Triggering small ETL workloads
Processing less data is the best way to reduce data platform costs. The key is to use event-driven pipelines rather than scheduled pipelines to only process data when it is ready!
Big data adds significant value to your organization but can also add significant cost. Buoyant Data specializes in improving data infrastructure with high performance low-cost ingestion and transformation pipelines with Rust, Python, Databricks, and AWS.
Let's improve the ROI of your data platform!
Contact usAs creators of the deltalake Python and Rust packages,
we have been supporting Delta Lake applications since the beginning.
Buoyant Data offers one-time on-demand support as well as on-going
technical support subscriptions for your team!
With years of experience creating and deploying Rust data applications with delta-rs, kafka-delta-ingest, and more, Buoyant Data can help your organization adopt and excel with high-performance, low-cost data services or AWS Lambdas built with Rust.
Our expertise in leveraging Delta Lake includes both the Databricks Platform (Serverless, Unity, etc) and the AWS Data Platform (Glue, Athena, EMR). We can help design and implement a scalable and efficient data platform for your organization.
For organizations with existing data infrastructure and analytics, we can analyze and optimize in-place to squeeze faster queries and lower costs out of your existing data platform without substantial rearchitecture.
Expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale.
Processing less data is the best way to reduce data platform costs. The key is to use event-driven pipelines rather than scheduled pipelines to only process data when it is ready!
In this episode of the Data Engineering Central podcast, I join Daniel Beach to talk the present and future of the data platform. We discuss the "lakehouse architecture" as a stepping stone into what comes next for data engineering in an increasingly LLM-driven ecosystem.
The storage changes we need today must support "multimodal data" which is a dramatic departure in many ways from the traditional query and usage patterns our existing infrastructure supports. This post explores some research and development to deliver multimodal data for analysts and developers without changing the entire platform.
