IOMETE and Firebolt

Augment Firebolt with IOMETE to reduce cost and improve performance.
Use Case

You are experiencing one or more of the following challenges

You are using Firebolt as a data warehouse, and it is becoming increasingly costly.
You want to expand your data platform capabilities and support data lake workloads using Apache Spark, as well as a notebook service for data preparation and machine learning training.
You want to keep sensitive data in your own cloud account to be compliant with regulations and privacy data ownership considerations and only send non-sensitive data to Firebolt.
You want to avoid vendor lock-in. Having all your data on proprietary Firebolt may limit your flexibility and potentially increase costs in the long run.

IOMETE is a modern cloud-prem lakehouse that provides a scalable, cost-effective, and secure data lakehouse and data warehouse solution in your own cloud environment

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.
IOMETE is a fully-managed service and completely runs in the customer's cloud environment.
IOMETE has a transparent flat-fee cost model. You can optimize costs by using AWS's reserved instances, discounts, spot instances, and other cloud optimizations in AWS to reduce your costs by 50% or more. IOMETE will handle the heavy processing while using Firebolt as a data warehouse for the last-mile analytics.
Improve your data platform capabilities by using the Apache Spark & Iceberg-based IOMETE platform. IOMETE 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.

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

Top scenario: You are 100% exposed to Firebolt’s consumption-based revenue model and may spend heavily on expensive compute credits. Middle scenario: Let IOMETE do the heavy lifting in the back. Bottom scenario: let IOMETE do all the work...

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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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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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Learn more in a 30 min demo discovery call.