STRIIM FOR SNOWFLAKE
Cloud Data Platform
Real-Time ETL with Stream Processing
Continuously Load Real-Time Data to Snowflake from a Wide Variety of Sources
The Striim platform, running in the AWS and Azure Cloud, integrates real-time data to Snowflake with low-impact from data warehouses (including Oracle Exadata, Teradata, Amazon Redshift), databases (including Oracle, SQL Server, HPE NonStop, MongoDB, Amazon RDS, and MySQL), log files from security devices and other systems, sensors, messaging systems, and Hadoop solutions with in-flight transformations.
Snowflake Computing customers can further accelerate their time-to-insight with Striim delivering the data in a consumable format for advanced analytics applications. By streaming enterprise data to Snowflake with built-in scalability, security, and reliability, Striim simplifies adopting a modern, cloud data warehouse in the cloud for time-sensitive, operational decision making.
WHY STRIIM FOR SNOWFLAKE
With real-time data synchronization capabilities, Striim enables businesses to have a phased migration to Snowflake from existing on-prem or cloud-based data warehouses.
For continuous data loading, Striim ingests real-time data from major enterprise databases using low-impact change data capture (CDC) to avoid any modification or performance impact on source production systems.
Via in-line transformations, including denormalization, filtering, enrichment and masking, before delivering to Snowflake with sub-second latency, Striim offers a simplified and scalable data architecture. In-flight data processing with Striim:
- Minimizes ETL workload
- Allows effective handling of large data volumes via filtering
- Reduces the architecture complexity, enabling end-to-end recoverability and full resiliency
- Facilitates compliance with privacy-related regulations via data masking
MODERN CLOUD DATA WAREHOUSE
- Build an operational data warehouse in the cloud by continuously moving enterprise data from on-prem and cloud-based databases, and other data stores
- Collect data from wide range of sources and deliver pre-processed data to Snowflake with sub-second latency to gain more operational value
- Set up real-time data pipelines to support time-sensitive analytics applications
- Avoid batch ETL related inefficiencies using non-intrusive CDC combined with in-flight data processing
Easily ingest critical enterprise data to Snowflake in real time using low-impact change data capture (CDC)
Pre-process your enterprise data through filter, transform, enrich and join operators in real time as it is being delivered into Snowflake
Migrate legacy data warehouses to Snowflake with phased transition without downtime or data loss
