Services/Data Analytics & Platforms

Data Analytics & Platforms

Build modern data lakehouses with embedded governance, metric stores, and self-service analytics that accelerate insights without sacrificing quality.

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Traditional Data Platforms Can't Keep Up

The data warehouse vs data lake debate created complexity without solving the real problem: how to deliver trusted, timely insights at scale. The lakehouse architecture finally unifies both worlds—but only if you build governance in from day one.

6+ months

Average data team backlog

10+

BI tools with conflicting metrics

40%

Data initiatives that fail

Core Capabilities

Modern Data Platform Architecture

Unified Data Lakehouse: Converge data warehouse and data lake into a single platform with ACID transactions, schema enforcement, and open formats.
✓Single source of truth for analytics
✓Open table formats (Delta, Iceberg)
✓Unified batch + streaming
Embedded Governance: Data quality, lineage, and access controls built into the platform—not bolted on after the fact.
✓Automated data lineage
✓Role-based access control (RBAC)
✓Data quality monitoring
Metric Stores: Centralized semantic layer that defines business metrics once, ensuring consistency across dashboards, reports, and ML models.
✓Single source of metric definitions
✓Version-controlled metrics
✓Consistent across tools
Self-Service Analytics: Enable business users to explore data, build dashboards, and answer questions without waiting for data teams.
✓Low-code analytics tools
✓Curated data products
✓Embedded analytics in workflows

Reference Lakehouse Architecture

Build your data platform in layers, from ingestion to consumption with governance throughout.

Ingestion

Fivetran, Airbyte, Kafka

Batch & streaming data pipelines

Storage

Databricks, Snowflake, BigQuery

Lakehouse with Delta/Iceberg tables

Transformation

dbt, Apache Spark, Dagster

SQL-based transformations, orchestration

Semantic Layer

dbt Semantic Layer, Cube, MetricFlow

Centralized metric definitions

Consumption

Tableau, Hex, Mode, Looker

Dashboards, notebooks, embedded analytics

Governance

Alation, Collibra, dbt Docs

Catalog, lineage, quality monitoring

Common Anti-Patterns to Avoid

Data Swamp

Problem: Dump everything into S3 without schema, governance, or discoverability

Fix: Implement lakehouse with schema enforcement and catalog

Tool Sprawl

Problem: 10+ BI tools, each with its own metric definitions

Fix: Consolidate on semantic layer + 2-3 consumption tools

Centralized Bottleneck

Problem: All analytics requests go through a single data team

Fix: Enable self-service with curated data products

Maturity Model

Four Levels of Data Platform Maturity

Siloed

Data scattered across tools, no shared definitions

•Manual exports
•Excel-based reporting
•No data governance

Centralized

Data warehouse with ETL pipelines, but governance is manual

•Snowflake/Redshift
•Scheduled ETL jobs
•BI dashboards

Lakehouse

Unified platform, embedded governance, metric stores

•Delta/Iceberg tables
•dbt transformations
•Automated lineage

Self-Service

Business users can discover, explore, and analyze data independently

•Data products
•Low-code tools
•Embedded analytics

Real-World Use Cases

Multi-Cloud Data Platform

Challenge: Data spread across AWS, Azure, GCP with no unified view or governance

Approach: Databricks lakehouse, dbt transformations, Alation catalog

Results:

✓Single source of truth
✓90% reduction in data pipeline incidents
✓3x faster time-to-insight

Self-Service Analytics

Challenge: Data team backlog 6+ months, business users frustrated by wait times

Approach: Curated data products, metric stores, low-code analytics tools

Results:

✓80% of queries self-served
✓Data team focus shifted to high-value work
✓User satisfaction +45 points

Ready to Build a Modern Data Platform?

Take our data maturity assessment to benchmark your capabilities and get a personalized lakehouse roadmap.

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