Acceldata provides a ready to use data monitoring solution for Snowflake which doesn't require much setup to get started with. It also provides a wide range of metrics.
Michael M.
Enterprise
Acceldata provides an "out of the box" environment monitoring solution for Snowflake with very little setup time and a varied selection of metrics that are monitored.
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.
Metrics
Speed up migration to Snowflake
Acceldata’s capabilities such as data drift, schema drift, reconciliation help speed migration to Snowflake. 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
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, 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.
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, and overprovisioned roles and privileges.
Stretch your Snowflake credits and avoid runaway consumption. Enforce guardrails and stay abreast of the 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
360o view into Snowflake 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.
Empower data engineers with self-service workload optimization
Warehouse and Compute Rightsizing
Optimize the size and performance of your Snowflake warehouses and compute
Table optimization
Optimize data layouts such as tables, partitions, clustering and materialized views
Workload optimization and performance finetuning
Optimize query structure and performance
Maximize data quality and eliminate data outages
A single pane of glass for 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.
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