Acceldata is now available on Google Cloud Marketplace!
Know more
By selecting “Accept All Cookies,” you consent to the storage of cookies on your device to improve site navigation, analyze site usage, and support our marketing initiatives. For further details, please review our Privacy Policy.

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.

No Registration Required!
TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

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!
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
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
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.

Ready to start your
data observability journey?