Qluster
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About
Qluster is an AI-powered data ingestion tool that automates data cleaning and validation. It supports various data sources and destinations, including Amazon S3, MinIO, Google Cloud Storage, SFTP, Dropbox, Postgres, and Snowflake. Qluster uses AI to automatically match incoming data to existing data, quarantine bad data, and notify users of data issues. It also provides data lineage and high security standards. Qluster is easy to use and highly scalable, and it's suitable for both cloud and on-prem deployments. It's designed to reduce data firefighting and enable non-engineers to ingest, clean, and unify data at scale. The tool is created by the makers of DeepDiff.
Platform
Task
Features
• high security standards
• observability
• data lineage
• data quarantine
• support for various data sources and destinations such as amazon s3, minio, google cloud storage, sftp, dropbox, postgres, snowflake etc
• ai-powered data matching
• automated data cleaning and validation
• data import and enrichment
FAQs
What data source connections does Qluster support?
Qluster has integrations with Amazon S3, MinIO, Google Cloud Storage, SFTP, and Dropbox. Qluster out of the box can read data from public addresses such as Google Sheets too.
What data destinations does Qluster support?
Postgres, S3, Google Cloud Storage, MinIO, and Snowflake.
Can data be transformed before arriving at their destination?
Yes, although Qluster is not designed to replace a traditional ETL tool, many of the common transformations can be achieved with Qluster.
What data formats does Qluster support?
We accept CSV, XLSX, JSON, JSONL. The files can be compressed and even GPG encrypted.
Which cloud environments does Qluster run on?
We currently support Google Cloud Services and AWS.
What infrastructure is required to run Qluster?
In Qluster SaaS deployment, you only need a Postgres database and an object storage layer such as AWS S3 or Google Cloud Storage.In Qluster enterprise deployment, a Kubernetes cluster is required in addition to the above.
Why can't I require my customers or vendors to use a CSV template instead of Qluster for data ingestion?
While this might be fine for ad-hoc imports, companies consistently sending new data need more time and resources to update their data exports to match other systems manually. Qluster makes this process effortless and eliminates issues related to human error.
Does Qluster offer managed support services for rule building or customization?
Yes, we can offer additional professional services to tailor the solution to your specific needs.
What client data does Qluster keep?
a. The client's data belongs to the client. b. The entire data flow lifecycle stays within your virtual private cloud in the on-prem deployment. c. In the hosted version, ephemeral data specific to a data source may be used by a process, i.e., in the form of logs. This data stream is essential to the ingestion process and is retained for debugging for up to a few days, depending on the client's requirements. d. In the hosted version, we can let you host the settings and logs in your infrastructure. Then there will be absolutely no traces of your data in our infrastructure except the metadata about your data.
How are the AI models trained?
Metadata from successful data imports will be used to train the AI model to help automate data quality rules and field mappings.
Does Qluster sell any client data to other companies?
Never have and never will! Client data security is our number one priority, and we take it very seriously.
Is algorithm training required for each source?
No, training is required for the dataset, not for each individual data source.
Who is responsible for training the Qluster algorithm?
The Qluster team will handle the training for each client. This is not something clients need to worry about.
What is the minimum number of rows for a training file?
We recommend at least 20 rows of data to have an accurate baseline of your data. We can work with less, but it's not ideal.
What information from the training data file is kept by Qluster?
The training data is used to extract metadata about your data that explains how your data looks in aggregate. We use the training data to make our anomaly detection algorithm more accurate and better understand your typical data structure, shape, and other data properties. Qluster retains this metadata to enhance our product offering. We do not keep any of your actual data. Your data belongs to you.
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