Databricks has become a leading platform for unified data analytics and AI, but it often comes with a steep learning curve, complex pricing, and cloud dependency. Many data teams find that Databricks’ Spark-centric interface and usage-based billing can be challenging – especially for organizations new to Spark or looking for simpler cost structures.

For example, Databricks’ frequent updates (with evolving features) are sometimes poorly documented, and its DBU pricing model can make costs unpredictable at scale. These factors prompt technical leaders to explore alternative platforms that may offer easier ETL pipelines, built-in analytics, fixed pricing, or broader cloud neutrality.

• Complexity and Learning Curve: Databricks is powerful but assumes deep Spark and SQL expertise. Non-experts often struggle with its developer-centric tools and rapid release cycle.
• Pricing Uncertainty: Its consumption-based pricing (Databricks Units) can quickly grow for large jobs or idle clusters, leading to budget surprises and high total cost of ownership.
• Infrastructure and Cloud Lock-in: Although multi-cloud, Databricks is a hosted SaaS and relies on proprietary components (Delta Lake, MLflow). Some teams prefer open architectures or fixed-cost solutions across clouds.
• Feature Gaps: Databricks provides strong notebooks and Spark pipelines, but lacks built-in BI/visualization tools and often requires external services for dashboards or reverse-ETL data activation.
• Enterprise Needs: Large organizations may need specialized governance, hybrid deployment, or domain-focused solutions (e.g. SAP data fabrics) beyond what Databricks offers out of the box.

Given these considerations, organizations are evaluating alternatives across domains like lakehouse architectures, data engineering, streaming, ML/AI, and collaborative analytics. Below we review the top 10 Databricks alternatives based on capabilities, use cases, and trade-offs.

Databricks Alternatives: Top 10

1. Peliqan – All-in-one Data Platform

Peliqan is a unified, cloud-native data platform that integrates ELT pipelines, data warehousing, analytics, and data activation into one solution. Designed for simplicity, Peliqan lets teams connect data from hundreds of sources without managing infrastructure.

It provides a Python-based, low-code environment and an AI-powered “Magical SQL” assistant to build transformations, dashboards, and workflows quickly. With 250+ ready-to-use connectors, Peliqan can ingest and process data from databases, APIs, SaaS apps, and event streams in any cloud.

Key Features

• Integrated cloud data warehouse (built on Postgres/Trino) with federated query engine
• AI-assisted query editor (“Magical SQL”) for generating transformations and analytics
• Reverse ETL and data activation (API/webhook publishing, Excel/BI export)
• Serverless Python-based scripts and notebooks for custom data workflows
• Built-in visual analytics and alerting on processed data

Ideal Use Cases: Data engineering teams and SaaS companies wanting an all-in-one stack without stitching multiple tools. Peliqan suits teams needing quick ETL and analytics deployment (for example, cloud-forward organizations and analytics consultants). Its point-and-click pipelines make it attractive for projects where ease-of-use and fast time-to-insight are critical.

Pros: Peliqan offers true all-in-one convenience – you don’t need separate ETL, warehouse, BI or reverse-ETL tools. It has transparent fixed pricing tiers (no unpredictable usage fees), instant connectivity with hundreds of data sources, and hybrid no-code/low-code development (clicks + Python). Setup is fast and it is cloud-agnostic, so teams can switch clouds if needed.

Cons: As a newer platform, Peliqan may have a smaller community or fewer third-party integrations than older vendors. It is optimized for cloud-native (SaaS-first) workflows and has limited on-prem deployment options for teams needing strict on-prem support.

2. Snowflake – Cloud Data Platform

Snowflake is a cloud-native data platform known for its unique multi-cluster, shared-data architecture. It enables businesses to store, manage, and analyze massive datasets with independent compute scaling, so queries run on isolated “virtual warehouses.”

Snowflake’s core innovation is separating compute from storage, which allows each to scale on-demand. It supports both structured and semi-structured data (JSON, Parquet) and offers secure data sharing across accounts and clouds. Snowflake runs on AWS, Azure, and GCP, making it widely adopted for enterprises needing a scalable, SQL-friendly warehouse.

