IOMETE and Amazon Redshift
Augment Redshift with IOMETE to reduce cost and expand your data platform capabilities.
You are experiencing one or more of the following challenges
You realize that in Redshift, storage is not decoupled from compute. This can make it difficult to scale the compute resources separately from the data resources
You are looking for more flexible data processing and support for data lake workloads using Apache Spark, as well as a notebook service for data preparation and machine learning training
You are seeking improved performance through auto-scaling that can handle any data volume without manual intervention, scaling up or down as needed
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 range of data processing tools, including Apache Spark, which can handle a wide range of data formats and processing requirements. This can make it easier to work with diverse data sets and build flexible data pipelines
IOMETE covers data science use cases, with Apache Spark jobs and a notebook service. Moreover, the built-in query federation allows you to query operational data sources directly without building any ingestion data pipeline
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 Redshift as a data warehouse for the last-mile analytics
Combine Redshift’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.
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.