IOMETE and Amazon EMR
Learn how IOMETE can be a cost-effective, high performance alternative to AWS EMR.
You may identify with one or more of the following situations
You are not a fan of EMR’s archaic and complex configuration management and are looking for a more user-friendly alternative.
You want users with less technical pedigree to perform data science and analytics at scale
You are looking for better security features, such as built-in authentication, authorization, and advanced data access control
You are looking to reduce the cost of EMR license fees, which quickly add up and become expensive as job clusters get larger
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 provides a scalable, cost-effective, secure, and user-friendly alternative to AWS EMR, making it easier to manage and providing a unified experience with easy configuration management
With IOMETE, users with less technical pedigree can perform data science and analytics at scale with the help of a unified analytics platform that provides an easy way to build end-to-end data pipelines and ML use cases
IOMETE features a transparent flat-fee cost model and facilitates cost reductions by using AWS’s reserved instances, discounts, spot instances, and other cloud optimizations in AWS
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Start a 15-day Free Trial. In the Free Trial you get access to the Enterprise Plan and can explore all features. No credit card required. After 15 days you’ll be automatically transitioned to the Free Plan
How to install IOMETE
Easily install IOMETE on AWS using Terraform and enjoy the benefits of a cloud lakehouse platform.
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.
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.
A virtual lakehouse is a cluster of compute resources that provide the required resources, such as CPU, and memory, to perform the querying processing.
Iceberg tables and Spark
IOMETE features Apache Iceberg as its table format and uses Apache Spark as its compute engine.
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.