2025's Hottest Modern Data Platforms

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Dear Reader…

The data engineering world is undergoing seismic shifts. With the volume of data doubling every two years, AI integration, and demand for real-time insights, investing in the right data platform has become mission-critical. This week we will dissect the five platforms redefining modern data architecture in 2025, to help you navigate the array of choices for cloud data warehouses.

The Evolution of Data Platforms: Why “Modern” Matters?

Before diving into specifics, let’s contextualise the "modern" in modern data platforms. Traditional on-premises systems—think Oracle or SQL Server—are increasingly giving way to cloud-native solutions prioritising scalability, flexibility, and cost efficiency. The data engineering demands in 2025 have morphed such that these features are now considered “table stakes” with the requirement to:

• Decouple storage and compute for elastic scaling

• Support both batch and streaming workflows

• Integrate natively with AI/ML tools

• Enable real-time analytics without ETL bottlenecks

This paradigm shift is driven by what industry analyst Tomasz Tunguz calls "The Great Consolidation", where enterprises are slashing fragmented toolchains in favour of unified platforms.

🤔 Who are the dominant players?

It is worth noting the difference in total market vs adoption rates - Databricks stands out as the hottest current platform, whereas Snowflake the most dominant in terms of market share - even though they have been a player for little more than a decade.

Some Honourable Mentions to Watch:

• Firebolt: 3x faster than Redshift for real-time analytics but limited to 1% market share due to niche use cases.

• Oracle Exadata: Retains 8% share in finance/healthcare but struggles with cloud migration costs.

Let’s see who’s leading this charge combining these two measures…

1. Snowflake: The Cloud-Native Powerhouse

Market Share: Maintains ~35% of the cloud data warehouse market, with 80% of deployments on AWS, 18% on Azure, and 2% on GCP.

Key Innovation: Zero-copy cloning + cross-cloud governance.
Snowflake continues dominating enterprise data warehousing by perfecting the cloud trifecta:

• Instant elasticity: Spin up XL warehouses for ETL bursts, downgrade for light queries

• Secure sharing: Live data exchanges between partners without replication

• Python-native: Snowpark ML streamlines model deployment directly on stored data

A Reddit user managing 80M customer records reports: "Snowflake delivers targeted marketing segments in 8–10 seconds vs Redshift’s 30–40 seconds—even on small clusters". Snowflake’s new Unistore engine now handles transactional workloads, challenging traditional OLTP databases.

Modern Data Edge: Snowflake’s acquisition of Streamlit empowers low-code ML app deployment, making it a full-stack analytics OS.

2. Databricks: The Lakehouse Visionary

Market Share: ~5% market share but growing at 45% YoY due to lakehouse adoption

Key Innovation: Delta Lake 3.0 + Unity Catalog
Databricks’ lakehouse architecture merges data lakes’ flexibility with warehouses’ performance:

• Delta Sharing: Open protocol for real-time data products

• Photon Engine: C++-accelerated Spark SQL cuts query times by 4x

• MLFlow Integration: End-to-end experiment tracking from Jupyter to production

Users migrating from Synapse note: "Databricks halved our nightly pipeline runtime despite 2x data growth—and Spark UI makes optimisation intuitive". The recent Delta Live Tables update introduces declarative pipeline orchestration, challenging Airflow’s dominance.

Modern Data Edge: Native integration with OpenAI’s API enables AI-augmented ETL—think automatic schema matching for CSV ingestions.

3. AWS Ecosystem: The Modular Colossus

Market Share: AWS Redshift holds ~20% market share, primarily among AWS-centric organisations

Key Innovation: Athena Federation + Redshift Serverless
Amazon’s strength lies in customisable stacks for instance:

• S3 as Data Lake: 2¢/GB storage with Iceberg/Delta Lake support

• Redshift RA3 Nodes: 64TB RAM clusters with AQUA caching

• Glue Flex: Pay-per-DPU ETL with 10-second billing increments

AWS’s modularity shines through its Cloud Development Kit (CDK), which enables teams to decompose complex systems into reusable, independent components. This approach exemplifies how modular design principles translate to tangible operational advantages. A healthcare analytics company reportedly leveraged CDK’s modularity to build a machine learning pipeline:

This decomposition enabled:

• Reusable Components: The DataPreprocessingStack was reused across NLP and imaging pipelines, reducing duplicate code.

