The Data Kingdom: Who’s the Lion King?

31 July, 2025

Earlier this year, I had the incredible opportunity to embark on a once-in-a-lifetime African safari with my girlfriend and close friends. Immersed in the wild, I came away with lasting lessons in patience, perspective, and the deep interconnectedness of ecosystems. Not long after, I found myself in a very different environment—San Francisco—attending both the Snowflake and Databricks conferences, surrounded by data professionals and cutting-edge technology.  In this blog, I draw parallels between these two seemingly unrelated experiences to offer a fresh and thoughtful perspective on today’s leading data platforms.

Introduction

In the vast kingdom of cloud data platforms, every player vies for their place in the Circle of Data Life. In the African safari, where balance depends on each creature knowing its role, modern organizations must understand where each platform fits within their own data ecosystem to thrive.

Choosing a cloud data platform is no longer about finding the “king of the jungle” but about recognizing which platform will best support your business strategy and empower your teams. That being said, there are clear leaders in the data platform ecosystem with strong advantages and potential disadvantages. Whether you’re dealing with the strength of a Mufasa-like powerhouse, the steady leadership of a Simba, or the cunning agility of a Scar, knowing their strengths and weaknesses helps you avoid pitfalls and seize business opportunities.

This guide aims to help you discern the roles these platforms play in YOUR unique environment—from Microsoft Fabric’s evolving realm, Databricks’ commanding presence, Snowflake’s reliable stewardship, to the broad but sometimes fragile territories of Google BigQuery and AWS.

Google BigQuery — The Giraffe with Fragile Legs

BigQuery stands tall in the data safari with its serverless architecture and effortless scalability. Like a giraffe scanning the horizon, it offers a high vantage point—ideal for organizations that value speed, simplicity, and flexibility. But beneath this elegant stature lurk structural quirks that can trip up even seasoned data teams.

Where it Thrives

• Instant, serverless scale: Users can run thousands of concurrent queries and analyze datasets with trillions of rows without managing clusters—Google highlights use cases scanning 100 trillion rows and supporting over 10,000 concurrent queries.
• Emerging AI and governance tooling: BigQuery integrates directly with Vertex AI, enabling in-database machine learning (via BigQuery ML), natural language querying (via Gemini), and predictive modeling at scale. On the governance side, Dataplex and Policy Tags support centralized metadata, lineage, and access control—meeting growing enterprise needs for compliance and responsible AI.

Where it Perishes

• Unpredictable cost via scan-based billing: Pay-as-you-scan pricing (≈ $5 per TB) can lead to sudden bill spikes. Inefficient queries—like using SELECT *, frequent row updates, and missing partitions or clustering—can hike bills.
• Complex IAM and permission management: GCP’s fine-grained permissions can slow collaboration and require specialized expertise to configure securely.
• Operational caveats: Regional inconsistencies, metadata propagation delays, and high data egress fees complicate multi-cloud or hybrid architectures.

Bottom Line: BigQuery offers unmatched serverless performance for bursty and analytical workloads, making it a top choice in some industries Retail, Financial Services, and increasingly Energy/Utilities. But its impressive legs can falter—leaving organizations exposed to steep, unplanned costs. Success requires disciplined query craft, robust cost governance, and thoughtful architecture. Used wisely, it towers over ordinary analytics; used carelessly, it can stumble spectacularly.

AWS Data Services — The Elephant Herd Too Big to Steer

AWS offers a vast, sprawling ecosystem—much like an enormous elephant herd roaming across diverse terrains. When moving in sync, it can reshape the landscape, carrying massive loads of data with precision and power. But without firm leadership, clearly defined paths, and well-trained guides, the sheer scale and momentum can lead to chaos, inefficiency, and runaway costs.

AWS lacks a native “single pane of glass” for end-to-end governance. Instead, teams must stitch together services like AWS Lake Formation, CloudTrail, Control Tower, and Cost Explorer to monitor security, performance, and spend. It’s a task that requires DevOps maturity, FinOps discipline, and strong architectural guardrails. Without these, even well-funded teams risk fragmentation—like a herd splitting in all directions.

This complexity has created a growing market for third-party solutions (e.g., Datadog, Alation, Monte Carlo, and Ternary) that attempt to bring visibility and coordination to AWS’s powerful but loosely coupled stack.

Where it Stomps

• Wide-ranging services (Redshift, Athena, Glue, EMR, etc.) covering almost any workload.
• Deep integrations for cloud-native teams.
• Constant innovation and ecosystem growth.

Where it Slows

• Service sprawl complicates governance and increases operational overhead.
• Requires mature DevOps and cloud expertise to manage tuning, costs, and security.
• Hidden costs in data transfers and inter-service interactions.

Bottom Line: AWS offers unmatched breadth but demands experienced teams and strong processes to prevent chaos and runaway costs.  Amazon had first-mover’s advantage in the cloud, and those companies that are using it still for data and artificial intelligence may be left behind.

