IOMETE and Snowflake

Step 1: Combine Snowflake and IOMETE to reduce your total cloud / data bill by 50%.
Step 2: Ditch Snowflake all together and save even more.

(On Snowflake and want to calculate how much you can save? Use our savings calculator.)

Use Case

Challenge

You may identify with one or more of the following situations

You want to avoid vendor lock-in. Having all your data on proprietary Snowflake may limit your flexibility and potentially increase costs in the long run.

You are using Snowflake and it is becoming increasingly costly. As data tends to grow over time, Snowflake’s consumption-based billing gets continuously more expensive for you, while the functionality they deliver does not necessarily change.

You want to keep sensitive data in your own cloud account to be compliant with regulations and/or privacy data ownership considerations and only send non-sensitive data to Snowflake.

You are looking for a modern and open lakehouse solution that is flexible and open rather than a proprietary solution.

Solution

IOMETE is a modern cloud-prem lakehouse that provides a scalable, cost-effective, and secure data lake and data warehouse solution

IOMETE is a fully-managed service and completely runs in the customer's cloud environment. In IOMETE's architecture the control plane and data plane are separated with the latter running in the customer's trust perimeter. This means that the customer owns their data 100% at all times.

IOMETE has a transparent flat-fee cost model that is usage-independent, a stark contrast with Snowflake's usage-based billing model which gets expensive quickly. By allocating more jobs to IOMETE you can save on Snowflake usage and cut your bill in half. This is especially true for large organizations.

Improve your data platform capabilities by using the Apache Iceberg and Spark based IOMETE platform. This platform covers data science use cases, with Apache Spark jobs and a notebook service available. Moreover, the built-in query federation allows you to query operational data sources directly without building any ingestion data pipeline.

The IOMETE lakehouse combines the strengths of data lakes and data warehouses, providing the scalability and flexibility of a data lake with the structure of a data warehouse.

Combine Snowflake’s and IOMETE strengths to cut costs by > 50% while improving performance

You might be using Snowflake for all compute jobs (top scenario in visual below). This can get expensive quickly due to Snowflake's usage-based revenue model: the more you use, the more you pay.

IOMETE's lakehouse architecture makes it perfectly suited for the heavy lifting in the bronze and silver tiers (middle scenario). You can cut your total cloud / data bill in half by transferring compute jobs from Snowflake to IOMETE.

Due to IOMETE's flat fee you prevent paying for the mark-up that Snowflake puts on AWS/Azure/GC cloud instances that it runs under the hood...

Over time you will transition to the bottom scenario and realize that you don't need Snowflake as IOMETE is better, faster at half the price.

(Hey Instacart, spending $100m on Snowflake is a bit rich...that's a lot of carrots and potatoes. We should talk... pretty sure we can save you 50%...).

If you are on Snowflake and want to know how much you can save check out our savings calculator.

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Resources

Guides

How to install IOMETE

Easily install IOMETE on AWS using Terraform and enjoy the benefits of a cloud lakehouse platform.

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Querying Files in AWS S3

Effortlessly run analytics over the managed Lakehouse and any external files (JSON, CSV, ORC, Parquet) stored in the AWS S3 bucket.

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Getting Started with Spark Jobs

This guide aims to help you get familiar with getting started with writing your first Spark Job and deploying in the IOMETE platform.

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Docs

Virtual lakehouses

A virtual lakehouse is a cluster of compute resources that provide the required resources, such as CPU, and memory, to perform the querying processing.

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Iceberg tables and Spark

IOMETE features Apache Iceberg as its table format and uses Apache Spark as its compute engine.

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The SQL editor

This guide aims to help you get familiar with getting startedThe SQL Editor is where you run queries on your dataset and get results.with writing your first Spark Job and deploying in the IOMETE platform.

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