Delivering huge volumes of data to Snowflake for Predictive ML Models

BryteFlow provides automated real-time delivery and syncing of data including deltas from a multi-tenant SQL Server database to Snowflake on GCP for Optimove, a leading CRM Marketing Hub.

Delivering huge volumes of data to Snowflake for Predictive ML Models

BryteFlow provides automated real-time delivery and syncing of data including deltas from a multi-tenant SQL Server database to Snowflake on GCP for Optimove, a leading CRM Marketing Hub.

“BryteFlow has made major contributions to our transition from on-prem SQL Server to GCP-based Data Warehouse. BryteFlow technical people are very helpful and full of willingness to assist in implementing their solution, as well as address concerns or issues. Overall, we replaced another vendor with BryteFlow, who are superior – both in their technical solution and the level of support.”

Ohad Sheory,

Chief Architect Optimove
INDUSTRY: COMPUTING

Optimove

Key Benefits
  • Real-time delivery and syncing of data including deltas from SQL Server to Snowflake on GCP
  • Completely automated no-code solution uses Change Tracking to deliver incremental data
  • Multi-tenancy was enabled with variable scaling as per tenant size
  • Managing deployment and monitoring using APIs
Client

Optimove is a leading CRM Marketing Hub and empowers brands to create and manage large-scale, CRM journeys, mapped by AI. Optimove autonomously surfaces valuable customer engagement opportunities, orchestrates self-optimizing customer journeys, and accurately measures the incremental impact of all marketing interactions. With Optimove, leading brands such as Papa John’s Pizza, Dollar Shave Club, and JD Sports can scale from communicating with tens of segments to orchestrating hundreds of segments, resulting in multi-channel customer interaction, customer loyalty, retention, and lifetime value maximization.

Challenge

Optimove was transitioning from on-prem SQL Server to a GCP-based Data Warehouse in order to migrate workloads to a Cloud environment. Optimove needed to get their clients’ data from a multi-tenant SQL Server database to Snowflake on GCP (Google Cloud Platform) in real-time to drive the AI-driven journey orchestration capabilities for their customers. This was not easy as petabytes of data from historical, behavioral, and real-time data sources had to be combined with Optimove’s out-of-the-box machine learning predictive models to create attributes, segments, campaigns, journeys and generate reports via a no-code interface in minutes. The replication solution had to be cloud-based and cost-effective, enabling deployment and monitoring through APIs.

The challenge lay not only in replicating high volumes of data in real-time but also addressing the issues of multi-tenant databases like security (unintentional data access, hacking, data loss) and possibility of lowered performance (response time in accessing data could vary for different clients).

Solution

BryteFlow provided Optimove a massively scalable, secure, no-code solution that loaded high volumes of data in near real-time, from the multi-tenant SQL Server database to Snowflake on GCP using Change Tracking. It merges and delivers complete, ready-for-analytics data that can be queried immediately. The BryteFlow solution can sync tables in real-time and this handles schema changes as well as the syncing of deltas with data better. The solution enables data from the multi-tenant SQL database to be defined and tagged with the Tenant Identifier or Database ID from where the record originated, so using this data becomes simple. The solution took just days to deploy and is at least 90% faster to deploy than comparable solutions. How BryteFlow Works

Result

Optimove can now access huge volumes of accurate data in real-time for their machine learning predictive models, to forecast the customer journeys for their clients, enabling timely, multi-channel customer engagement and interaction, while increasing customer loyalty.

BryteFlow being a completely automated tool, has also done away with potential issues such as the significant coding that might be needed to handle the number of databases, number of tables in each database and the issue of schema evolution not being in sync across the various tenants. This has led to savings of effort and time.

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