Key Features

• Multi-cluster shared-data architecture: independent compute clusters (“virtual warehouses”) on demand
• Native support for JSON/Parquet/ORC files and ANSI-standard SQL interfaces
• Secure Data Sharing (share live data without copying) and Snowflake Data Marketplace
• Time Travel and Clone for historical queries and zero-copy cloning
• Cross-cloud availability (runs on AWS, Azure, GCP simultaneously)

Ideal Use Cases: Cloud data warehousing and analytics at enterprise scale. Snowflake excels when you have large volumes of data from many sources and want nearly infinite concurrency for BI dashboards or data science. It is often chosen by companies that prioritize elastic scalability and broad cloud support.

Pros: Snowflake offers near-infinite scalability and concurrent performance without manual tuning. Queries generally run fast, and it automatically handles infrastructure management (storage, maintenance, optimization). Its ecosystem integrations (connector support, partner tools) are mature. Users benefit from familiar SQL and zero-administration – e.g. Snowflake is fully managed and even handles software updates for you.

Cons: Snowflake’s usage-based pricing (per-second, per-cluster) can make costs unpredictable with heavy workloads, and customers can get surprised by credits on idle clusters. Unlike Databricks, Snowflake has no native notebook interface or built-in ETL/BI tools – teams must use separate pipelines or BI layers. It is also entirely cloud-hosted (no on-prem option).

3. Google BigQuery – Serverless Data Warehouse

Google BigQuery is Google Cloud’s fully managed, serverless data warehouse built for real-time analytics and petabyte-scale queries. BigQuery uses a decoupled architecture with columnar storage and a distributed query engine, so you can run massive SQL queries without provisioning resources. Its serverless model means you don’t need to manage clusters or infrastructure – Google handles scaling automatically.

BigQuery also includes built-in machine learning (BigQuery ML), geospatial analysis, and a blazing-fast in-memory BI cache (BI Engine) to accelerate dashboards. It natively integrates with GCP services (e.g. Dataflow, Pub/Sub) and honors open formats like Iceberg and Delta through BigLake.

Key Features

• Serverless, auto-scaling compute – run SQL/Python queries without cluster management
• Built-in BigQuery ML for model training and Vertex AI integration for advanced ML
• Real-time data ingestion (Streaming APIs) and BI Engine for accelerated analytics
• Support for open table formats and federated queries across Cloud Storage and BigLake
• Seamless integration with Looker, Data Studio, and Cloud AI/ML services

Ideal Use Cases: Organizations on Google Cloud or looking for “no-ops” analytics at scale. BigQuery is great for ad hoc analytics, event streaming data, and BI dashboards where auto-scaling is essential. It suits scenarios like IoT analytics, batch ELT on GCS data, or large query workloads where you want minimal database administration.

Pros: BigQuery requires virtually no setup or tuning – you simply query data and Google handles everything. Pay-per-query pricing is straightforward for low-volume usage. It excels on very large datasets (analytics on terabytes and petabytes) and offers strong integration with Google’s data ecosystem (Analytics 360, Cloud Functions, etc.).

Cons: At extremely high query volumes, costs can add up quickly since you pay per data scanned. Also, BigQuery is tightly tied to GCP, so data egress or multi-cloud scenarios may be complicated. It has limited multi-user notebook or scheduling features compared to Databricks (you generally use external tools or scheduled queries).

4. Amazon Redshift – Cloud Data Warehouse

Amazon Redshift is AWS’s fully managed, petabyte-scale data warehouse service. It is based on a columnar storage design with a Massively Parallel Processing (MPP) architecture, allowing fast analytical queries across large datasets. Redshift can run on provisioned clusters or via its serverless offering; it tightly integrates with the AWS ecosystem (loading data from S3, Athena, EMR, and SageMaker).

Notably, Redshift Spectrum lets you query data directly on S3 in open formats (Parquet, ORC, etc.) without loading it into the warehouse, blending data lake and warehouse architectures. It also offers ML integration through Amazon SageMaker and a new AQUA acceleration layer for faster query performance.

