Best Databricks Alternatives and Competitors in 2025

Databricks has redefined data infrastructure with its unified lakehouse platform — combining data engineering, analytics, machine learning, and governance into one collaborative workspace. It’s built on Apache Spark and Delta Lake and supports notebooks, SQL, Python, and real-time data workloads. Databricks is widely used by data engineers, analysts, and data scientists for everything from ETL to ML model deployment.

However, in 2025, many teams are looking for Databricks alternatives that offer lower costs, simpler workflows, deeper SQL support, or less lock-in. Some want a platform focused purely on lakehouse querying, others prefer open-source tools, or solutions better aligned with their cloud ecosystem (AWS, GCP, Azure). Whether you’re scaling data science or simplifying analytics, strong Databricks competitors are available across open-source, SaaS, and hybrid stacks.

Here are the top Databricks alternatives and competitors to consider in 2025 for modern data engineering, analytics, and AI workflows.

What is Databricks?

Databricks is a cloud-native data and AI platform built around Apache Spark, Delta Lake, and MLflow. It supports ETL, data exploration, machine learning model training, and production deployment through collaborative notebooks and managed compute clusters. Databricks offers Delta Live Tables, SQL endpoints, Unity Catalog for governance, and integrated MLOps capabilities. It is available across AWS, Azure, and GCP. While it’s powerful, some teams find it expensive, complex to manage, or more than they need for pure analytics use cases.

Why Look for Databricks Alternatives?

1. High Cost at Scale: Databricks pricing can grow quickly with heavy workloads, especially if you’re using premium features or autoscaling clusters.

2. Steep Learning Curve: Despite UI improvements, Databricks still requires comfort with notebooks, Spark, and configuration tuning — which can slow onboarding.

3. Not SQL-First: While SQL endpoints exist, Databricks is primarily optimized for Python/Scala/ML workflows. Pure SQL users may prefer alternatives built for analytics teams.

4. Vendor Lock-In: Databricks’ proprietary enhancements (Delta Live Tables, Unity Catalog) may tie teams into its ecosystem more tightly than desired.

5. Better Lakehouse Simplicity Elsewhere: Tools like Dremio or Starburst offer easier access to data lakes without managing clusters or learning Spark APIs.

Top Databricks Alternatives (Comparison Table)

#	Tool	Open Source	Best For	Deployment
#1	Dremio	Yes	SQL-first lakehouse queries	Cloud / Self-hosted
#2	Snowflake	No	Cloud-native data warehousing	Cloud
#3	Amazon EMR	Yes	Spark on AWS infrastructure	Cloud
#4	Azure Synapse Analytics	No	Spark + SQL on Azure	Cloud
#5	Google BigQuery	No	Serverless SQL analytics	Cloud
#6	Starburst	Yes	Trino-based data lake querying	Cloud / Hybrid
#7	Dataiku	No	Low-code AI & analytics	Cloud / On-prem
#8	Apache Spark (Standalone)	Yes	Self-managed Spark workloads	Self-hosted
#9	Vertica	No	High-speed analytics at scale	Cloud / On-prem
#10	ClickHouse	Yes	Real-time OLAP analytics	Cloud / Self-hosted

10 Best Alternatives to Databricks

#1. Dremio

Dremio is a lakehouse query engine that enables lightning-fast SQL queries directly on object storage (S3, ADLS). It offers a self-service semantic layer, caching, and Apache Arrow-based performance. Ideal for replacing Databricks for analytics without Spark overhead.

Features:

• SQL engine for data lakes
• Apache Iceberg + Delta Lake support
• Data reflections for performance
• BI tool integration (Tableau, Power BI)
• Open-source and enterprise editions

#2. Snowflake

Snowflake is a fully managed cloud data platform known for its scalability, separation of compute and storage, and ease of use. It’s ideal for teams replacing Databricks with pure SQL workloads and fast onboarding.

