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5X vs GBQ: Who wins the data readiness game?
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The most crucial metric for data teams isn't the volume of data collected, query execution speed, or pipeline uptime. It's data readiness.
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.
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).
The five layers of a data-ready system are:
1. Ingestion
2. Warehouse
3. Modeling
4. Orchestration
5. Business Intelligence
So how do 5X and GCP stack up on these? Let’s find out!
Google Cloud Platform
Google ecosystem covers warehousing and BI but you need separate tools for ingestion, modeling, and orchestration to achieve data readiness.
How 5X complements the Google Cloud ecosystem
GBQ vs 5X: A comparison on data readiness level
Other considerations
What next?
Google Cloud is making strides in AI with new features and tools like:
- 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
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.
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.
Building a data platform doesn’t have to be hectic. Spending over four months and 20% dev time just to set up your data platform is ridiculous. Make 5X your data partner with faster setups, lower upfront costs, and 0% dev time. Let your data engineering team focus on actioning insights, not building infrastructure ;)
Book a free consultationHere are some next steps you can take:
- Want to see it in action? Request a free demo.
- Want more guidance on using Preset via 5X? Explore our Help Docs.
- Ready to consolidate your data pipeline? Chat with us now.
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