Top 5 Databricks Alternatives You Must Know in 2024

As per Forbes Advisor 2024 stats, 64% of businesses expect AI to increase their overall productivity, demonstrating the growing confidence in AI's potential to transform business operations. Having said that, Databricks platform is one of the leading choice for enterprises for its powerful data engineering and machine learning capabilities.

However, with the rise of no-code and low-code tools featuring data analytics, integrations, and automated data workflows, AI is now accessible to anyone. There are more than plausible tools to compete with it.

This blog will cover the top five alternatives to Databricks—MarkovML, Dataiku, Snowflake, Azure Synapse, and Google BigQuery—and why you should consider them for your business.

Let's get started!

But before that, why should you even look for Databricks alternatives?

Reasons to Look for Databricks Alternatives in 2024

• Cost Structure: Databricks can be expensive, especially for small businesses and those with limited budgets. Many platforms offer flexible pricing, like pay-as-you-go, better suited for smaller teams or variable workloads.
• Deployment Flexibility: Databricks have a bit of a learning curve and can be complex and difficult to set up.
• Scalability: Databricks can sometimes struggle with scaling tasks that depend on each other, slowing down performance. However, proper planning with features like Delta Lake and autoscaling can improve efficiency for large workloads.
• User Interface/Experience: Databricks has a steep learning curve, and its notebooks can feel clunky to new users.
• Machine Learning Capabilities: Databricks may run into memory issues when processing large datasets with certain tools like Spark MLlib, which can affect model training and performance. Some applications may also experience slower performance.
• Integrations: Due to their complexity, some users find it challenging to integrate Databricks with their existing systems and have raised security concerns, which can increase cost and create governance issues.
• Multi-Cloud Support: Databricks can be harder to use in multi-cloud environments, as separate instances are needed for each cloud. This increases operational complexity and may lead to data consistency issues.

Top 5 Alternatives of Databricks

1. MarkovML - Agentic AI Platform for Enterprise

Why choose MarkovML over Databricks

• No-Code Interface: MarkovML provides a no-code interface, making it easy for anyone to use AI to grow their business.
• Data Analytics: MarkovML allows you to interact with your data in plain English, visualize data insights, and build reports.
• AI and ML Capabilities: MarkovML's no-code AI Workflows come with pre-built AI and machine learning operators to perform ML actions on your data.
• Generative AI Capabilities: MarkovML integrates with leading LLMs like OpenAI and Google Gemini, making it perfect for AI-driven applications.
• Secure and Collaborative: MarkovML provides a secure, collaborative environment for teams to easily build and manage projects while keeping data safe and fully compliant.

MarkovML vs Databricks

Who should choose MarkovML

Businesses looking for a unified, no-code AI platform that simplifies data analytics, visualization, labeling, and other data actions, such as AI workflow automation, building Gen AI applications, and more, all without coding knowledge should choose MarkovML.

To learn more, check out our products and solutions.

Who should choose Databricks

• Companies who need more advanced data engineering capabilities with a focus on large-scale data processing and AI.

2. Dataiku

Why choose Dataiku over Databricks

• Ease of Use: Dataiku offers a more user-friendly interface, making it accessible for users with limited coding experience.
• End-to-End Integration: Dataiku is a unified platform that supports the entire data pipeline, from data preparation to machine learning.
• Bi-directional Deployment: Dataiku allows users to deploy models trained in Databricks such as MLFlow models and vice versa, enabling seamless collaboration between data scientists and engineers. Databricks has limited support for model deployment and management.
• Integration with Databricks: Dataiku's partnership with Databricks enables users to leverage the strengths of both platforms, combining the scalability and performance of Databricks with the ease of use and model management capabilities of Dataiku.

Dataiku vs Databricks

Who should choose Dataiku

• Businesses looking for a user-friendly platform with strong collaboration features and full data pipeline integration.

Who should choose Databricks

• Companies that need a platform focused on data engineering and machine learning at scale.

3. Snowflake

Why choose Snowflake over Databricks

• Data Warehousing: Snowflake is optimized for structured data and is well-suited for data warehousing and business intelligence workloads.
• Separation of Storage and Compute: Snowflake allows independent scaling of storage and compute resources, offering better cost optimization.
• Scalability: Snowflake offers independent scaling of storage and compute resources, allowing it to handle large workloads and scale up or down as needed.
• Multi-Cloud Support: It runs on multiple cloud platforms (AWS, Azure, GCP), providing flexibility in cloud strategies.
• Real-time data support: Snowflake offers real-time data support through its Snowpipe feature, allowing for continuous data ingestion and processing

Snowflakes vs Databricks

Who should choose Snowflake

• Organizations focused on data warehousing and cloud flexibility with independent scaling of resources.

Who should choose Databricks

• Businesses who needs an end-to-end platform for data engineering and machine learning.

4. Google BigQuery

Why choose Google BigQuery over Databricks

• Fully Managed Service: BigQuery is a serverless data warehouse that requires no infrastructure management.
• **Real-Time Analytics:**BigQuery's columnar and ANSI SQL databases enable fast analysis of petabytes of data, making it suitable for real-time analytics and reporting.
• Integration with Google Suite: BigQuery seamlessly integrates with Google Analytics and Google Data Studio, making it a convenient choice for businesses already using Google's ecosystem.
• Cost Efficiency: With its pay-as-you-go pricing, BigQuery is cost-effective for both small and large businesses.

Google BiqQuery vs Databricks

Who should choose Google BigQuery

• Businesses that want a fully managed, cost-efficient solution for real-time analytics without complex infrastructure management.

Who should choose Databricks

• Organizations looking for a robust platform for data engineering and advanced analytics.

5. Azure Synapse

Why should you use Azure Synapse over Databricks

• Unified Analytics Platform: Azure Synapse combines data integration, data warehousing, and big data analytics into one service.
• Open-source Apache Spark: Azure Synapse Analytics comes with open-source Apache Spark, which provides users with more flexibility and customization options.
• .NET Support: Azure Synapse Analytics has built-in support for .NET for Spark applications, making it a better choice for developers already invested in the .NET ecosystem.
• Microsoft Ecosystem Integration: It integrates seamlessly with Microsoft tools like Power BI and Azure ML, making it ideal for enterprises in the Microsoft ecosystem.
• Security and Compliance: Advanced security features and compliance capabilities help protect sensitive data.

Azure Synapse Analytics vs Databricks

Who should choose Azure Synapse

• Large enterprises already invested in .net ecosystem, requiring robust security features and Data warehouses and interface tools with Seamless Data Lake integration

Who should choose Databricks

• Companies focused one faster query performance and better data governance, GPU-enabled clusters for Machine Learning workloads and Streamlined ML workflows with Git integration.

Conclusion

While Databricks is a robust solution for large-scale data engineering and machine learning, the alternatives each offer unique strengths that cater to different business needs.

MarkovML stands out for its no-code AI workflows, automated EDA, data analytics, and ease of use, making it ideal for businesses looking to simplify AI deployment. Dataiku and Google BigQuery provide user-friendly interfaces and real-time analytics for teams that want accessible yet powerful tools. Snowflake and Azure Synapse are strong contenders for enterprises focused on cloud data warehousing and integrated analytics.

Selecting the best platform depends on your organization's priorities—whether it's ease of use, scalability, or advanced machine learning capabilities. You may choose MarkovML.

Book a call to learn more about what Markov has to offer!