Features:

• Cloud-native + multi-cloud support
• Auto-scaling compute clusters
• Support for structured + semi-structured data
• SQL-based transformations and BI
• Native governance and time travel

#3. Amazon EMR

Amazon EMR runs Hadoop, Spark, Hive, and Presto on AWS infrastructure. It’s a natural Databricks replacement for teams that want more control over open-source frameworks on AWS.

Features:

• Managed Spark and Hadoop stack
• Works with S3, Glue, Athena
• Flexible instance types (spot, autoscaling)
• Integrates with SageMaker, Redshift
• Pricing based on EC2 resources

#4. Azure Synapse Analytics

Synapse is a unified analytics platform that supports Spark pools, SQL-on-demand, pipelines, and BI integrations. It’s a solid Databricks alternative for Azure-first teams building end-to-end analytics workflows.

Features:

• Unified SQL + Spark workspaces
• Built-in orchestration + Data Factory
• Power BI and Azure ML integration
• Delta Lake and Parquet support
• RBAC and managed VNET support

#5. Google BigQuery

BigQuery is a serverless data warehouse built for real-time, SQL-based analytics. It offers blazing-fast queries and auto-scaled performance without the need for cluster tuning — perfect for replacing Databricks for analytics-heavy teams on GCP.

Features:

• SQL-only, serverless interface
• Integration with Looker, AI Platform
• Streaming ingestion and partitioning
• Pay-per-query or flat-rate pricing
• Data sharing and security tools

#6. Starburst

Starburst is a data lake query engine based on Trino, built for high-speed federated SQL queries across heterogeneous sources like Iceberg, Delta Lake, and Hive. It offers a modern alternative to Databricks for teams avoiding Spark complexity.

Features:

• Federated SQL on data lakes
• Open formats: Iceberg, ORC, Parquet
• Starburst Galaxy (SaaS version)
• Security + RBAC support
• Data mesh-ready architecture

#7. Dataiku

Dataiku is an end-to-end data science platform that balances no-code workflows and Python-based development. It replaces Databricks for business teams collaborating on ML with analysts and engineers.

Features:

• No-code + notebook hybrid UX
• AutoML, visualization, and pipelines
• Project versioning and Git integration
• Role-based security and approvals
• Cloud or on-prem deployments

#8. Apache Spark (Standalone)

If you want to run Spark without Databricks’ managed services, you can deploy Apache Spark manually or via managed clusters like EMR, GKE, or Kubernetes. This is ideal for cost-conscious teams wanting full control.

Features:

• Batch + streaming data support
• RDDs, DataFrames, MLlib
• Works with Hadoop, HDFS, S3
• Flexible tuning and monitoring
• Open-source under Apache 2.0

#9. Vertica

Vertica is a columnar, high-speed MPP database that supports massive analytical workloads, real-time processing, and ML integration. It’s a viable Databricks alternative for telecom, financial services, and ad tech use cases.

Features:

• Columnar OLAP engine
• Machine learning in-database
• Cloud + on-prem + hybrid support
• High concurrency + workload isolation
• ANSI SQL + Python APIs

#10. ClickHouse

ClickHouse is a fast open-source OLAP database built for analytical workloads. It handles billions of rows with sub-second latency and is ideal for teams building dashboards, observability platforms, or time-series analytics in place of Databricks pipelines.

Features:

• Columnar storage engine
• Sub-second queries on large datasets
• Open-source and cloud editions
• Horizontal scaling and sharding
• Supports Kafka + streaming ingest

Conclusion

Databricks delivers a powerful data platform, but it’s not always the best fit for every organization. In 2025, there are strong alternatives that offer better SQL-first support, more cost-efficient scaling, simpler architecture, or faster onboarding. Whether you’re building a lakehouse, orchestrating ML, or delivering high-performance analytics — you have choices.

Dremio and Starburst lead for open lakehouses. Snowflake and BigQuery shine for scalable SQL analytics. Tools like Dataiku and Vertica bring enterprise features with flexibility. Choose the platform that matches your tech stack, user skillset, and future growth path.

Databricks Alternatives FAQs