Microsoft Fabric — Scar, The Schemer with a Shifting Story

Microsoft Fabric plays the part of Scar — ambitious, opportunistic, and constantly reinventing itself to stay relevant. With his silver tongue and royal lineage, Scar once promised unity — but left confusion, fragmentation, and some regret in his wake. So too has Microsoft Fabric emerged from the ashes of Azure Synapse, Power BI, and countless other overlapping services — wrapped now in a unified narrative of “one Microsoft platform for data and AI.”

At first glance, Fabric is seductive. For Microsoft shops already invested in Power BI, Azure AD, and Office 365, Fabric feels familiar — an easy on-ramp to unify data engineering, analytics, and business intelligence in one workspace. With an emphasis on Copilot integration, no-code/low-code ELT, and tight Power Platform alignment, Fabric is especially attractive to IT departments under pressure to modernize without hiring an army of cloud engineers.

But beneath the charm lies a platform still finding its footing. Fabric is less reliable with new features rolling out constantly, pricing models still shifting, and enterprise governance features either missing or half-baked. For organizations looking for stability, this evolving identity can raise red flags — especially when mission-critical workloads are at stake.

Where It Charms

• Copilot-Driven Productivity: Microsoft is leaning heavily into Copilot (generative AI) across Fabric — from generating DAX queries to automating pipeline development. For newer users and business analysts, this unlocks productivity fast — though accuracy and transparency still lag.
• Strong for Microsoft-Centric Orgs: Teams already using Azure AD, Office 365, Power BI, or Purview will find Fabric deeply integrated and familiar. It’s a natural extension for companies with Microsoft enterprise agreements and existing security/compliance frameworks.
• Governance Potential: Tools like Microsoft Purview, Fabric Capacity Metrics, and tenant-wide policy settings show early signs of enabling enterprise governance — but most features still require tuning or aren’t yet fully deployed.

Where It Schemes

• Rapid Rebrands and Fragmentation: Fabric is yet another rebrand in Microsoft’s long list of data tools (Azure SQL DW → Synapse → Fabric). Many organizations are still running legacy workloads in Synapse or Power BI Premium and now must sort out which “Microsoft” platform to prioritize.
• Capacity-Based Pricing Surprises: Fabric’s pricing model is built around capacities, not workloads — similar to Power BI Premium. This means costs can spike unexpectedly if teams don’t size their capacity units correctly, especially for mixed workloads or unpredictable usage.
• Governance and Policy Gaps: Despite the promise of “unified governance,” policy enforcement across workspaces, lineage tracking, and granular RBAC are still inconsistent or immature. This is particularly problematic for regulated industries and larger orgs with decentralized teams.
• Developer Experience Lags: While Fabric is friendly for analysts and BI pros, it lacks the flexibility, DevOps maturity, and SDK depth that data engineers and ML practitioners expect from modern platforms like Databricks or AWS.

Bottom Line: Fabric offers a fast on-ramp but demands vigilance. For Microsoft-native teams, it’s a strategic bet with some rough edges and a need for ongoing attention.  The manufacturing industry is known for leaning into the “simplicity” Microsoft promises with “one vendor and one data ecosystem”. However, many of these Microsoft shops are aggressively shifting to greener pastures as the Fabric pride lands under Scar diminish.

Snowflake — Simba, The Responsible Heir

Snowflake is the Simba of the data kingdom — the rightful heir to the modern data warehouse throne. Once underestimated, it has matured into a leader through steady innovation, relentless focus on simplicity, and an emphasis on governance and performance. Like Simba, Snowflake may not be the flashiest or most daring, but its loyal following comes from trust: organizations know what they’re getting — dependable performance, clean user experience, and enterprise-grade security.

Snowflake became synonymous with the modern data stack for good reason. It made analytics easy again. The platform’s separation of compute and storage, automatic tuning, and SaaS delivery model liberated data teams from infrastructure headaches.

But while Simba inherited a strong kingdom, questions remain about his readiness to rule in the AI era. Snowflake’s innovation curve — particularly in generative AI and advanced ML — has lagged behind newer entrants like Databricks. And while its product announcements at the San Francisco Summit were substantial, many felt like catch-up moves to match capabilities others had already delivered months before such as Databricks.

Where It Leads

• Frictionless Experience: Fully managed infrastructure means no clusters to manage, no knobs to tune. You load data and query — it just works.
• Strong Governance and Security: Role-based access control, object tagging, auditing, and fine-grained policies are built in — essential for regulated industries.
• Data Sharing and Marketplace: Snowflake’s native data sharing is a game-changer. Organizations can share live data across regions and clouds without ETL. The Snowflake Marketplace extends this to third-party data providers.
• Workload Segmentation with Warehouses: You can assign different virtual warehouses for different workloads (BI, ETL, data science) to avoid resource contention — a massive benefit for concurrency management.

