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## Architecture
The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.
| Feature | Druid | Databricks |
|---|---|---|
| Separation of storage and compute | No | Yes |
| Supported cloud infrastructure | Can be installed anywhere | AWS, Azure, GCP. Marketplaces and BYOC |
| Isolated tenancy – option for dedicated resources | Single tenant | • Control plane in Databricks account • Data plane in customer VPC (optional) • Storage in customer VPC • Serverless SQL runs in Databricks account with private connectivity |
| Control vs abstraction of compute | • Complex configuration of compute tier with multiple role-specific nodes • Configurable node count • Configurable compute types (virtual machines or kubernetes) | • Configurable clusters and instance types • Serverless SQL warehouses (GA 2025) run in Databricks account with private connectivity, no public IPs • Pro/Classic warehouses run in customer VPC |
| Self-hosted and hybrid deployment options | Self-managed deployment required | • Databricks on customer cloud accounts • Unity Catalog for hybrid governance |
| ACID Compliance and Transactions | Limited ACID support with eventual consistency | • ACID transactions with Delta Lake • Time travel and versioning • Concurrent read/write operations |
Druid is an OLAP engine designed to provide fast real time analytics. Druid adopts a clustered architecture with servers that host various role specific processes. These processes address real time and batch ingestion, indexing, querying of historical and real time data. Apache Druid can be deployed as a virtual machine or a Kubernetes based cluster. Druid does not support a decoupled compute & storage architecture. Deep storage in the form of object storage is used to replicate data to.
Databricks was built by the founders of Spark as an analytics platform to support machine learning use cases. It leverages the Spark framework to process data residing in a data lake and is supported on AWS, GCP and Azure. Databricks coined the marketing term "Lakehouse '' architecture to illustrate the unification of data lake and data warehouse use cases. Customers still manage Spark clusters that process data residing in a Delta lake. Conversion of data to Delta Lake format is required to leverage the functionality of Delta Lake. Databricks Sql is a relatively new addition to simplify access to data stored in a data lake.
## Scalability
There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.
| Feature | Druid | Databricks |
|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | Scale-up of nodes requires careful planning and downtime. Addition of new nodes for scale-out is possible | Autoscaling clusters based on workload demand. Serverless SQL warehouses provide near-instant scaling (2-6 seconds startup) |
| Elasticity – Scaling for higher concurrency | Supports 100s to 100,000s queries per second (1000+ QPS) with proper configuration and scaling | • 10 concurrent queries per cluster limit • Scales up to 40 clusters per warehouse (400 total concurrent queries) • Serverless SQL warehouses provide near-instant autoscaling • Pro/Classic warehouses take several minutes to provision new clusters • Real-world performance degradation typically occurs at 50-150 concurrent queries depending on complexity |
Druid provides the ability to handle fast ingest and high concurrency. Custom sizing and cluster tuning are required to balance the compute, memory, storage needs of each process within Druid and to provide high concurrency. Druid clusters can be grown by adding nodes with automatic rebalancing of storage segments assigned to nodes. Self hosted Druid on Kubernetes is an option that users leverage to simplify scaling. Additionally, Cloud based managed Druid offerings are being rolled out. However, these managed offerings are limited in scale and scaling is not granular.
Databricks allow for autoscaling of clusters based on utilization. Additionally, increasing concurrency associated with a sql endpoint can be accomplished through the addition of clusters. Query concurrency per cluster is maxed at 10. However, scaling with additional clusters for concurrency is possible. Databricks provides a choice of instance types.
## Performance
Performance is the biggest challenge with most data warehouses today. While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.
