Our main use case for Striim is real-time data integration and change data capture, which we used to pull live transaction logs directly from IBM DB2 and stream them seamlessly into our modern cloud data warehouses. A specific example of how we used Striim in our project is when we were migrating data to new and modern cloud data warehouses from IBM DB2 where we used the CDC feature of Striim. Real-time data integration and the CDC feature really helped us in that project. From a BA perspective, our business users basically needed instant access to the live production data for analytics, which we provided them by using Striim. As a developer, I would say our bottleneck was that running heavy query workloads directly on the mainframe threatened our SLA transaction windows, so Striim helped in that perspective also.
We were using batch data from an Oracle database, which was causing the Oracle database to slow down. We enabled change data capture and used Striim to read data from Oracle databases and ingest into Snowflake, which was our data warehouse. We were using an ETL model using QlikView, which was taking several days for any software update. With Striim, that has come down to a few minutes. Whenever we do any releases, it would only take a few minutes for Striim to ingest into Snowflake. I would say we went from several days to maybe one to two hours for any release.
I use Striim to perform change data capture from relational databases to non-relational databases in my organization. I implement CDC with Striim by transferring data from Oracle Database to MongoDB in my environment. I have a series of monitored tables where every time a change occurs in the source database, the data is transferred to the target database. The synchronization process is straightforward and efficient for my daily data flows.
Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim...
Our main use case for Striim is real-time data integration and change data capture, which we used to pull live transaction logs directly from IBM DB2 and stream them seamlessly into our modern cloud data warehouses. A specific example of how we used Striim in our project is when we were migrating data to new and modern cloud data warehouses from IBM DB2 where we used the CDC feature of Striim. Real-time data integration and the CDC feature really helped us in that project. From a BA perspective, our business users basically needed instant access to the live production data for analytics, which we provided them by using Striim. As a developer, I would say our bottleneck was that running heavy query workloads directly on the mainframe threatened our SLA transaction windows, so Striim helped in that perspective also.
We were using batch data from an Oracle database, which was causing the Oracle database to slow down. We enabled change data capture and used Striim to read data from Oracle databases and ingest into Snowflake, which was our data warehouse. We were using an ETL model using QlikView, which was taking several days for any software update. With Striim, that has come down to a few minutes. Whenever we do any releases, it would only take a few minutes for Striim to ingest into Snowflake. I would say we went from several days to maybe one to two hours for any release.
I use Striim to perform change data capture from relational databases to non-relational databases in my organization. I implement CDC with Striim by transferring data from Oracle Database to MongoDB in my environment. I have a series of monitored tables where every time a change occurs in the source database, the data is transferred to the target database. The synchronization process is straightforward and efficient for my daily data flows.