Key Features

• Columnar, MPP design for high-performance SQL analytics
• Redshift Spectrum for querying external data on Amazon S3
• Concurrency Scaling and RA3 nodes for independent compute/storage scaling
• Integration with AWS ML (SageMaker) and AI services
• Materialized views and result caching for faster BI queries

Ideal Use Cases: Enterprises with large data workloads on AWS. Redshift is ideal when you want a fast cloud data warehouse that works seamlessly with AWS data sources (S3, Aurora, Kinesis). It handles both traditional BI reporting and mixed OLAP/OLTP workloads. Companies already invested in AWS often choose Redshift for predictable performance in a familiar environment.

Pros: Redshift provides predictable and fast performance on structured data. Deep AWS ecosystem integration (SageMaker, Glue, Kinesis) enables end-to-end pipelines. Redshift Serverless offers pay-per-use without provisioning. The pricing is transparent – you know what you’ll pay for each cluster or serverless endpoint.

Cons: Redshift requires frequent administration (vacuuming, indexing, table maintenance) for optimal performance. It is less suitable for unstructured data and is AWS-centric (though you can technically run it on other clouds via outposts). Compared to Databricks, Redshift lacks native notebook support and multi-engine flexibility.

5. Azure Synapse Analytics – Unified Azure Data Platform

Azure Synapse Analytics is Microsoft’s unified data and analytics service, combining data warehousing, big data processing, and data integration. Synapse includes serverless and dedicated SQL pools, Spark pools, and data pipelines (similar to Azure Data Factory).

It integrates tightly with Power BI for reporting and Azure ML for machine learning. Synapse’s key differentiator is its unified workspace where users can query data lakes and warehouses side-by-side using SQL or Spark. It also leverages Azure Purview for data governance and offers native connectors for hundreds of data sources.

Key Features

• Dual SQL options: serverless SQL for on-demand queries and dedicated SQL pools for provisioned performance
• Apache Spark pools for large-scale data processing and ML
• Integrated data pipelines (Azure Data Factory engine) for ETL/ELT
• Power BI integration for seamless BI dashboards
• Azure Purview for enterprise-wide data governance

Ideal Use Cases: Azure-based enterprises needing a unified platform for data warehousing, ETL, and analytics. Synapse works well for organizations that rely heavily on Microsoft tools (Office 365, Power BI, Teams) and want a single workspace for SQL and Spark workloads. It is also attractive for companies migrating from SQL Server to the cloud.

Pros: Synapse offers a truly integrated experience in the Microsoft ecosystem. You get both serverless and provisioned options for flexibility. The ability to query data lakes directly with SQL (without loading data) saves time and cost. Its tight integration with Power BI simplifies BI deployment.

Cons: Synapse is heavily Azure-centric with limited multi-cloud support. The learning curve can be steep for teams unfamiliar with Azure. Pricing for dedicated pools can add up quickly, and the platform has fewer third-party integrations compared to Databricks.

6. Cloudera Data Platform (CDP) – Hybrid Data Cloud

Cloudera Data Platform (CDP) is a comprehensive data management and analytics platform designed for hybrid cloud and multi-cloud environments. CDP evolved from the merger of Cloudera and Hortonworks, combining their Hadoop expertise with modern cloud services.

It provides data engineering, data warehousing, machine learning, and streaming analytics across on-premises and cloud infrastructures (AWS, Azure, GCP). CDP’s unique strength is its focus on security, governance, and regulatory compliance through SDX (Shared Data Experience) which provides unified security and metadata management across all workloads.

Key Features

• Hybrid cloud architecture supporting on-premises, public cloud, and multi-cloud deployments
• SDX for unified security, governance, and metadata management
• Data Engineering, Data Warehousing, Machine Learning, and Operational Database services
• Apache Iceberg table format support for open lakehouse architecture
• CDP Private Cloud for containerized, self-service analytics on Kubernetes

Ideal Use Cases: Large enterprises with strict compliance requirements, hybrid cloud strategies, or existing Hadoop investments. CDP excels when you need consistent data governance across multiple environments and regulated industries (finance, healthcare, government) requiring on-premises options. It is also suited for organizations wanting to modernize legacy Hadoop clusters.

