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Snowflake vs Apache Pinot

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

Compare Snowflake to Rockset here

Compare Apache Pinot to Rockset here

Snowflake vs Apache Pinot Architecture

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

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.

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 vs Apache Pinot Ingestion

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

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.

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 vs Apache Pinot Performance

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

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.

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 vs Apache Pinot Queries

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

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.

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 vs Apache Pinot Scalability

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

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.

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.