5X vs GBQ: Who wins the data readiness game?

The most crucial metric for data teams isn't the volume of data collected, query execution speed, or pipeline uptime. It's data readiness.

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Data readiness means having clean, structured, and centralized modeled data readily available for traditional BI, advanced analytics, data activation, and now increasingly more for AI. It’s the cornerstone for powering the entire Gen AI and LLM world. If you don't understand your data, neither will your AI.

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This article compares 5X with GBQ from a data readiness perspective on the platform level, rather than as a data warehouse (GBQ is actually great as a warehouse). 

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The five layers of a data-ready system are:

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1. Ingestion

2. Warehouse

3. Modeling 

4. Orchestration

5. Business Intelligence

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So how do 5X and GCP stack up on these? Let’s find out!

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Google Cloud Platform

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Ingestion

Limited automatic ingestion with Google Analytics and native ingestion solutions

Warehouse

BigQuery offers excellent storage and compute capabilities

Modeling

- Basic capabilities to run & schedule jobs using Dataform
- spark-bigquery-connector supports reading BigQuery tables into Spark's DataFrames
- Offers limited customization options

Orchestration

- Requires additional setup for full pipeline orchestration
- Can be difficult to configure and manage
- Basic development environment

Business intelligence

Full coverage with Looker Studio and Enterprise Looker

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Google ecosystem covers warehousing and BI but you need separate tools for ingestion, modeling, and orchestration to achieve data readiness.

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How 5X complements the Google Cloud ecosystem

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Ingestion

- Built on top of industry-standard tools like Fivetran and Gravity
- Offers Fivetran’s enterprise-grade connectors
- Build custom connectors in days with Gravity Data
- Will soon support Apache Iceberg Tables

Warehouse

- Can work on top of BigQuery
- Also works with multiple other warehouses, including Snowflake, Redshift, and Databricks

Modeling

- Uses dbt for enterprise-grade modeling with unique simplicity
- Support for SQL, Python, Notebooks

Orchestration

- Offers Dagster to ship pipelines quickly with 1-click scheduling
- Prebuilt templates to accelerate dev time

Business intelligence

- Provides Superset as an inbuilt option in the platform
- Deep integrations and provisioning Power BI, Looker, Sigma and Tableau directly from 5X

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GBQ vs 5X: A comparison on data readiness level

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Other considerations

Total cost of ownership (TCO)

The cost of building pipelines in GCP can add up quickly. Multiple services for different ingestion methods (Dataflow, Pub/Sub, etc.) can lead to unexpected charges. Plus, separate tools for ingestion, modeling, and orchestration further add to the overall cost.

5X's integrated platform typically reduces TCO by 30-50% compared to a piecemeal approach.

Integrated services offering

5X’s integrated services are approximately 25% of the cost of US-based consultancies and 70% of the cost of building and scaling an in-house team in America.

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What next?

Google Cloud is making strides in AI with new features and tools like:

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• Vertex AI - fully-managed AI development platform
• ML functions - using trained ML models directly within SQL queries
• AutoML Tables - automates building and deploying ML models on tabular data, and
• Federated learning - AI model training across multiple datasets without centralizing data

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But to fully leverage AI, your data must be clean and well-organized. It needs a data readiness platform that excels in ingestion, modeling, orchestration, and business intelligence, areas that BigQuery doesn’t fully address.

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To get the best of both worlds, use 5X on top of GBQ. This allows you to use GBQ’s warehousing power and 5X’s data readiness tools and features.

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