Smarter Databricks Pipelines. Trusted AI.

Unify data quality, pipeline reliability, and compute optimization across your Databricks Lakehouse—powered by agentic intelligence.

Book a Demo

Take Product Tour

Download Solution Brief

Trusted by Top G2000 Data and AI Enterprises

Build Trust. Deliver Reliable Outcomes.

A global CPG brand faced frequent job failures in Databricks. With Acceldata, they

Feel the Impact

Cut job failure detection from hours to 10 minutes

Restored 100% dashboard reliability

Reduced cluster spend by 25%
‍

Invisible Risks. Expensive Outcomes.

What’s Breaking Your Databricks Pipelines? Data quality, lineage gaps, and AI drift stall even the best architectures.

Data Quality Gaps: Schema drift and nulls compromise analytics and AI.

Pipeline Failures: No lineage slows Spark job debugging.

Operational Complexity: Inefficient clusters and jobs inflate cloud costs.

AI Readiness Issues: Drift and staleness degrade model accuracy.

Governance Gaps: Manual policies can’t scale with complex dataflows.

Migration Hurdles: Transitions stall without real-time validation.

Debugging Bottlenecks: No root-cause traceability across Spark pipelines.

Ensure Trusted Data at Scale

Detect drift, anomalies, and quality issues early—AI automation resolves them in minutes.

Debug Pipelines with Intelligence

Get near real-time visibility into Spark jobs, SQL workloads, and pipeline dependencies.

Power AI with Clean, Reliable Data

Deliver high-quality inputs to ML models built with MLflow, Feature Store, and Delta Lake.

Compliance Readiness Across Databricks Estate

Apply automated, metadata-driven policies across Unity Catalog, tables, and jobs.

Accelerate and De-Risk Migrations

Validate performance and lineage before and after Lakehouse transitions.

Scale with Confidence, Not Complexity

Shift from reactive incident response to autonomous, AI-driven pipeline operations.

Real Problems. Solved with Acceldata.

Observability with memory. Governance with automation. Outcomes with confidence.

Future-Proof AI Data Pipelines

Use AI to detect and remediate anomalies early to maintain trust as data workloads evolve.

Deliver Reliable ML & BI Outputs

Validate feature freshness before every model run and fix issues before they break dashboards.

Catch Data Failures Early

Auto-detect issues in Delta Live Tables and pipelines.

Fix the Root Cause

Trace broken BI dashboards to failed ETL in Unity Catalog.

Streamline Governance

Apply intelligent rules to assets, queries, and jobs across SQL Warehouses and notebooks.

Cut Costs Without Guesswork

Reduce compute waste with insights into idle clusters and misfiring jobs.

Power Databricks Your Way

Built to fit your architecture. Choose the right deployment for your scale.

PushDown Mode

Runs natively in Databricks for efficient observability.

Read the Brief

ScaleOut Mode

Spark-based engine for high-scale, hybrid environments.

Read the Blog

See How it Works

Trust Earned. Value Proven.

Acceldata has opened quite a few doors for us. The Data Reliability aspects have been able to satisfy all of our traditional Data Quality requirements with the additional benefit of measuring as data moves between multiple environments.

— Timothy C., Senior Expert of Data Governance - Enterprise

Dominate with Data

40%

reduction in pipeline downtime.

30%

faster time-to-model deployment.

25%

lower cluster costs.

99.9%

SLA adherence on migrated workloads.

Unify Your Databricks Stack

Built to fit your architecture. Choose the right deployment for your scale.

See All Integrations

Clear Doubts. Deploy with Confidence.

Can Acceldata help optimize Databricks cluster and job costs?

Absolutely. Acceldata identifies overprovisioned clusters, idle workloads, and inefficient Spark jobs—then recommends actions to right-size resources and reduce costs.

How does Acceldata protect AI/ML workflows on Databricks?

By continuously monitoring data quality and detecting drift in training datasets, Acceldata ensures your ML models remain accurate, reliable, and production-ready.

Can I trace failures across complex, multi-hop pipelines in Databricks?

Yes. Acceldata provides full end-to-end lineage from ingestion to output, enabling rapid root-cause analysis across Spark jobs and datasets.

