5X vs Azure: The Hunt for Core Data Readiness

Azure Data Platform is Microsoft's cloud service for handling all things data. It covers storage, integration, databases, and analytics. You have services like Azure Data Lake Storage for storage, Azure Data Factory for moving and transforming data, and databases like Azure SQL and Cosmos DB.

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Azure is now making it easier to use AI with its data. They've added Azure OpenAI and Azure AI Studio that let you build your own copilot and generative AI apps. Plus, they've built an arm-based virtual machine called Azure Cobalt to run general-purpose and cloud-native workloads. 

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But what about the core data readiness?

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The true measure of a data platform isn't query speed, storage capacity, or cool AI features; it's data readiness. Clean, structured, and centrally modeled data is the fuel for BI, advanced analytics, data activation, and increasingly, AI use cases. A solid data foundation is crucial for AI and LLMs to deliver accurate and valuable insights. You may have all the AI power, but without clean, accessible data, your models are just as good as their input. 

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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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How does Azure measure up against these layers of a data readiness platform? Let's find out.

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Azure

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Ingestion

• Offers Azure Data Factory to create ingestion pipelines. Supports connectors for databases, files, SaaS applications. Offers incremental load, data profiling, and cleansing. Not ideal for complex data pipelines, limited real-time processing compared to dedicated streaming platforms.
• Provides Azure Event Hubs for real-time data ingestion. Used primarily for streaming data, not ideal for batch ingestion.
• Azure Stream Analytics for real-time analytics on streaming data. Limited in terms of complex data transformations compared to Spark.

Warehouse

• Azure Synapse Analytics combines data warehousing and data lake capabilities. Supports SQL-based analytics and Spark for big data processing. Offers dedicated SQL pools for high-performance data warehousing.
• Azure SQL Database for relational database needs, often used as a data mart.
• Heavy reliance on Azure infrastructure can restrict flexibility and increase costs.
• Doesn’t match the performance of dedicated data warehouses for complex OLAP workloads.

Modeling

• Supports SQL, Python, and Scala for data manipulation and analysis. Offers MLlib for ML algorithms.
• Azure Synapse Analytics provides data modeling through SQL views and stored procedures.
• Lacks out-of-the-box table and column-level data lineage.
• Uses Spark SQL for complex transformation and processing, and DataFrames for manipulation.
• Lacks an enterprise-grade modeling tool like dbt natively.

Orchestration

• Offers Azure Data Factory to orchestrate data pipelines, schedule workflows, and manage dependencies.
• Provides Azure Logic Apps for building cloud-based application workflows with connectors to various services.
• More complicated to manage (schedule and orchestrate) data pipelines as it uses multiple tools.
• Struggles with complex, dynamic, or branching workflows.

Business Intelligence

• Offers integrations with Power BI and Power BI Embedded.
• Lacks integrations with other popular BI tools like Tableau, Looker, Sigma, etc.

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How 5X complements Azure

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Ingestion

• Offers 500+ pre-built connectors from all of the most used data sources.
• Hours, day implementations for custom connector development for the long tail of connectors.
• Simplifies handling incremental data updates for scenarios requiring near real-time data pipelines.
• Support for Apache Iceberg Tables in S3 or other flat storage.

Warehouse

• Works with multiple cloud warehouses like GBQ, Snowflake, Redshift, and Databricks.

Modeling

• Integrates with dbt for enterprise-grade data modeling.
• Offers features like lineage tracking, version control, and modular transformations.
• Supports SQL, Python, and notebooks for transformation flexibility.
• Offers table and column-level data lineage.

Orchestration

• Offers Dagster to ship pipelines quickly with 1-click scheduling.
• Enterprise grade scheduling and DAGS with easy-to-use UI.
• Prebuilt templates to accelerate dev time.
• Easier to manage pipelines in a unified workspace.

Business intelligence

• Compatible with any BI tool.
• Provides 5X BI as an inbuilt option in the platform.
• Deep integrations and provisioning of Power BI, Looker, Sigma and Tableau from 5X.

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Azure vs 5X: A comparison on core data readiness

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Other considerations

Total cost of ownership (TCO)

Azure

Azure's cost structure can be complex with multiple service offerings like Azure Synapse Analytics, Azure Data Factory, Azure Analysis Services, and Power BI having its own pricing model. This can make it challenging to predict and control costs.

5X

Consolidates all functionalities into a single platform. This eliminates the need for multiple tools and associated costs. This integrated approach can reduce TCO by 30% through simplified billing, reduced infrastructure, and operational efficiencies.

Integrated services

Azure

Azure offers a comprehensive suite of integrated services, including compute, storage, networking, analytics, and AI.

The cost of integrating these services can add up, as each service comes with its own pricing model. Plus, using Azure requires expertise for implementation and optimization, which can incur additional costs if external consultants are needed.

5X

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.

Databricks on Azure

Azure also offers the Azure Databricks analytics platform. However, when using Databricks on top of Azure, there are a few caveats to keep in mind:

• Requires strong technical skills for setup; lacks drag-and-drop or GUI-based tools.

• Offers basic built-in visualization; often needs external BI tools for advanced visuals.

• Since it’s cloud-based, resource management becomes crucial to avoid extra costs.

• Integrating with Azure and non-Azure services can be complex and time-consuming.

• Has a steeper learning curve compared to Synapse, especially for teams new to Spark.

If you prefer Databricks, it’s wise to consider using it with 5X for more streamlined data readiness and additional support. For a deeper comparison, check out our 5X vs Databricks article.

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Wrapping up

Azure is one of the most complete full-stack data platforms that is making strides in AI and ML with Azure AI and Cobalt launches. But some of the layers of core data readiness (ingestion, modeling, and orchestration) aren’t mature yet. 

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To address these gaps and solidify your data readiness, use 5X with Azure. Using 5X on top of Azure, you can build a scalable data platform that addresses the entire data lifecycle, from ingestion to consumption. This way, you can maximize the value of Azure's core strengths while overcoming its limitations in data readiness.

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