Can you trust the data you are receiving? Is it accurate, complete, and consistent?Request a demo
Data can go wrong in a million ways. Stop playing whack-a-mole.
You don't have control over incoming files
Custom file ingestion pipelines are brittle and take a lot of valuable engineering time to maintain.
Anything that can go wrong, will go wrong.
It is a never ending game of patching holes in the system.
Automatically detect new files from your vendors or simply drop the file into Qluster.
Start by giving Qluster you clean data so it can create an internal model of how your good data should look like. Qluster uses statistics and machine learning to profile and learn from your data. You can also define custom validation rules through the UI.
Qluster uses AI to automatically match the new incoming data to your existing data. You can always decide to add new fields here when needed.
Once Qluster is trained, it will stop and quarantine the bad data from entering your system. You can review the bad data at your convenience and fix it. Both sender and receiver of data are enabled to log in and see what went wrong.
Qluster aggregates similar data issues together and notifies you through Slack, email, or other means when your attention is needed. It provides the 360 degree picture of what is going on and who did what when.
Qluster tracks how data is modified as it gets cleaned. Every value modification is recorded, along with why and who changed it.
Qluster follows the industry's highest standards when it comes to the security of your data. Files are encrypted as soon as they are picked up from the sources. Qluster does not hold on to your data. You can even host Qluster's settings in the hosted deployment. Logs of data are constantly wiped out to make sure no traces of data are left within our systems.
We create solid data tools. DeepDiff has over 7 million monthly downloads.
Qluster is cloud native and runs on top of Kubernetes. It comes in a hosted version and in an on-prem version.
Currently we support Google Cloud.
AWS and Azure deployments are coming soon.
Qluster uses statistics and machine learning to profile and learn from your data.Learn more
Qluster adapts data structures to the incoming data so you don't have to predefine schemas if you don't want to.
Qluster shifts the burden of data cleaning from solely being the receiver's responsibility to a collaborative process between the two parties.
Let us solve your data ingestion problems so you can focus on what matters most to your business.
By aggregating data issues, we enable individual users to solve similar problems for the rest of the community in the same industry.
Qluster's goal is to be the easiest intelligent data ingestion tool for structured or semi structured data, i.e csv, ljson, or parquet files.
Qluster Enterprise (on-prem deployment) allows validation code in any language to be run via Docker images providing the ultimate freedom to do what you need to do.