STRIIM

Partnership

Indium has successfully taken its digital services to the next level through a strategic partnership with Striim. This partnership has accelerated data-driven decision making for our joint enterprise customers who want to leverage the capabilities from a real-time Big Data Analytics platform. Indium has been developing innovative data pipeline solutions on continuous ingestion of real-time data from various databases, cloud applications, etc., thus ensuring continued uninterrupted service.

Who is STRIIM?

Striim serves a vast number of customers across the globe with a platform that offers services in data integration, change data capture, real-time cloud integration, log correlation, edge processing, and streaming analytics. As a product, Striim is an easy to use replication tool that reduces complexity at work, where a user with minimal programming knowledge can configure it.

Why Striim?

Striim offers a solution for gathering, clustering, analysing, delivering, filtering and transferring Big Data from source to target. Without any complex and expensive architectural setup and time-consuming remote calls, a huge volume of data can be streamed using its data cache in a memory grid. Striim also provides a holistic security model that enhances data protection from the source to user dashboard. By offering a highly scalable, reliable, practical and secure end-to-end architecture, Striim facilitates the integration with a vast majority of relational databases seamlessly.

Striim Architecture

Collaboration with STRIIM

By partnering with Striim, Indium has been able to leverage its robust work environment and culture to support customers in real-time Big Data processing, using Striim. Our exceptionally talented employees provide quality services in the areas of Big Data, IoT, Data Visualization and end-to-end data platform management. The Striim team takes a proactive approach in solving technical challenges across all technologies and platforms, like Cloud (Azure, GCP, AWS etc.), Databases (Oracle, SQL Server, NoSQL etc.), and so on.

Benefits

With Striim, customers acquire a wide range of benefits that enriches their business with real-time data movement and in-stream transformation. Additionally, Striim records any data changes that occurs on the logs instantly without the necessities of a page refresh, and before the relational databases finishes its processing. This process helps the relational database vendors and third parties in enhancing their database management system. The other advantages of Striim are:

  • Multiple heterogeneous database support
  • Data transformation within the pipeline during replication
  • User-friendly interface
  • Provision for Data Definition Language (DDL) and Data Manipulation Language (DML) replication
  • Standalone cluster (single machine) and distributed cluster (multiple machines) support
  • Peer to peer replication support, whereby data will be stored in the metadata repository and in case of any failover data can be retrieved.
  • Innovative licensing strategy

Use case

Indium’s expertise in Striim has grown over time with multiple successful customer implementations. Data streaming, initial data load, Change Data Capture, secured data transfer, generation of real-time reports, etc. are some of the classic use cases.

  • Our support aided a customer in leveraging Striim to move their data from Oracle database to Postgres in Google Cloud Platform. The architecture implemented included effective data monitoring and customised visualization of the streaming data. Additionally, alerts were created when multiple data pipelines were being accessed simultaneously.
  • Indium helped a client with the implementation of Striim in their messaging queue platform. With this setup, the client could stream data in their Kafka queue and write data into the Kafka logs using Kafka writer, which could then be consumed by multiple downstream system and application.
  • Our team aided in supporting a customer through an open processor (custom scripts) in Striim to provide a data audit feature for every transaction hitting the database. The changes will be updated in a log database, enabling tracking of the data change, for example insert, delete and update. Additionally, our team created another open processor in moving the current system time forward by 7 hours, before replicating the timestamp column in the database.

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