Problem Statement
Data engineers are reactive to data issues
Problem Statement

Most data
issues are
If issues are reported, they are not quickly resolved
Incidents are found only when reported by data consumers
Proactive Data Observability: Shift left and solve problems at the source

Improved Mean Time to Discovery
Discover issues before data consumption

Improved Mean Time to Resolution
Instantly trace the cause of incidents

Improve data product SLAs
Enhance trust and consumer satisfaction

How Databand Approaches Observability
5 Steps to Proactive
Get the steps to achieve proactive
data observability.
Beyond Pretty Graphs:
How End-to-End Lineage Drives Better Actions.
Demo Center
See Databand in action with quick demos and videos.
Trusted by modern
data teams
Databand is trusted by the modern data teams to achieve better data quality for their business. Whether it’s detecting broken data pipelines or data quality issues at-rest in your warehouse, Databand has you covered.


Top US banks use Databand


Average improvement of mean time to resolution


Data quality monitoring for pipelines and at-rest


Supported integrations for data observability

Continuous data observability for
data engineering

Detect data incidents early, resolve them fast, and deliver trustworthy data.

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Detect earlier

Pinpoint unknown data incidents, and reduce mean time to detection (MTTD) from days to minutes.

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Resolve faster

Improve mean time to resolution (MTTR) with incident alerts and routing from weeks to hours.

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Deliver trustworthy data

Ensure confident decision-making for business consumers and keep customers happy.

We are different, see why

Let’s face it. There are a lot of data observability solutions out there. Here’s why Databand’s “shift-left” approach is different. Unlike others that only monitor data-at-rest in your warehouse, Databand provides a continuous data observability approach that ties directly into all stages of your data lifecycle, starting with your source data..

IBM Databand

Continuous Observability

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End-to-end observability

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Focused on data-in-motion + data-at-rest (from data pipelines to lakes to warehouses)

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Real-time alerting on pipeline execution

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Custom alerts for data SLAs

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Cross tool data lineage and impact analysis


Reactive Observability

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Singular focal observability

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Focused only on data-at-rest (warehouse only)

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Retroactive alerting on table inspection

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Incomplete metadata collection for SLAs

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Siloed lineage within each tool

How Databand works
Databand removes bad data surprises by detecting and resolving them before they create costly business impacts.
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Collect metadata

Automatically collect metadata from your modern data stack like Airflow, Spark, Databricks, Redshift, dbt, and Snowflake.

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Profile behavior

Build historical baselines based on common data pipeline behavior and get visibility into every data flow, from source to destination.

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Detect and alert data incidents

Detect high severity data reliability errors that impact your most critical pipelines and alert impacted teams.

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Resolve the root cause

Create smart communication workflows to resolve data quality issues & meet SLAs.

Open extensible
DataOps management
Databand’s open-source library enables you to track data quality Information, monitor pipeline health, and automate advanced DataOps processes.We Keep our library open to give engineers complete control over tracking data and the resorces needed to build custom extensions.
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Incident Management

Improve data reliability and quality under one roof with a single pane of glass for all your data incidents.

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End-to-end Lineage

Visualize how data incidents impact upstream and downstream components of your data stack.

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Data Reliability Monitoring

Monitor data pipeline errors such as failed runs, longer than expected durations, missing data operations, and unexpected schema changes.

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Data Quality Metrics

Continuously validate data quality with dataset metrics for SLAs, column changes, and null records.

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Anomaly Detection

Eliminate the unknown by seeing trends & detecting anomalies from your metadata in real-time.

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DataOps Alerting and Routing

Customize incident alerts and route notifications to impacted DataOps teams for faster resolution.

Databand core capabilities

Databand’s combined capabilities provide a one-stop-shop for all your data incidients. Now platform and data engineers can focus on building. not fixing their modern data stack.

How it works?

SiNGL collates the master data from source systems, cleanses, de-duplicates and using industry specific algorithms delivers a “Golden Record”. This Golden Record is stored in a Central Repository and can then be distributed back to the sources systems and to downstream applications – in batch and real-time.

Batch Integration Architecture

Real-time Integration Architecture


Other Benefits

  • Create a single, consolidated view of information 
  • Retain Customers – Gain visibility into total portfolio
  • Identify households for cross-sell and up-sell opportunities
  • Next best Offer – Manage product bundling 
  • Perform cross-channel analysis to assess credit risk levels 
  • Control access to client information to support privacy controls 
  • Meet compliance and regulatory requirements 

Fix data incidents fast

See how Databand can transform data observability at your organization today.

Book a demo

SiNGL is offered to Clients under the following two models

On Premises Model

  • SiNGL is hosted in the client’s secured data-centre (Infra provisioned by client)
  • The pricing includes the cost of the solution, services and support
  • The Services will be performed every month/quarter (or at the agreed frequency)

Cloud Model

  • SiNGL is hosted in Alpharithm’s secured data centre and managed by Alpharithm
  • The pricing includes the cost of the solution, services and support and Infrastructure
  • The services will be performed every month/quarter (or at the agreed frequency)