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Snowflake vs Elasticsearch

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

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Snowflake Architecture vs Elasticsearch

Separation of compute and storage
Isolation of ingest and query
Yes - separate virtual warehouses for batch data loading, ELT jobs and queries
No - There are dedicated ingestion nodes but indexing, compaction and updates occur on the data nodes
Isolation for multiple applications
Yes - separate virtual warehouses for each workload
Full isolation with replication
Use of storage hierarchy
Cloud object storage for shared data accessible from any virtual warehouse
• Hot, warm and cold storage on disk • Frozen storage on cloud storage
Deployment model
SaaS - infrastructure, software and cluster ops managed by service provider
On-prem, 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.

Elasticsearch is an open-source distributed search engine built on Apache Lucene, a full text search library. Elasticsearch is a distributed system, which means that it is designed to operate across multiple nodes, each responsible for a part of the data.

Snowflake Ingestion vs Elasticsearch

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
• Logstash JDBC input plugin for relational databases • Open-source Kafka plugin or Kafka Elasticsearch Service Sink Connector (available only to managed Confluent and Elasticsearch) • REST APIs or client libraries to sync data directly from the application
Semi structured data
Ingests JSON and XML as a VARIANT data type
Yes- Ingests JSON and XML without a predefined schema
Transformations and rollups
• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts
Yes - Ingest pipelines can be configured to remove fields, extract values from text and enrich data. Ingest pipelines require ingest nodes in the cluster. Rolling up historical data is in technical preview

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.

Elasticsearch has a number of integrations as well as a REST API. It is a NoSQL database and natively supports semi-structured data. Transformations typically occur upstream so that data can be modeled for optimal performance before it is indexed in Elasticsearch.

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Snowflake Queries vs Elasticsearch

Query language
DSL - domain specific language
No- Need to use workarounds including data denormalization, application-side joins, nested objects or parent-child relationships
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
• REST API • Java, Javascript, Go, .NET, PHP, Perl, Python, Ruby, Rust
Visualization tools
Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more
• Kibana • PowerBI, Qlik, Tableau

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.

Elasticsearch has its own domain specific language (DSL) based on JSON. Joins are not a first class citizen in Elasticsearch requiring a number of complex and expensive workarounds. Elasticsearch is known for its developer tooling and supports a number of client libraries. Kibana is the visualization layer for Elasticsearch and is frequently used for log analytics and monitoring.

Snowflake Performance vs Elasticsearch

Streaming ingest
• Ingests on a batch basis • Snowpipe typically ingests in minutes
• Ingests on a per-record or batch basis • Data latency on a per-record basis is typically 1-2 seconds
Data warehouse with immutable storage. Updates rewrite and merge entire partitions
Update API can update, delete or skip modifying the document. The entire document must be reindexed; in-place updates are not supported
Storage format
Compressed columnar format stored in cloud object storage
JSON documents
Inverted index
Query latency
Seconds to minutes on petabytes of data
50-1000ms queries on 100s of TBs

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.

Elasticsearch is a search engine that utilizes an inverted index. Although this approach leads to storage amplification, it also enables low-latency queries that demand less computation. Elasticsearch is tailored to accommodate large scale, append-only data such as logs, events, and metrics. To manage frequently updated data, users often utilize the Bulk API to minimize computational costs and ensure consistent query performance.

Snowflake Scalability vs Elasticsearch

Vertical scaling
Resize virtual warehouses via web interface or using DDL commands for warehouses
Manually resize machines
Horizontal scaling
• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set
• Elasticsearch is horizontally scalable and can scale by adding nodes to the cluster • When using managed Elastic, autoscaling policies can be used to self-monitor cluster health and it is the responsibility of the operator to update resource allocations either manually or using APIs. Elasticsearch rebalances the data automatically obeying shard allocation rules • There are many cluster-level operations that need to be monitored when scaling

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.

Elasticsearch is horizontally scalable and can scale by adding more nodes to the cluster. Its tightly coupled architecture means that compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling Elasticsearch often requires deep expertise as there are many levels of the system that need to be managed- the server, operating system, network and software.

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