Data Engineering on Databricks v/s Microsoft Fabric - when to use which platform?

Choosing between Databricks and Microsoft Fabric depends on your team's skills, your infrastructure, and the type of work you're doing. Here's a quick breakdown:

• Databricks is perfect for advanced data processing, machine learning, and big data tasks. It supports multiple clouds (AWS, Azure, GCP) and is great for technical teams comfortable with coding.
• Microsoft Fabric is better for business users and organisations already using Microsoft tools. It focuses on ease of use, low-code/no-code capabilities, and seamless integration with Azure.

Key Differences:

When to Use Databricks:

• For big data workflows, machine learning, and teams with coding expertise.

When to Use Microsoft Fabric:

• For organisations prioritising ease of use, business intelligence, and Azure integration.

Both platforms excel in different areas, and in some cases, combining them might be the best solution.

Should You Start Using Microsoft Fabric Instead of Databricks?

Platform Features and Capabilities

Both platforms bring powerful tools for data engineering, each tailored to meet different organisational demands.

Databricks Core Functions

Databricks leverages Apache Spark to handle large-scale data and AI workloads efficiently. It also incorporates Delta Lake technology to ensure ACID transactions [1].

Here are some of its standout features:

Its serverles lakehouse architecture allows for efficient handling of large datasets without the need for complex infrastructure.

On the other hand, Microsoft Fabric focuses on deep integration within the Microsoft ecosystem.

Microsoft Fabric Core Functions

Microsoft Fabric emphasises integrated analytics by centralising operations through OneLake, which seamlessly connects with other Microsoft tools.

Key features include:

Fabric’s Unity Catalog Mirroring enables instant data access without requiring replication [2]. Additionally, it provides flexibility with Synapse notebooks and Data Flows, catering to both code-first users and those who prefer no-code tools.

These features highlight the unique strengths of each platform, paving the way for a detailed comparison.

Direct Platform Comparison

Feature Comparison Matrix

Here’s a breakdown of how Databricks and Microsoft Fabric stack up in key data engineering features:

Platform Advantages and Limits

The matrix highlights how each platform caters to distinct needs, shedding light on their strengths and limitations.

"Cost-efficiency in Databricks demands continual monitoring and adjustment to your specific workload." - Matt Flesch, Consultant, Analytics8 [5]

Databricks Highlights:

Databricks shines in performance optimisation with features like:

• Autoscaling to dynamically adjust cluster nodes
• Photon Engine for faster query execution
• Advanced disk caching for frequently accessed data

Microsoft Fabric Highlights:

Microsoft Fabric is cost-effective for large-scale tasks. For example, processing 1 billion rows of raw CSV data in a Fabric Notebook cost less than $0.20 [4]. This makes it a strong choice for enterprise-level data processing.

Integration Capabilities:

One standout feature is Unity Catalog Mirroring, which allows instant access to Databricks managed data from Fabric workloads without the need for replication [2].

Performance Considerations:

Choosing between these platforms often comes down to your team’s expertise and infrastructure. Databricks is ideal for teams skilled in coding and looking for high-performance tools, while Microsoft Fabric is better suited for organizations that value ease of use and integrated analytics. Each platform’s unique capabilities make them effective for different scenarios.

Platform Applications

Building on the core functions mentioned earlier, here's a breakdown of when each platform works best.

When to Use Databricks

Databricks is ideal for advanced, code-driven data processing tasks. Its primary use cases include:

• Handling complex big data workflows using optimised Apache Spark
• Running machine learning projects with MLflow
• Meeting custom infrastructure and data residency needs
• Supporting development teams skilled in Python, Spark SQL, or Scala
• Integrating CI/CD pipelines with Git and DevOps tools
• Fine-tuning performance at detailed levels

"If your data team consists of experienced professionals, then Databricks will be the choice for you." - Neudesic [2]

With its PaaS model, Databricks offers extensive control over infrastructure. This makes it a great fit for teams needing tailored data engineering environments. However, if your organisation values integrated analytics and ease of use over this level of control, Microsoft Fabric might be a better fit.

When to Use Microsoft Fabric

Microsoft Fabric is designed for organisations seeking streamlined, user-friendly analytics, especially those already leveraging Microsoft tools.

Key use cases include:

• Seamless integration with Azure services and unified analytics
• Quick deployment of analytics with minimal coding
• Transitioning to SQL-based data warehouses
• Managing container orchestration
• Supporting business-focused data operations

As a SaaS platform managed by Microsoft, Fabric shifts the focus away from infrastructure management to analytics. Features like Dataflow Gen2 make data ingestion and transformation accessible to both technical teams and business users.

"Microsoft Fabric serves as a user-friendly all-in-one analytics platform leveraging Azure technologies, ideal for business users, while Databricks excels in big data processing and machine learning across major cloud providers, catering to more technical data professionals." - Neudesic [2]

This comparison lays the groundwork for exploring how to align platform capabilities with your organization's specific requirements.