What’s the deployment model for Acceldata on Databricks?

You can choose between native PushDown mode (runs on Databricks for efficiency) or ScaleOut mode (external Spark engine for high-volume workloads).

How fast can I see value after deploying Acceldata?

Most teams gain visibility into data health and job performance within hours. Full observability—including agentic alerts, lineage, and cost insights—can be activated within days.

How does agentic observability differ from traditional monitoring?

Traditional tools detect problems; Acceldata's agentic observability reasons over them, recommends fixes, and enables self-healing pipelines.

Resources & News

Stay Ahead with Acceldata

Solution Brief

Acceldata for Databricks

Tech Blog

Hadoop vs Snowflake – Optimize migrations.

CEO Blog

Announcing Acceldata Agentic Data Management

Timothy C.

Senior Expert of Data Governance - Enterprise

Acceldata has opened quite a few doors for us at Nestle. We have started with the Data Reliability features of the product and moved into the Cloud/Cost Optimization aspects.

Acceldata User

Small - Business

It helped to improve data and pipeline reliability and cost-optimization.
‍

Stop runaway consumption. Maximize ROI on Databricks spend

Maintain continuous control over your Databricks lakehouse, underutilized clusters, inefficient workflows and jobs, DLTs, SQL warehouses, notebooks and runaway queries.

Forecast your future spend with 96-98% accuracy.

Take Product Tour

Watch Video

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Timothy C.

Senior Expert of Data Governance - Enterprise

Acceldata has opened quite a few doors for us at Nestle. We have started with the Data Reliability features of the product and moved into the Cloud/Cost Optimization aspects.

Acceldata User

Small - Business

It helped to improve data and pipeline reliability and cost-optimization.
‍

Be efficient in fine-tuning, support, and maintenance tasks

For data-driven enterprises operating at Petabyte scale, Acceldata Enterprise Data Observability is the only viable choice today!

Stop

runaway consumption and cost spikes with timely alerts

Trace

why & who of cost overruns with 65% faster MTTR

Forecast

data and compute budgets with 97% accuracy

X-ray Databricks account usage, Optimize ROI with precise tips.

Try Acceldata free for 30 days

Identify wasted spend in 30 minutes

Connect your data sources or use sample demo data

Guided experiences and email/phone assistance

Start Free Trial

No credit Card Required!

Reduce runaway consumption with Acceldata

For Finops Teams

Reduce infrastructure costs by 20% in first 2 weeks

75% decrease in downtime per incident and anticipate cost spikes using anomaly detection and root cause analysis with multidimensional utilization drilldowns and accurate chargebacks

Prevent incidents and inefficient resource utilization and wastage with a cost-aware culture across teams using automated thresholds and remediation with contextual reporting, timely alerts, automated recommendations, and detailed RCA

Analyze current contract plans and budgets, and create 96-98% accurate spend forecasts and cost allocations using department/project level chargeback and AI-driven budgeting

Learn more

Improve Data Ops & infrastructure with Acceldata

For Platform Teams

Operational health checks to improve Data Ops & infrastructure

Switch from batch or periodic sprints to continuous, automated, real-time monitoring and optimization of resources: underutilized clusters, workflows, DLT pipelines, job runs, notebooks and queries.

Stretch your Databricks DBUs and avoid runaway consumption. Enforce guardrails and stay abreast of latest best practices via automated codification into your data observability solution

75% fewer performance-related incidents, and reduce time spent on finetuning, support and maintenance with continuously-ON monitoring, RCA, automated remediation with recommendations, alerts and notifications

Learn more

For Platform & data Teams

360^o view into Databricks spend and infrastructure utilization

Continuous, granular visibility into spend and workload utilization through dashboards, resource monitors, trends, and contextual drilldowns pinpointing to root causes of issues and ownership

Awareness of feature utilization, overspend, suboptimal queries, and overprovisioning - through usage information across entities such as clusters, workflows, job runs, queries & notebooks, with insights into chargeback by org, BUs, teams, projects, and users.

Loop in the right team members at the right time with relevant reports directly inboxed to you and with timely alerts and notifications through multiple channels: Slack, email, ServiceNow tickets, MS Teams, Jira, and others.

