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3.10.3 Common Striim use cases

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Striim is a distributed data integration and intelligence platform that can be used to design, deploy, and run data movement and data streaming pipelines. The following are common business applications for the Striim platform. (Note that these examples include just a small fraction of the thousands of source-target combinations Striim supports.)

  • Cloud adoption, including database migration, database replication, and data distribution. Popular data pipelines for this scenario include:

    • RDBMS to RDBMS, including from Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop to homogeneous or heterogeneous databases running on Amazon Web Services, Google Cloud Platform, Microsoft Azure, or Oracle Cloud.

    • RDBMS to data warehouse, including Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop to Google BigQuery, Amazon Redshift, Azure Synapse, or Snowflake.

  • Hybrid cloud data integration, including on-premise to cloud, on-premise to on-premise, cloud to cloud, and cloud to on-premise topologies. Popular data pipelines for this scenario include:

    • RDBMS to RDBMS, including from Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop to homogeneous or heterogeneous databases running on Amazon Web Services, Google Cloud Platform, Microsoft Azure, or Oracle Cloud.

    • RDBMS to queuing systems, including Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop to Kafka or cloud-based messaging systems such as Google PubSub, Azure Event Hubs, or Amazon Kinesis.

    • Queuing systems to RDBMS, including Kafka, Google PubSub, Azure Event Hubs, or Amazon Kinesis to Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop.

    • RDBMS to cloud-based storage systems, including Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop to Amazon S3, Google Cloud Storage, or Azure Data Lake Storage.

    • Cloud-based storage systems to RDBMS, including Amazon S3, Google Cloud Storage, or Azure Data Lake Storage to Oracle Database, SQL Server, MySQL, PostgreSQL, MariaDB, or HP NonStop.

  • Digital transformation, including real-time data distribution, real-time reporting, real-time analytics, stream processing, operational monitoring, and machine learning. Popular use cases for this scenario include:

    • Real-time alerting and notification for CDC workloads (see the discussion of alerts in Running the CDC demo apps).

    • Streaming analytics using data windows (see Sample applications for programmers).

    • Running SQL-based continuous queries on moving data pipelines.

    • Creating real-time dashboards on CDC or Kafka workloads.

3.10.3
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