| Feature | Druid | Databricks |
|---|---|---|
| Indexes | Compressed bitmap indexes for data access and roll-ups to manage aggregations | None |
| Compute tuning | On-premises, self-managed hardware. Druid requires infrastructure management and leverages commonly available instance types | Choice of cluster type, node types including SSD-optimized instances. Serverless provides automatic resource allocation with Intelligent Workload Management (IWM) |
| Storage format | Columnar storage format with time-based sorting | • Delta Lake format with Liquid Clustering (February 2025 – replaces Z-ordering and traditional partitioning) • Cannot use Liquid Clustering alongside Z-ordering on same table • Allows for sorted data in Delta Lake • Requires Optimize to maintain ordering |
| Table-level partition & pruning techniques | Restrictive time-based partitioning. Can partition based on other secondary columns | • Table level partitioning • Liquid Clustering for improved query performance and reduced data skew (February 2025) • Z-ordering (legacy, replaced by Liquid Clustering) • Periodic optimization of storage required |
| Result cache | Ability to support caching on broker (set to off by default) | Multi-layered caching: local in-memory cache per cluster plus remote result cache (serverless only) that persists across all warehouses in workspace |
| Warm cache (SSD) | Yes, at much larger segment level granularity | Yes. Delta cache for data read by queries at file level granularity |
| Support for semi-structured data & JSON functions within SQL | Recommend flattening JSON or translate to array prior to loading. No support for JSON parsing at query runtime | Yes |
| Vector Search and AI Capabilities | No native AI or vector search capabilities | • MLflow integration and Databricks ML platform • Native vector search in Delta Lake (Vector Search) • AI and ML workloads optimized |
| Query Optimizations | • Compressed bitmap indexes • Roll-up aggregations • Time-based optimization • Query optimization requires manual tuning | • Photon engine (C++ vectorized engine providing 3-8x average speedups, maximum speedups over 10x) • Automated stats collection (January 2025) enables cost-based optimization • Predictive I/O for faster point lookups and data updates • Liquid Clustering (February 2025) • Intelligent Workload Management (IWM) with AI-powered resource allocation • Delta cache • Materialized views support |
Druid provides high performance through columnar storage format, parallel processing, bitmap indexes and roll-ups. Druid, however, recommends a denormalized data model for performance needs. Join operations in Druid are a relatively new feature with various limitations, especially if there is a need to join large datasets.
Databricks is designed to leverage the Spark framework for processing large volumes of data. It leverages compressed Parquet files in a Delta Lake. To reduce the amount of data processed, it uses data pruning on partitions and Parquet file metadata. Databricks does not provide any indexes.
## Use cases
There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years.
| Feature | Druid | Databricks |
|---|---|---|
| Low-latency dashboards | • Sub-second load times optimized for time-series and real-time analytics • Built for high-concurrency interactive dashboards • Requires denormalized data model | • Sub-second to seconds load times at TB+ scale • Enhanced by Photon engine (3-8x average speedups) and Delta cache • Serverless SQL warehouses provide rapid startup (2-6 seconds) • Performance depends on cluster configuration |
| Enterprise BI | • Limited integrations with traditional Enterprise BI tools • Strong for real-time operational dashboards • Requires specialized visualization tools | • Strong for data science and ML workloads • Unified analytics platform approach • Growing traditional BI integrations • Serverless SQL warehouses improve accessibility • Delta sharing capabilities |
| Data Apps and AI Applications (Customer-facing low-latency high concurrency) | • Built for high concurrency (1000+ QPS) with distributed architecture • Sub-second response times for time-series data • Optimized for real-time operational applications • No AI capabilities | • 10 concurrent queries per cluster, scaling to 400 total concurrent queries per warehouse • Real-world performance degradation typically occurs at 50-150 concurrent queries depending on workload complexity • Serverless provides near-instant autoscaling • Photon engine delivers 3-8x performance improvements • Strong ML and AI platform integration |
| Ad hoc | • Not optimized for ad-hoc queries • Requires predefined roll-ups and data modeling • Limited flexibility for exploratory analysis | • Excellent for ad-hoc with decoupled storage/compute • Serverless SQL warehouses provide instant provisioning • Intelligent Workload Management handles unpredictable workloads automatically • Strong for exploratory data analysis and ML workloads • Automated stats collection improves query planning |
Druid is designed as an OLAP engine to provide fast access to aggregations that are run against large volumes of data. Druid is typically used for customer facing analytics and streaming data processing. Druid is used as an add-on with other data warehousing products that are efficient at scaling, joining, and filtering large volumes of data. It is not a suitable option for data warehouse replacement.
Databricks is a mature Spark based platform proven for processing streaming data. It is widely used for Machine Learning use cases by data scientists through the use of integrated notebooks. From a low latency query perspective, while it offers features like Delta Cache, it does not provide specialized indexes that can deliver low latency queries.