Snowflake vs Apache Druid

Compare and contrast Snowflake and Apache Druid by architecture, ingestion, queries, performance, and scalability.

Compare Snowflake to Rockset here

Compare Apache Druid to Rockset here

Snowflake vs Apache Druid Architecture

Architecture

Snowflake

Apache Druid

Deployment model

SaaS - infrastructure, software and cluster ops managed by service provider

• SaaS or self managed. • Imply’s Druid cloud offering requires customers to configure, scale, and capacity plan

Use of storage hierarchy

Cloud object storage for shared data accessible from any virtual warehouse

• Queries are served from data on disk and an in-memory cache • Cloud storage or HDFS for deep storage

Isolation of ingest and query

Yes - separate virtual warehouses for batch data loading, ELT jobs and queries

• Ingestion and queries are run on the same node by default • The ingestion and querying processes can be run on separate nodes, however not for real-time data

Separation of compute and storage

Yes

No, although Imply’s offering separates compute and storage

Isolation for multiple applications

Yes - separate virtual warehouses for each workload

Snowflake is the data warehouse built for the cloud. Snowflake is well-known for separating storage and compute for better price performance. With Snowflake, multiple virtual warehouses can be spun up or down for batch data loading, transformations and queries all on the same shared data.

Druid’s architecture employs nodes called data servers that are used for both ingestion and queries. High ingestion or query load can cause CPU and memory contention compared with Druid alternatives. Breaking apart the pre-packaged ingestion and query server components involves planning ahead and additional complexity, and is not dynamic.

Snowflake vs Apache Druid Ingestion

Ingestion

Snowflake

Apache Druid

Data sources

• Third party ETL tool to ingest data into Snowflake including Fivetran, Hevo or Striim • Bulk loading from S3, GCS, Azure Blob Storage • Sink Connector for Apache Kafka in Confluent Cloud

Data connectors to: • Events streams (e.g. Kafka, Kinesis) • Data lakes (e.g. S3, Google Cloud Storage) • RDBMS and HDFS databases CDC events from databases require manual conversion to Druid events

Semi structured data

Ingests JSON and XML as a VARIANT data type

Druid requires flattening nested data at ingest and maintaining a flattening spec as the schema changes over time

Transformations and rollups

• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts

Yes, using ingestion specs written in JSON support rollups and simple transformations. SQL ingest transformations available for Imply

Snowflake is an immutable data warehouse that is built for batch ingestion and relies heavily on the modern data stack ecosystem for data connectors and transformations. Snowflake has a number of integrations to ETL and ELT solutions including Fivetran, Hevo, Striim and dbt. While Snowflake does have support for semi-structured data in the form of a VARIANT type, it is best to structure the data for optimal query performance.

Druid has built-in connectors that manage ingestion from common data sources. Unlike some Druid competitors, it doesn’t support nested data, so data must be flattened at ingest. Denormalization is also required at ingest, increasing operational burden for certain use cases.

Snowflake vs Apache Druid Performance

Performance

Snowflake

Apache Druid

Updates

Data warehouse with immutable storage. Updates rewrite and merge entire partitions

Druid segments become immutable once committed and published, making it more appropriate for append-only use cases

Indexing

• Bitmap index

Query latency

Seconds to minutes on petabytes of data

Typically sub-second query latency for denormalized, flattened datasets up to 100s of TBs

Storage format

Compressed columnar format stored in cloud object storage

Columnar format partitioned by time

Streaming ingest

• Ingests on a batch basis • Snowpipe typically ingests in minutes

• Sub 15 seconds

Snowflake is designed for batch analytics with analysts and data scientists infrequently accessing large-scale data for trend analysis. Snowflake, like many data warehouses, is immutable and does not support frequently changing data efficiently. Snowflake uses a columnar store to return aggregations and metrics efficiently, often with query response times in the seconds to minutes on petabytes of data.

Druid is designed to make streaming data queryable as quickly as possible. JOINs are either impossible or incur a large performance penalty. Updates are only possible via batch jobs. Druid leverages data denormalization and write-time aggregation at ingestion to reduce query latency.

Snowflake vs Apache Druid Queries

Queries

Snowflake

Apache Druid

Joins

Yes

Yes, only for datasets that fit in memory, and with a query latency penalty

Query language

SQL

Druid native queries • Druid SQL

Developer tooling

• SQL APIs - make SQL calls to Snowflake programmatically • UDFs for Javascript, Python, Java and SQL functions • Go, JDBC, .NET, Node.js, ODBC, PHP, Python drivers

• Druid SQL API

Visualization tools

Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more

Pivot, maintained by Imply

Snowflake supports SQL as its native query language and can perform SQL joins. Snowflake for developers introduced a number of developer tools including SQL APIs, UDFs and drivers to support application development. As Snowflake was originally built for business intelligence workloads, it integrates with a number of visualization tools for trend analysis.

Druid has a native JSON-based query language and provides Druid SQL as an alternative that translates into its native queries. JOINs are not recommended.

Snowflake vs Apache Druid Scalability

Scalability

Snowflake

Apache Druid

Vertical scaling

Resize virtual warehouses via web interface or using DDL commands for warehouses

Users can manually increase or decrease server sizes

Horizontal scaling

• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set

• Users can manually add additional nodes to a scaled-out cluster. • Imply automates some of the horizontal scaling process.

Snowflake virtual warehouses can be scaled up for faster queries or scaled out using multi-cluster warehouses to support higher concurrency workloads. Snowflake has shared blob storage that scales automatically and independently.

Druid users are exposed to complex decisions about the number and size of servers as clusters are scaled.