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Apache Pinot vs Snowflake

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

Compare Apache Pinot to Rockset here

Compare Snowflake to Rockset here

Apache Pinot vs Snowflake Architecture

Architecture
Apache Pinot
Snowflake
Deployment model
PaaS or self managed
SaaS - infrastructure, software and cluster ops managed by service provider
Use of storage hierarchy
Hot storage plus Deep Store for backup and restore operations
Cloud object storage for shared data accessible from any virtual warehouse
Isolation of ingest and query
No
Yes - separate virtual warehouses for batch data loading, ELT jobs and queries
Separation of compute and storage
No, although StarTree separates compute and cloud storage, which is an important consideration for those considering StarTree vs. open-source Pinot
Yes
Isolation for multiple applications
Full isolation with replication
Yes - separate virtual warehouses for each workload

Pinot is a real-time distributed OLAP datastore that ingests both batch and streaming data. It has a distributed systems architecture that scales both horizontally and vertically, but unlike alternative OLAP databases, it does not decouple storage and compute. It supports both self-managed and PaaS options.

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.


Apache Pinot vs Snowflake Ingestion

Ingestion
Apache Pinot
Snowflake
Data sources
Streaming • AWS Kinesis • Apache Kafka Batch • Cloud Storage • PostgreSQL • MySQL • Snowflake • Google BigQuery File upload Write API
• 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
Semi structured data
Support for JSON column types, but JSON must first be converted to a string
Ingests JSON and XML as a VARIANT data type
Transformations and rollups
Yes
• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts

Pinot supports high-performance ingest from streaming data sources. Each table is either offline or real time. Real-time tables have a smaller retention period and scale based on ingestion rate while offline tables have a larger retention period and scale based on the amount of data. In order to persistently store the generated segments that make up a table, you will need to change controller and server configs to add deep storage.

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.

Apache Pinot vs Snowflake Performance

Performance
Apache Pinot
Snowflake
Updates
• By default, all data in Pinot is immutable • Upserts only supported for streaming ingest • No support for upserts on data using the star tree index • No support for upserts on out-of-order events
Data warehouse with immutable storage. Updates rewrite and merge entire partitions
Indexing
• Manually configured • Forward index, inverted Index, Star-Tree index (columnar), bloom filter, range index, search index, JSON index, geospatial index, timestamp index
No
Query latency
50-1000ms queries on 100s of TB
Seconds to minutes on petabytes of data
Storage format
Pinot stores data in a columnar format and adds additional indexes to perform fast filtering, aggregation and group by
Compressed columnar format stored in cloud object storage
Streaming ingest
1-2 second ingest for streaming data
• Ingests on a batch basis • Snowpipe typically ingests in minutes

Like its competitors, Pinot can achieve sub-second query latency at high concurrency. However, this level of performance requires tuning, management, and deep expertise. Compared with the open-source version, the PaaS versions of Pinot address some of these issues, but similarly require expertise while making tradeoffs affecting query performance.

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.


Apache Pinot vs Snowflake Queries

Queries
Apache Pinot
Snowflake
Joins
SQL joins limited to inner join, left-outer, and semi-join
Yes
Query language
SQL
SQL
Developer tooling
• API for querying SQL via POST command • Clients for JDBC, Java, Python, and Golang • Integrations with Trino and Presto
• 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
Visualization tools
Integrations with Tableau and Superset
Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more

In Pinot, SQL-like queries are received by brokers and scatter the request between real-time and offline servers. The two tables then process requests, send results back to the broker, and responds with the result. Joins are limited, as is support for UDFs and subqueries, making Pinot more or less useful depending on the use case.

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.


Apache Pinot vs Snowflake Scalability

Scalability
Apache Pinot
Snowflake
Vertical scaling
All 4 pinot node types can be resized manually
Resize virtual warehouses via web interface or using DDL commands for warehouses
Horizontal scaling
• Users can add additional Pinot nodes to scale horizontally • Rebalancing is manual
• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set

Pinot allows for vertical scaling by increasing CPU and memory for each node as well as horizontal scaling by adding additional nodes. Capacity planning is a time-consuming, iterative, and manual task. It involves load testing and tuning across multiple vectors including read QPS, write QPS, number of streaming partitions, daily data size, retention period, types of workloads, number and type of segments, and much more.

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.