Qluster
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About
Qluster turns raw, messy CSV/JSON into trusted, clean data sheets, preventing downstream surprises caused by bad data. It addresses the pain of manually finding and fixing data issues, and the challenges of incoming external data changing without notice. Qluster offers an Adaptive Data Import approach, learning from clean data to create internal models, using statistics and machine learning to profile and validate. Key features include intelligent matching, data quarantine, observability, and data lineage. It supports connections to various data sources like Amazon S3, MinIO, Google Cloud Storage, SFTP, and Dropbox, and destinations like Postgres, S3, Google Cloud Storage, MinIO, and Snowflake. Qluster is designed for easy setup, is highly scalable, and can be deployed on cloud (Google Cloud, AWS, Azure coming soon) or on-premise. It enables analysts and operations teams to ingest, clean, and unify data at scale, shifting the burden of data cleaning to a collaborative process. The tool supports CSV, XLSX, JSON, and JSONL formats, even compressed or GPG encrypted files. It uses metadata from successful imports to train AI models for automating data quality rules and field mappings, ensuring client data security.
Platform
Task
Features
• observability
• data lineage
• training
• intelligent matching
• data quarantine
• security as a first class citizen
• connections
FAQs
What data source connections does Qluster support?
Qluster has integrations with Amazon S3, MinIO, Google Cloud Storage, SFTP, and Dropbox. It can also read data from public addresses such as Google Sheets.
What data destinations does Qluster support?
Qluster supports Postgres, S3, Google Cloud Storage, MinIO, and Snowflake as data destinations.
Can data be transformed before arriving at their destination?
Yes, although Qluster is not designed to replace a traditional ETL tool, many common transformations can be achieved with it.
What data formats does Qluster support?
Qluster accepts CSV, XLSX, JSON, and JSONL formats. Files can also be compressed and GPG encrypted.
Which cloud environments does Qluster run on?
Qluster currently supports Google Cloud Services (GCP) and Amazon Web Services (AWS).
What infrastructure is required to run Qluster?
For SaaS deployment, you only need a Postgres database and object storage (AWS S3 or Google Cloud Storage). Enterprise deployment additionally requires a Kubernetes cluster.
Why can't I require my customers or vendors to use a CSV template instead of Qluster for data ingestion?
While CSV templates work for ad-hoc imports, Qluster effortlessly manages consistent data streams, eliminating manual updates and human errors for companies receiving new data regularly.
Does Qluster offer managed support services for rule building or customization?
Yes, Qluster offers additional professional services to tailor the solution to your specific needs and can assist with rule building or customization.
What client data does Qluster keep?
Client data belongs to the client. In on-prem deployment, data stays within your VPC. In hosted version, ephemeral data/logs are kept briefly for debugging. Metadata about data structure is retained, not actual data.
How are the AI models trained?
Metadata from successful data imports is used to train the AI model, helping automate data quality rules and field mappings.
Does Qluster sell any client data to other companies?
Qluster prioritizes client data security and has never, and will never, sell any client data to other companies.
Is algorithm training required for each source?
No, training is required for the dataset as a whole, not for each individual data source.
Who is responsible for training the Qluster algorithm?
The Qluster team is responsible for handling the training for each client, so clients do not need to worry about it.
What is the minimum number of rows for a training file?
Qluster recommends at least 20 rows of data for a training file to ensure an accurate baseline of your data.
What information from the training data file is kept by Qluster?
Qluster uses training data to extract metadata about your data's aggregate structure and properties. This metadata is retained to enhance the product offering; your actual data is not kept.
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