Acceldata

Founded by Rohit Choudhary in 2018

Monitor data quality, track pipeline performance, and manage enterprise workloads across hybrid environments

Pricing model

Demo

Rating

★ Positive

Time to value

Immediate

You can use Acceldata to track data quality and pipeline reliability across on-premise and cloud systems. It reconciles records across different databases to find discrepancies, profiles tables to catch schema changes, and alerts engineering teams to anomalies before they disrupt downstream reports. Teams can also trace data lineage end-to-end to understand dependencies and troubleshoot job failures. Additionally, it monitors big data infrastructure such as Hadoop and Spark clusters to control costs, reduce cluster downtime, and inspect agentic workflows without moving datasets out of your environment.

What Acceldata does

Set automated data quality rules across multiple data assetsTrace data lineage from reports back to original sourcesProfile dataset distributions without writing manual SQL queriesTrack LLM prompts and model outputs in productionInspect failed pipeline jobs and identify root causesMonitor cluster resource usage across big data workloadsReceive instant alerts when data quality rules failAutomated data reconciliation to detect mismatches between source and target systemsContinuous data profiling to track freshness, completeness, and schema driftStatistical anomaly detection to catch pipeline errors before data reaches usersEnd-to-end data lineage mapping to track records from ingestion to reportingLLM tracing and prompt evaluation for enterprise generative AI applicationsCluster and queue monitoring for Hadoop, Spark, and YARN environmentsRole-based access controls and privacy guardrails for distributed data assets

Pricing breakdown

Pricing model: Book a demo

Frequently asked

Apache Spark

Kubernetes

Salesforce

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