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Cake for Analytics

Slice and explore datasets, surface data quality issues, and build custom dashboards inside a composable, cloud-agnostic platform.

 

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Overview

Before you train a model or deploy a pipeline, you need to understand your data and catch issues before they create downstream problems. Analytics is the first line of defense in any AI workflow, helping teams identify anomalies, visualize trends, and evaluate model inputs at scale.

Cake integrates tools like Superset, AutoViz, and Spark into your AI pipelines, so you can explore datasets, visualize distributions, track edge cases, and debug feature behavior in context. Run one-off analyses in notebooks, automate reporting in workflows, or scale computations across distributed clusters, all without switching platforms.

With Cake’s composable architecture, you don’t need to choose between notebooks, dashboards, or pipeline-native reports; they all live in one portable stack. Whether you’re running in staging or prod, across AWS, GCP, Azure, or on-prem, your analytics tools stay consistent, scalable, and infrastructure-neutral.

Key benefits

  • Accelerated decision-making: Unified analytics pipelines delivered real-time insights instead of lagging reports.

  • Improved data accessibility: Centralized dashboards made complex data easily consumable across teams.

  • Strengthened compliance: Governance and audit features ensured analytics workflows met enterprise regulatory requirements.

  • Reduced operational costs: Automated reporting and optimized compute lowered the expense of managing large-scale analytics.

  • Increased flexibility: Modular architecture enabled integration.

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Increase in
MLOps productivity

 

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Faster model deployment
to production

 

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Annual savings per
LLM project

THE CAKE DIFFERENCE

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From lagging dashboards to
intelligent, real-time insights

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Static dashboards

Built for reporting, not decision-making: Traditional BI tools summarize data but don’t support automation or dynamic workflows.

  • Data is delayed, pre-aggregated, and manually queried
  • No integration with LLMs, agents, or downstream actions
  • Can’t reason over unstructured or semi-structured data
  • Hard to share insights across models, teams, or applications

Result:

Insights that stay stuck in dashboards, not in workflows

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Analytics with Cake

Composable analytics for real-time decisions and automation: Cake connects analytics to agents, models, and workflows—so insights actually drive outcomes.

  • Stream data from any source and analyze in real time
  • Integrate with AI agents to summarize, interpret, or act on insights
  • Support structured and unstructured data with full observability
  • Build once, and re-use analytics pipelines across teams and systems

Result:

Live insights, smarter automation, and analytics that drive value, not just reports

EXAMPLE USE CASES

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How teams are using Cake’s analytics stack

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Exploratory data analysis

Quickly scan for patterns, distributions, or anomalies in raw datasets before training.

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Data quality & drift monitoring

Visualize feature distributions and check for skew or unexpected changes over time.

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Stakeholder reporting

Create reproducible, shareable dashboards from pipeline outputs or notebook results without the need for any external BI platform.

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Ad hoc exploration with notebooks

Enable data teams to run exploratory analyses in Jupyter, Hex, or Deepnote with access to live, governed datasets.

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Embedded analytics for applications

Deliver dashboards, metrics, and visualizations directly within customer-facing products using tools like Metabase or Superset.

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Time-series and event analytics

Analyze high-frequency data from IoT devices, logs, or clickstreams using optimized backends like ClickHouse or Druid.

IN DEPTH

From data to foresight: predictive analytics that works

Anticipate demand, optimize decisions, and guide strategy with predictive models built on Cake’s modular AI infrastructure.

Read More >

BLOG

Why data intelligence is the next competitive advantage

Learn what data intelligence is and how it transforms raw data into actionable insights, driving business value and strategic decision-making.

Read More >

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
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“

"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

Read the case study
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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

COMPONENTS

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Tools that power Cake's analytics stack

 

Frequently asked questions

What is analytics in AI?

Analytics in AI refers to the use of machine learning, statistical models, and large-scale data processing to uncover insights from complex datasets. It goes beyond descriptive reporting to enable predictive and prescriptive decision-making.

How does Cake support enterprise analytics?

What types of analytics can I build with Cake?

Why choose Cake over traditional analytics platforms?

Can Cake integrate with my existing BI and visualization tools?

Learn more about analytics built on Cake

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How to Build an Enterprise AI Stack (That Doesn’t Break at Scale)

How to Build an Enterprise AI Stack (That Doesn’t Break at Scale)

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