making it ideal for real-time use cases. It supports a wide range of source and target systems, including IBM i, mainframes, relational databases, and cloud platforms like Snowflake, Google Cloud, or Databricks.

Key features of Connect CDC include:

• Real-time data replication: Streams changes instantly to keep target systems in sync with the source.
• Low-impact capture: Minimizes performance overhead on the source system (e.g., IBM i) by efficiently reading change logs or journals.
• Flexible deployment: Supports on-premises, cloud, or hybrid environments with a “design once, deploy anywhere” approach.
• Broad compatibility: Integrates with legacy systems like IBM i as well as modern platforms, ensuring enterprise-wide data accessibility.

How Does Connect CDC Work with IBM i?

IBM i systems, built on the Db2 for i database, are widely used in industries like finance, manufacturing, and retail for mission-critical applications. These systems store transactional data in journals, which are logs that record all database changes. Connect CDC leverages these journals to enable efficient, real-time data integration with minimal impact on the IBM i environment.

Here’s how it works with IBM i:

1. Change Detection:
   • Connect CDC monitors the IBM i journals, which are a native feature of the Db2 for i database. Journals capture all database transactions (inserts, updates, deletes) as they happen.
   • The CDC engine reads these journal entries to identify changes without requiring direct queries to the database, reducing system load.
2. Data Extraction:
   • Once changes are detected, Connect CDC extracts the relevant data from the journal entries. This process is lightweight and optimized to handle the unique structure of IBM i data, including its support for complex, denormalized tables or multi-member files if needed.
3. Transformation and Mapping:
   • Connect CDC allows users to transform and map the extracted data to fit the schema or requirements of the target system. This step ensures compatibility between the IBM i source and modern targets, such as cloud data warehouses or streaming platforms like Apache Kafka.
4. Real-Time Delivery:
   • The captured changes are streamed to the target system in real time or near real time. For example, data from an IBM i ERP system could be sent to a cloud-based analytics platform or an e-commerce application via Kafka, ensuring downstream systems always have the latest information.
5. Integration with Modern Ecosystems:
   • Connect CDC supports integration with a variety of targets commonly used with IBM i data, such as Snowflake, Databricks, Confluent Kafka, or Microsoft Azure. This enables IBM i shops to participate in modern data architectures like data lakes or event-driven systems.

Benefits for IBM i Users

• Modernization: Unlocks IBM i data for use in cloud, analytics, or AI initiatives without requiring a full system migration.
• Minimal Overhead: By leveraging journals, Connect CDC avoids resource-intensive polling or querying, preserving IBM i performance.
• Scalability: Handles growing data volumes and new use cases by easily adding sources or targets without redevelopment.
• Security: Maintains secure data transfers, critical for industries relying on IBM i for sensitive transactional data.

In summary, Connect CDC acts as a bridge between the robust, legacy IBM i environment and contemporary data ecosystems. It uses IBM i’s journaling system to capture changes efficiently and streams them to target platforms, enabling real-time insights and integration while keeping the source system stable and secure. This makes it a powerful tool for organizations aiming to extend the value of their IBM i investments into modern data-driven strategies.