DataBuck Monitor | AWS S3 Cloud Data ingestion Monitor & Validation

Catch data errors at ingestion Automate data pipeline validation and control and cut data rework efforts by 90.

Data Observability for Azure Data Lake, Warehouse, and Pipeline:

Prevent critical data issues before it breaks your pipeline or reaches the data users.

Autonomously observe for critical errors at all points in the Data Testing for Azure pipeline and alert data engineers to control data flow.

Observe the Data Lake, Data Warehouse and the Pipeline:

Data Freshness, Missing files, Additional files, Changes in the file structure, Sudden changes in record count

Supported Platforms:

Data Lakes:

AWS S3, Azure Data Lake (ADL/ADLS), Google GCP

Data Warehouse:

Snowflake

Data Pipelines:

Glue, Airflow, Databricks, DataFlow, Synapse

Challenges with current Observability tools:

Data Lake is the starting point for your data journey and the data pipeline is the artery of your organization. Monitoring just the Snowflake Warehouse is good, but grossly insufficient. The most prevalent Observability tools in the market today are designed for monitoring Data Warehouses like Snowflake. They are not unified platforms that can span across an organization.

DataBuck’s unified Observability platform:

Powered by Machine Learning Data Quality, DataBuck is an advanced Observability tool. It automatically figures out what data is expected, when, and where. The right stakeholders get alerted when there are deviations.

Benefits of DataBuck:

Ensures data is reliable as soon as it lands in the lake as it cascades down the pipeline and also in the data warehouse The only solution that leverages AI/ML to identify hard-to-detect errors Plug and play for Data Engineers – no rules writing Self-service for business takeholders Auditable.

What do you gain?

Every step a data error propagates and flows downstream the cost to fix it is 10x more expensive. Cut cost and increase productivity by a giant leap.

Observe data autonomously with AI/ML

Cut data maintenance work and cost by over 50%

10x efficient in scaling Data Observability to 1,000’s of data sets

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