Where It Hesitates

• Limited AI/ML Native Capabilities (Today): While Cortex and Snowpark are steps forward, native model training, fine-tuning, and vector search capabilities still trail behind Databricks or Vertex AI.
• Cost Transparency and Control: Like others, Snowflake can become expensive if workloads aren’t carefully governed. Warehouse sprawl, auto-suspend misconfigurations, and poorly optimized queries can burn compute credits quickly.
• Concurrency at Extreme Scale: Snowflake generally handles typical enterprise workloads well, but some customers pushing ultra-high concurrency or streaming use cases have found limitations.
• Slower Innovation Cadence: Compared to the rapid fire of releases from Databricks and even Microsoft Fabric, Snowflake’s pace of delivery in emerging areas (like GenAI, governance automation, and data observability) has been more measured.

Bottom Line: Snowflake is the balanced choice for organizations prioritizing governance and ease of use, growing steadily but not pushing the bleeding edge.  Snowflake offers a simple, easy to use platform but perhaps limiting for the long haul since their product development in the AI space is innovating at a slower pace. T hey do offer a strong foundation of capabilities centralized around core data and business intelligence.

Databricks — Mufasa, The King of the Data Kingdom

Databricks commands respect like Mufasa — powerful, wise, and built to handle vast, complex domains. In the cloud data safari, Databricks stands as the most complete and unified platform for data, analytics, and AI. With its Lakehouse architecture, Databricks merges the reliability and governance of data warehouses with the openness and flexibility of data lakes. It empowers organizations to unify all their data teams — analysts, scientists, and engineers — in one collaborative environment.

Databricks doesn’t just roar — it leads. It has become the go-to platform for companies looking to move beyond dashboards and into AI-driven decision-making. The introduction of tools like Unity Catalog, Lakebase, and MLflow cement Databricks as not just a compute engine but a strategic control plane for your data estate.

It’s also a platform that is deeply investing in Agentic AI, automated data pipelines, real-time streaming, and open table formats like Delta Lake — all of which reflect a maturity and focus on real-world enterprise scale and trust.

Where It Roars

• AI-Native Architecture: Databricks is built with AI in mind, making it easier to go from raw data to production ML models. With integrations like MosaicML, Foundation Model APIs, and MLflow, it’s not just an analytics tool — it’s an AI platform.
• Open and Unified Lakehouse: Delta Lake, Unity Catalog, and the Lakehouse paradigm eliminate silos between structured, semi-structured, and unstructured data. You can use one set of tools and one system of governance across all use cases.
• Collaboration Across Personas: Shared notebooks, Databricks SQL, and Delta Live Tables let engineers, analysts, and data scientists work side-by-side in the same environment. This reduces friction and accelerates outcomes.
• Cross-Cloud Portability: Available on AWS, Azure, and GCP, Databricks gives organizations flexibility while maintaining consistent architecture and governance — a key differentiator in an increasingly multi-cloud world.
• Enterprise-Ready Governance: Unity Catalog provides centralized governance across data, AI models, and notebooks — supporting lineage, RBAC, and auditability at scale.
• Performance and Cost Efficiency: The Photon engine and intelligent workload management can deliver significant cost/performance gains when tuned properly — up to 2–3x in some benchmarks.

Where It Struggles

• Steeper Learning Curve: Databricks’ depth means it can feel overwhelming for teams new to Spark, notebooks, or distributed systems. It’s a powerful engine — but you need a trained driver.
• Governance Still Maturing in Some Areas: While Unity Catalog is a major step forward, customers in highly regulated industries may still find gaps, especially when compared to more mature warehouse tools.

Bottom Line: Databricks rewards organizations that are ready to grow into AI-driven leadership. It is not the easiest platform to adopt — and like Mufasa, it demands wisdom, discipline, and strength. But for those who make the investment, the Lakehouse offers a unified path from raw data to real-time insight to intelligent action.

In San Francisco, it was clear that Databricks isn’t just talking about the future — it’s building it. From their GenAI capabilities to their commitment to openness and enterprise governance, Databricks has become the reference architecture for modern data platforms.

Summary Table

Practical Closing Thoughts — Navigating the Data Landscape

The cloud data platform market is much like a sprawling African safari — diverse, dynamic, and ever-evolving. Every platform plays a role in the Circle of Data Life, but only one roars loudest. Databricks stands apart as Mufasa — powerful, open, and wise in navigating the complex terrains of AI, engineering, and analytics.

Still, your data kingdom won’t thrive by crowning a single ruler. It flourishes when each platform plays to its strengths, like the species of the safari— some specialized, others adaptable — all contributing to a balanced, resilient ecosystem.

One major takeaway from my time in San Francisco was clear: Databricks is the most comprehensive, unified platform on the market. Its pace of innovation, enterprise maturity, and relentless customer focus are driving measurable outcomes across industries. For organizations serious about scaling AI and data productivity, Databricks is no longer a nice-to-have — it’s a strategic imperative.

And the best part? You don’t need to wait.
Start your own safari today with the newly released Databricks Free Edition — no credit card required, no cost to explore:

👉 https://www.databricks.com/learn/free-edition