Pros: CDP offers true hybrid/multi-cloud flexibility with consistent experiences across environments. Enterprise-grade security and governance features are built-in. The platform provides strong support for both batch and streaming analytics. Organizations benefit from Cloudera’s extensive professional services and support.

Cons: CDP has a higher total cost of ownership compared to cloud-native solutions. The platform complexity requires significant expertise to deploy and manage. It may be overkill for smaller organizations or simple use cases. The user experience is less modern compared to newer platforms like Databricks.

7. Starburst Enterprise – Query Engine for Data Lakes/Mesh

Starburst Enterprise is a commercial distribution of Trino (formerly Presto SQL) that provides fast, distributed SQL analytics across multiple data sources. Unlike traditional warehouses, Starburst doesn’t require data movement – it queries data where it lives, whether in data lakes (S3, ADLS), databases, or streaming systems. This makes it ideal for data mesh and federated query architectures.

Starburst offers enterprise features like security, caching, cost-based optimization, and connectors to 50+ data sources including Snowflake, MongoDB, Elasticsearch, and Kafka.

Key Features

• Federated query engine accessing 50+ data sources without data movement
• Support for open table formats (Iceberg, Delta Lake, Hudi) and data lakes
• Materialized views and smart indexing for query acceleration
• Fine-grained access control and data masking for security
• Kubernetes-native deployment with auto-scaling and multi-cluster support

Ideal Use Cases: Organizations implementing data mesh or federated architectures who want to avoid data duplication. Starburst is excellent for interactive analytics across diverse data sources, migration projects (querying old and new systems simultaneously), and companies wanting to leverage existing data lake investments without moving data.

Pros: Starburst eliminates ETL complexity by querying data in place. It provides excellent performance on data lakes with pushdown optimizations. The platform is cloud-agnostic and supports hybrid deployments. Open-source Trino roots mean no vendor lock-in.

Cons: Starburst is primarily a query engine, not a full platform – you need separate tools for ETL, ML, and storage. Performance depends heavily on underlying data source optimization. It requires careful configuration and tuning for optimal results. The platform lacks native notebook or visualization capabilities.

8. Apache Spark – Unified Analytics Engine

Apache Spark is the open-source distributed computing framework that powers many data platforms, including Databricks. Spark provides unified APIs for batch processing, streaming, SQL queries, and machine learning across large datasets.

It runs on various cluster managers (Kubernetes, YARN, Mesos) and supports multiple languages (Scala, Python, Java, R, SQL). While Databricks commercializes Spark, many organizations run open-source Spark directly on cloud infrastructure or on-premises for complete control and cost optimization.

Key Features

• Distributed in-memory processing for 100x faster performance than Hadoop MapReduce
• Unified APIs for batch, streaming, SQL, and ML workloads
• Spark SQL for structured data processing with DataFrames and Datasets
• MLlib for scalable machine learning and GraphX for graph processing
• Structured Streaming for real-time data processing

Ideal Use Cases: Organizations wanting maximum control and customization of their data processing. Spark suits teams with strong engineering capabilities who can manage infrastructure, companies wanting to avoid vendor lock-in, and scenarios requiring specific optimizations or custom deployments. It is also ideal for organizations already invested in open-source ecosystems.

Pros: Spark is completely open-source with no licensing costs. It offers maximum flexibility and customization options. The large community provides extensive resources and contributions. You can run Spark anywhere – on-premises, cloud, or hybrid environments.

Cons: Running Spark requires significant operational expertise and infrastructure management. You need to handle cluster provisioning, monitoring, and optimization yourself. There’s no built-in storage layer or user interface. Integration and tooling require more effort compared to managed platforms.

9. Amazon EMR – Managed Hadoop/Spark Service

Amazon EMR (Elastic MapReduce) is AWS’s managed service for running big data frameworks like Spark, Hadoop, HBase, and Presto. EMR simplifies provisioning clusters and automatically handles infrastructure management while giving you control over configurations.

It integrates deeply with AWS services – reading from S3, writing to DynamoDB, and connecting with SageMaker for ML. EMR offers both EC2-based clusters and EMR Serverless for automatic scaling. Recent additions include EMR on EKS for Kubernetes deployments and EMR Studio for notebook-based development.

