Finding it difficult extracting data to make timely decisions? You are not alone. Learn how others apply EdgeSet to join databases, with little or no technical knowledge ~low-code, all within minutes
Reducing monthly Amazon AWS cost by up to 99%
Instead of doing analysis on Amazon EC2 and RDS (cost of ~$5k/month), client stored trading data in Amazon S3 (~$50/month) and utilised EdgeSet. EdgeSet scans the S3 files and infers the structure of the data, creating a SQL view of all the data so that the team need only write normal SQL queries instead of a custom data loading and analysis scripts.
Other than Amazon S3, the data can be anywhere, including a hard drive or other cloud services like Google Cloud Storage, Azure Blob Storage, Backblaze B2, etc. As long as the data is in CSV, ORC, Parquet, or similar tabular formats, you’ll be able to query it after a short, sub 1-minute scan of the folder structure, and data sampling that’s automatically done by EdgeSet.
Real Estate Predictions
Real Estate Investments
We improved on divergent and incomplete data sources, and joined databases whilst preserving data integrity. As a result, an AI/ML model was created to help the client predict how long a property would stay on the market at a given price. This enabled the client to purge their portfolio and acquisition pipeline, avoiding multi-year holding periods.
After the client acquired a competitor with millions of patients, we connected the disparate data sources together, de-duplicated customer records, and built both simple dashboards and more complex statistical products. This enabled the client to profit from synergies beyond the price paid for the acquisition.