• Granular Monitoring: Each stack published CloudWatch metrics, allowing anomaly detection in data ingestion without alert fatigue.

• Cost Attribution: Billing reports per stack revealed the inference layer consumed 60% of budgets, prompting GPU instance right-sizing.

Modern Data Edge: Bedrock Integration allows natural language querying via Amazon Q e.g. "Show sales trends for vegan products in Q2" becomes a SQL query.

4. Google BigQuery: The AI-Native Contender

Market Share: Commands ~28% market share, dominating GCP-centric enterprises

Key Innovation: BigQuery ML + Gemini Integration
BigQuery leverages Google’s AI pedigree through:

• BigQuery Studio: Unified SQL/Python IDE with Colab integration11

• Analytics Hub: Live dataset marketplace with 150+ public sources11

• Logica: Prolog-inspired logic programming for recursive queries

Users praise its geospatial prowess: "Processing 1B location pings costs $12 vs $80 on Redshift—critical for our logistics optimisation". The Omni layer now queries S3/Azure data without cross-cloud fees.

Modern Data Edge: BigQuery Data Canvas uses genAI to autocomplete pipelines from natural language prompts.

5. Microsoft Fabric: The Integrated Challenger

Market Share: ~12% market share, growing rapidly in enterprises using Power BI and Dynamics 365.

Key Innovation: OneLake + DirectLake Mode
Microsoft’s answer to platform sprawl combines:

• Power BI DirectLake: Enabling you to query delta tables without imports

• Data Activator: With event-driven triggers for real-time alerts

• Copilot Integration: And GitHub-style AI pair programming for Spark

Early adopters report: "Fabric cut our ETL dev time 40% — having SQL Endpoint/Lakehouse in one platform prevents context switching". However, some concerns linger about maturity versus Databricks.

Modern Data Edge: Purview Integration automates sensitivity labels across Power BI, Excel, and Teams—a governance game-changer.

🔝 Comparative Insights: Choosing A Platform

Some key considerations we have identified, from comparisons and reports in forums for you to keep in mind, as you think about architecting Cloud Data Warehouses:

Directionally we also see a future where Tunguz’s "Agentic Data" concept is materialising, this where platforms embed AI agents for:

• Auto-optimization: Snowflake’s Search Optimization Service tunes without DBA input

• Anomaly Detection: Databricks’ Lakehouse Monitoring flags schema drifts

• Cost Control: BigQuery’s Active Assist recommends partitioned tables

Simultaneously, toolchain consolidation is accelerating. The average stack shrank from 22 tools in 2023 to 14 today.Expect platforms to expand vertically—Snowflake’s acquisition of Streamlit and Microsoft’s Power BI-Dataverse links exemplify this trend.

🙀 Beyond the Hype…

Clearly, choosing a platform isn’t about chasing features—it’s about aligning with organisational DNA, at a high-level we assess platforms to be aligned in this way:

1. AWS for cloud-agnostic flexibility

2. Snowflake for governed analytics at scale

3. Databricks for ML-driven organisations

4. BigQuery for AI/geo-heavy workloads

5. Fabric for Microsoft-centric shops

As Reddit’s data engineers caution: "Modern doesn’t mean abandoning Kimball—it means automating drudgery so we focus on insights". The winning platforms will balance innovation with practicality, turning data chaos into boardroom advantage. Stay curious, but stay critical—2025’s shiny tool might be 2026’s technical debt.

That’s a wrap for this week

Happy Engineering Data Pro’s