Data Observability for Snowflake Register
Data Observability for Snowflake Register
Unravel’s AI-driven cloud cost optimization for BigQuery delivers insights based on Unravel’s deep observability at the job, user, and code level to supply AI-driven cost optimization recommendations for slots and SQL queries, including slot provisioning, query duration, autoscaling efficiencies, and more. With Unravel, BigQuery users can speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads.
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Unravel’s AI-powered Insights Engine understands all the intricacies and complexities of BigQuery and the supporting infrastructure to optimize efficiency and performance.
Unravel for BigQuery provides a single source of truth to improve collaboration across functional teams and accelerates workflows for common use cases. Below are just a few examples of how Unravel helps BigQuery users for specific situations.
Scenario: Understand what we pay for BigQuery down to the user/app level in real time, accurately forecast future spend with confidence.
Benefits: Granular visibility at the project, job, and user level enables FinOps practitioners to perform cost allocation, estimate annual cloud data application costs, cost drivers, break-even, and ROI analysis.
Scenario: Identify the most impactful recommendations to optimize overall cost and performance.
Benefits: AI-powered performance and cost optimization recommendations enable FinOps and data teams to rapidly upskill team members, implement cost efficiency SLAs, and optimize BigQuery pricing tier usage to maximize the company’s cloud data ROI.
Scenario: Identify the most impactful recommendations to optimize the cost and performance of a project.
Benefits: AI-driven insights and recommendations enable product and data teams to improve slot utilization, boost SQL query performance, leverage table partitioning and column clustering to achieve cost efficiency SLAs and launch more data jobs within the same project budget.
Scenario: Live monitoring with alerts.
Benefits: Live monitoring with alerts speed MTTR and prevent outages before they happen.
Scenario: Debugging a job and comparing jobs.
Benefits: Automatic troubleshooting guides data teams directly to the pinpoint the source of job failures down to the line of code or SQL query along with AI recommendations to fix it and prevent future issues.
Scenario: Identify expensive, inefficient, or failed jobs.
Benefits: Proactively improve cost efficiency, performance, and reliability before deploying jobs into production. Compare two jobs side-by-side to find any metrics that are different between the two runs, even if the queries are different.
Yes. BigQuery is serverless, meaning that you do not need to deploy compute or storage. BigQuery pricing includes compute and storage costs and depends on the type of analysis, type and amount of storage, data ingestion and other factors. You can also reserve compute capacity to reduce processing costs. Estimate BigQuery costs online with the Google Cloud pricing calculator.
Yes. Unravel supports both on-demand compute pricing and BigQuery Editions. For example, Unravel can help you improve cost efficiency by recommending a different pricing plan. Unravel can also recommend the minimum and maximum slot settings depending on your workload.
Yes. Unravel alerts can be generated by setting AutoActions. For example, you can use an AutoAction to notify you about a situation that requires manual intervention, such as resource contention or stuck jobs.
Yes. Unravel provides AI-powered recommendations to help you improve query efficiency. Optimal SQL queries enable you to accelerate results and serve more user requests with your existing BigQuery resources. For example, Unravel helps you make better use of partition and cluster keys, improve performance with partition pruning, and avoid common anti-patterns such as SELECT * on “wide” tables that contain a large number of infrequently-used columns.
Yes. Unravel can be set up to automatically create and configure resources in more than 100 projects at a time. You can either add single projects or multiple projects at a time for Unravel monitoring. These projects can be added either with customer-supplied credentials or with Unravel-generated credentials.
Yes. You can create custom dashboards using a collection of pre-built components. Custom dashboards help you improve communication and collaboration between teams and give leaders visibility to key metrics.
Yes. You can create new alerts using a flexible set of criteria and communications platforms integrations. Custom alerts effectively provides an early warning system to alert you of resource usage spikes before it hits the cloud bill.
Unravel provides granular Insights, recommendations, and automation for before, during and after your Spark, Hadoop and data migration to BigQuery. Unravel for BigQuery is a complete data observability platform to help you tune, troubleshoot, cost-optimize, and ensure data quality on BigQuery. Unravel provides AI-powered recommendations and automated actions to enable intelligent optimization of data pipelines and data applications.
Yes, Unravel includes showback/chargeback reports for BigQuery compute and storage costs by projects and users. You can specify the reporting time window to match your organization’s budgeting cycle.