Making the Right Choice

Choosing between Databricks and Microsoft Fabric depends on your organisation's specific needs, resources, and goals. Below, we break down key factors to consider and how you can access expert help for implementation.

Selection Criteria

Here’s a comparison of how each platform fits different requirements:

Databricks' usage-based pricing is great for workloads that fluctuate, while Microsoft Fabric offers predictable costs, starting at 2 CUs for $262.80 per month [1].

"Depending on specific requirements, a combined model that leverages the unique strengths and capabilities of Fabric and Databricks may be practical." - Neudesic [2]

Both platforms meet strict security standards. Databricks provides detailed control through Unity Catalog, while Microsoft Fabric integrates with Azure Defender and Microsoft 365 security tools [3].

Finding Expert Freelancers on Talentblocks

[Image placeholder: Talentblocks]

Proper implementation is crucial to align platform capabilities with your goals. Talentblocks makes it easy to find skilled professionals for your project needs:

• Skill-Based Matching: Use Talentblocks’ filters to connect with freelancers experienced in Databricks or Microsoft Fabric.
• Flexible Engagement Models: Weekly time blocks let you scale support, whether you need platform selection advice or full implementation help.
• Transparent Pricing: Clear costs based on expertise and project duration, with streamlined payment options.

Here are some tips for maximising success during implementation:

• For Microsoft Fabric: Leverage Dataflow Gen2's connectors for quicker data ingestion.
• For Databricks: Use SQL endpoints to integrate with Power BI.
• For Hybrid Setups: Combine Fabric for data ingestion and Databricks for complex processing tasks.

Conclusion

Microsoft Fabric provides an integrated analytics platform tailored for organisations using Azure, while Databricks stands out with its advanced data processing, machine learning capabilities, and support for multi-cloud environments.

When it comes to pricing, Microsoft Fabric offers a capacity-based model for cost predictability, whereas Databricks uses a usage-based approach, allowing costs to scale with workloads. To choose the right platform, consider factors such as your team’s expertise, infrastructure needs, budget, security priorities, and future growth plans.

For many businesses, combining the strengths of both platforms can be the most effective strategy. However, success depends on customizing the solution to match your specific goals. Carefully evaluate the criteria outlined above to ensure your platform aligns with your organisation’s needs. If you’re looking for expert guidance, Talentblocks connects you with skilled freelancers who can fine-tune your implementation and help you get the most out of your chosen platform.

As Dhruba Borthakur, co-founder and CTO of Rockset, notes:

"In choosing a data analytics platform, it is important to think through the full spectrum of analytic and AI use cases you'll need to support both now and in the future." [6]

FAQs

How should I choose between Databricks and Microsoft Fabric if my organisation uses multiple cloud providers?

If your organisation operates across multiple cloud platforms, the decision between Databricks and Microsoft Fabric depends on your specific use case and priorities. Databricks is cloud-agnostic, offering seamless support for major providers like Azure, AWS, and Google Cloud. This makes it a strong choice for organisations needing flexibility across different cloud environments.

On the other hand, Microsoft Fabric is deeply integrated with Azure technologies, making it ideal for businesses heavily invested in the Azure ecosystem. If your workflows require both platforms, they can be integrated effectively - data can be stored in Azure Data Lake Storage (ADLS) or OneLake and accessed through either tool. Consider your organisation's existing infrastructure and long-term goals to determine the best fit for your needs.

What are the benefits of combining Databricks and Microsoft Fabric for a hybrid data engineering approach?

Integrating Databricks and Microsoft Fabric can unlock significant advantages for hybrid data engineering solutions. By using both platforms, organisations can harness Databricks' scalability and advanced data processing capabilities alongside Microsoft Fabric's user-friendly tools and governance features. This combination allows teams to address both technical and non-technical requirements effectively.

A hybrid approach enhances flexibility and scalability, enabling technical teams to manage complex workloads in Databricks while empowering less technical users to work seamlessly in Fabric. Additionally, it supports secure data management with fine-grained access controls, ensuring compliance and data protection. Together, these platforms provide a balanced, cost-efficient solution tailored to diverse business needs.

How do Databricks and Microsoft Fabric compare in terms of cost-effectiveness for different workloads?

Databricks uses a pay-as-you-go pricing model, where costs are based on the computational resources consumed, measured in Databricks Units (DBUs). This flexibility allows businesses to scale expenses based on their workload demands, making it a cost-effective choice for variable or unpredictable workloads.

Microsoft Fabric also offers a pay-as-you-go model but includes a minimum starting cost of approximately $300 per month, which provides 2 Compute Units (CUs). Additionally, Microsoft Fabric offers a reserved instance pricing option, enabling users to lock in lower rates by committing to resource usage for a year. This approach can result in cost savings of up to 40% compared to on-demand pricing, making it a better fit for consistent, long-term workloads.

When choosing between the two, consider the variability of your workloads and whether your project benefits more from flexible scaling or predictable, reserved pricing.