Key Features

• Managed clusters for Spark, Hadoop, Presto, HBase, and other big data tools
• EMR Serverless for automatic scaling without cluster management
• EMR on EKS for containerized Spark on Kubernetes
• Deep AWS integration with S3, Glue Data Catalog, and SageMaker
• EMR Studio for collaborative notebook development

Ideal Use Cases: AWS-centric organizations needing managed big data processing. EMR works well for ETL pipelines on S3 data, temporary clusters for specific jobs, migration from on-premises Hadoop to cloud, and teams wanting Spark/Hadoop without operational overhead. It is also cost-effective for workloads with variable compute needs.

Pros: EMR offers flexible pricing with per-second billing and spot instances. It provides managed infrastructure with automatic scaling. Deep AWS ecosystem integration simplifies data pipelines. You get choice of multiple frameworks (Spark, Presto, Hadoop) in one service.

Cons: EMR is essentially AWS-only with limited portability. Cluster startup time can be slow (5-10 minutes) for on-demand jobs. It requires more configuration than fully managed services like Databricks. The user interface and developer experience lag behind modern platforms.

10. Google Cloud Dataproc – Managed Spark/Hadoop Service

Google Cloud Dataproc is GCP’s fully managed service for Apache Spark, Hadoop, and other open-source data tools. Dataproc emphasizes speed and simplicity – clusters start in 90 seconds and integrate seamlessly with BigQuery, Cloud Storage, and Bigtable.

It supports autoscaling, preemptible VMs for cost savings, and workflow orchestration through Cloud Composer (Airflow). Dataproc on GKE enables running Spark on Kubernetes for better resource utilization. The service also offers optional components like Jupyter, Zeppelin, and Conda for enhanced development experiences.

Key Features

• Fast cluster provisioning (90 seconds) with per-second billing
• Autoscaling and preemptible VMs for cost optimization
• Native integration with BigQuery, BigLake, and Cloud Storage
• Dataproc on GKE for Kubernetes-based Spark deployments
• Workflow templates and Cloud Composer integration for orchestration

Ideal Use Cases: GCP users needing managed Spark/Hadoop for ETL and analytics. Dataproc excels for batch processing on Cloud Storage data, migration from on-premises Hadoop to GCP, temporary clusters for specific workloads, and integration with BigQuery for hybrid analytics.

Pros: Dataproc offers the fastest cluster startup times in the industry. Per-second billing with automatic termination reduces costs. Tight BigQuery integration enables powerful hybrid analytics. The service is simpler to use than EMR with better default configurations.

Cons: Dataproc is GCP-specific with limited multi-cloud options. It has fewer features than Databricks (no Delta Lake, limited ML tools). The notebook experience is basic compared to modern platforms. Like EMR, it requires more hands-on management than fully integrated platforms.

Comparison Table: Key Databricks Alternatives

Peliqan vs Databricks: Quick Comparison

For data teams comparing Databricks with an all-in-one data platform, the table below highlights key differences in deployment, integration approach, analytics features, and pricing.

Conclusion

Databricks pioneered the modern data lakehouse, but no single platform suits all needs. The best alternative depends on your team’s priorities – whether that’s ease of use, cost transparency, multi-cloud flexibility, or advanced AI. For example, Peliqan emphasizes a unified, easy-to-use stack with predictable pricing and built-in dashboards.

Snowflake and BigQuery excel as managed analytics warehouses for high performance and concurrency, while Azure Synapse integrates tightly with Power BI for Microsoft-centric shops. Open engines like Spark, EMR, and Dataproc offer maximum flexibility for custom pipelines and streaming, at the cost of more management overhead. Platforms like Dremio empower analytics directly on data lakes, avoiding data duplication. And enterprise-grade options like IBM Cloud Pak deliver full governance and hybrid support, albeit with complexity and cost.

Ultimately, teams should weigh trade-offs: usability vs. control, fixed pricing vs. pay-as-you-go, cloud lock-in vs. open source. As one Peliqan analysis notes, the ideal solution strikes the right balance of scalability, developer-friendliness, and total cost. By comparing features, supported workloads, and pricing models, you can choose a Databricks alternative that best fits their data architecture strategy and business requirements.