Learn more

Struggling with Pipeline Reliability in Databricks?

Ensure trusted data, accelerate insights, and pipeline visibility across your Databricks environment.

Download Brief

Metrics

Speed up migration to Databricks Lakehouse

Acceldata’s capabilities such as data drift, schema drift, reconciliation help speed migration to Databricks. Migrate thousands of pipelines across hundreds of sources with ease.

15%

faster migrations and faster time-to-delivery

20%

faster load time and 10% faster query runtime within 2 weeks

20%

productivity improvement in admin and support within a month

10%

productivity improvement in data engineering within a month

Request Demo Start Free Trial

Empower data engineers with self-service workload optimization

Cluster and Compute Rightsizing

Optimize the size and performance of your Databricks lakehouse and clusters

Table optimization

Optimize data layouts such as tables and clusters

Cluster and Compute Rightsizing

Optimize jobs run as well as query structure and performance

MAXIMIZE DATA QUALITY AND ELIMINATE DATA OUTAGES

A single pane of glass across your data environment

Continuously ensure reliability of data and pipelines across your data landscape, in addition to cost and operational optimization

Data Quality Policies and Anomaly Detection

Ensure the reliability and timeliness of data across your data landscape with Acceldata’s continuous and automated data quality monitoring platform.

Leverage anomaly detection and a flexible and highly scalable framework with policies for all the 6 Dimensions of Data Quality (accuracy, completeness, consistency, freshness, validity and uniqueness), data reconciliation, schema drift, and data drift.

Data Pipeline Observability

Gain end-to-end visibility and insights into your data assets and pipelines from start to finish to ensure data gets delivered properly and on-time.

Eliminate operational blindspots and clogged, slow, inefficient or stopped pipelines by continuously observing pipelines such as Kafka and Airflow.

Shift Left and the “1 x 10 x 100 Rule” of Data Quality

Detect problems at the beginning of your data landscape to isolate issues before they hit your Databricks Lakehouse or before they affect downstream analytics and consumption.

Implement the “1 x 10 x 100 Rule” of Data Quality that states that the cost of fixing a problem increases exponentially as you move from the source or ingestion zone to the consumption zone or Databricks Lakehouse, where it finally lands up

Observability Into All Data Across Your Landscape

Get visibility into all your data in Databricks and across your entire data stack: data-at-rest, data-in-motion, and data-for-consumption.

Trace transformation failures and data inaccuracy across tables and columns with detailed data lineage and by pinpointing the exact root cause.

Smarter Databricks Pipelines. Trusted AI.

Build Trust. Deliver Reliable Outcomes.

Invisible Risks. Expensive Outcomes.

Make Databricks AI-Ready.

Real Problems. Solved with Acceldata.

Power Databricks Your Way

Trust Earned. Value Proven.

Dominate with Data

Unify Your Databricks Stack

Clear Doubts. Deploy with Confidence.

Stay Ahead with Acceldata

Acceldata for Databricks

Hadoop vs Snowflake – Optimize migrations.

Announcing Acceldata Agentic Data Management

Users Love Us

Timothy C.

Acceldata User

Ready to get started

Stop runaway consumption. Maximize ROI on Databricks spend

Users Love Us

Timothy C.

Acceldata User

Be efficient in fine-tuning, support, and maintenance tasks

Stop

Trace

Forecast

X-ray Databricks account usage, Optimize ROI with precise tips.

Try Acceldata free for 30 days

Reduce infrastructure costs by 20% in first 2 weeks

Operational health checks to improve Data Ops & infrastructure

360o view into Databricks spend and infrastructure utilization

Struggling with Pipeline Reliability in Databricks?

Speed up migration to Databricks Lakehouse

Empower data engineers with self-service workload optimization

Cluster and Compute Rightsizing

Table optimization

Cluster and Compute Rightsizing

A single pane of glass across your data environment

Data Quality Policies and Anomaly Detection

Data Pipeline Observability

Shift Left and the “1 x 10 x 100 Rule” of Data Quality

Observability Into All Data Across Your Landscape

360^o view into Databricks spend and infrastructure utilization