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MagicOrange Lakehouse Architecture

MagicOrange Lakehouse Architecture

Azure Databricks + MagicOrange

is powered by — Unified Cloud Analytics and AI platform

: Bhushan Tambatkar

is the market’s most advanced IT Financial Management platform. Our proprietary technology enables CIOs & divisional leaders to discover and align their costs with the business and increase visibility of complex cost combos.

is Cloud first multi-tenant SaaS offering, on . Since its inception MagicOrange has been using Azure’s native SaaS offerings to construct and scale our MagicOrange Prism Platform.

Once we first began implementing an information platform for MagicOrange back in 2015, we weren’t anticipating huge data volumes. Existing Azure service offerings like Azure SQL DB, App Services, Storage Accounts, Power BI, Evaluation Services were greater than sufficient to run and grow our platform, considering that every one of those services offered scalability. But after a number of years after we began to see significant increases in data volumes with more customers onboarding to the platform, we began to search for scalable, durable and value effective solutions in Azure Cloud to construct out our MagicOrange Data and Analytics Platform.

We went through an exercise of evaluating which tools we wanted to construct our Data and Analytics Platform. We quickly realized that there are some tools which were good at ETL, others specialized in warehousing, and a few a greater fit for Analytics. Overall, if we selected this path, stitching together various tools and technologies, we could be spending more cash on the assorted services and there could be management overhead.

After evaluating various tools and platforms, corresponding to Snowflake, we selected to go together with Azure Databricks because it is more cost effective, there’s less management overhead, and it meets all of our requirements for a next gen Cloud Data and Analytics Platform.

Here is the MagicOrange architecture and the important thing areas we’ve found helpful as we scale the business.


Thanks for reading, and stay tuned for more..

References: lakehouse, databricks-security , Delta-Lake, auto-loader , copy-into, Delta-Live Table pipelines, Workflows, databricks-data-science, databricks-ml, databricks-automl, unity-catalog, databricks-sql, delta-sharing



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