Databases Compared: Databricks vs. Snowflake vs. ChaosSearch vs. Elasticsearch

For organizations that generate large amounts of data, implementing a cloud database solution is a critical step towards enabling performant and cost-effective data storage, transformation, and analytics. Choosing the right cloud database solution involves careful consideration of features, capabilities, costs, and use cases to ensure alignment with your organization’s needs and objectives.

This blog post features an in-depth comparison of four popular cloud database solutions: Databricks vs. Snowflake vs. ChaosSearch vs. Elasticsearch.

We’ll explore the key features and characteristics of these database solutions, including solution architecture, data models, supported data types, structures, and query languages, strengths, weaknesses, and optimal use cases to help you determine which cloud database solution is right for your organization.

Databricks vs. Snowflake vs. ChaosSearch vs. Elasticsearch

Overview: Features, Core Strengths and Considerations

Databricks

Databricks is a data lakehouse platform built on Apache Spark and designed to accelerate innovation by enabling data engineering, data science, and ML use cases in a collaborative and scalable environment.

Features and Key Strengths

• Databricks integrates data engineering, data science, and machine learning capabilities in a single environment, breaking down data silos and promoting collaboration.
• Delivers scalable, high-performance data processing and analytics with help from Apache Spark.
• Support for multiple query languages gives users more flexibility.

Considerations

• Databricks has a steep learning curve. Users require strong skills in programming, data structures, and algorithms to generate the maximum value from their data. Organizations may have to acquire or develop new skills and competencies to utilize Databricks to its full potential.

Snowflake

Snowflake is a cloud-based data warehouse solution with database storage and query processing capabilities that help organizations store, manage, and analyze large volumes of structured, semi-structured, and unstructured data.

Features and Key Strengths

• Snowflake’s elastic scalability enables customers to independently scale compute and storage resources based on workload demands.
• Secure data sharing capabilities make it easy to share data with internal and external stakeholders.
• Snowflake handles infrastructure, provisioning, configuration, and maintenance so customers can focus on extracting valuable insights from data.

Considerations

• Having to move data from cloud object storage into the Snowflake platform results in data egress and monthly data storage fees that lead to data retention trade-offs and/or high TCO.

ChaosSearch

ChaosSearch is a cloud data lake platform that transforms cloud object storage into a hot analytical database to support operational and business use cases for data analytics at massive scale.

Features and Key Strengths

• ChaosSearch leverages cost-effective cloud object storage as primary storage backing.
• Enables log analytics with no data movement, no ETL process, and no data retention limitations or trade-offs.
• Delivers a natural language assistant powered by Gen AI to help customers extract value and get answers from their data.
• Unique architecture reduces log and event analytics costs for customers to a fraction of other solutions.

Considerations

• ChaosSearch provides a built-in OpenSearch Dashboards user interface and exposes API’s but does not support external self-managed OpenSearch Dashboards or Kibana to connect directly.

Elasticsearch

Elasticsearch is a distributed search and analytics engine, commonly used for log analytics and full-text search. Elasticsearch, Logstash, and Kibana are often deployed together as the ELK stack, an open-source software stack primarily used for log management and analytics.

Features and Key Strengths

• Elasticsearch’s distributed architecture enables horizontal scalability.
• Inverted indexing technology enables fast, high-performance querying.
• Open-source solution with low barrier to adoption and no software licensing fees for self-managed version.

Considerations

• Scaling your Elasticsearch deployment involves adding additional nodes, sharding, and replicas to handle increased data volumes. As an Elasticsearch index increases in size, users often notice slow indexing and degraded query performance. Expiring data to reduce index size and maintain query performance results in data retention trade-offs.

Comparison Chart (Click any linked feature for more detail)

Deployment

Databricks

Databricks is a cloud-native solution that can be deployed on Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

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Databricks can be deployed on AWS, GCP, or Azure to enable data warehousing, engineering, streaming, data science, and ML use cases.

Snowflake

Snowflake runs completely on cloud infrastructure. Similar to Databricks, a Snowflake account may be hosted on AWS, GCP, or Microsoft Azure.

ChaosSearch

ChaosSearch is a cloud-native service that can be deployed on AWS or GCP.

Elasticsearch

Organizations can deploy Elasticsearch on-prem, on all major public clouds (i.e. AWS, GCP, Azure), or in a private or hybrid cloud environment.

Service/Business Model

Databricks

Databricks is primarily considered a Platform-as-a-Service (PaaS) offering. Users manage data processing and analytics workflows, while Databricks manages the underlying infrastructure and virtual machines needed to execute analytics workloads. Databricks pricing is based on usage of compute resources.

Snowflake

Snowflake is a Software-as-a-Service (SaaS) offering with a pay-as-you-go pricing model. Snowflake charges a monthly fee for data stored inside the platform, as well as incremental pricing based on virtual warehouse usage and processing time.

ChaosSearch

ChaosSearch is a fully managed SaaS offering with pay-per-use pricing. Customers can choose between ingestion-based and worker-based pricing models to optimize ownership costs based on their unique access patterns, circumstances, and preferences.

Elasticsearch

Elasticsearch is available as an open-source self-managed database solution, and as a fully managed SaaS product (Elastic Cloud). When self-managing Elasticsearch in the cloud, Elasticsearch users will incur costs for data storage and compute resources from their public cloud provider. Elastic Cloud pricing is based on the customer’s usage of virtual storage, memory, and virtual compute resources.

Solution Architecture

Databricks

Databricks’ architecture consists of two layers: a Control Plane that hosts Databricks back-end services (e.g. graphical UI, REST APIs), and a Data Plane that handles data processing and external interactions.

Snowflake

Snowflake’s architecture consists of three layers:

• Database Storage Layer - A fully managed database layer where data is stored inside the Snowflake platform and may be accessed by Snowflake customers via SQL query.
• Query Processing Layer - Snowflake processes queries using virtual warehouses. Each one is a massive parallel processing (MPP) compute cluster with multiple compute nodes allocated from a public cloud provider.
• Cloud Services Layer - A collection of services that coordinate Snowflake activities, including authentication, infrastructure and metadata management, query parsing and optimization, and access controls.

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Snowflake’s architecture includes a database storage layer, query processing layer, and cloud services layer.

ChaosSearch

With ChaosSearch, customers can ingest telemetry data from multiple sources directly into cost-effective Amazon S3 or Google cloud storage.

Data that lands in cloud object storage may be indexed using proprietary Chaos Index® technology. From there, customers can transform and query their data in Chaos Refinery® before creating visualizations and building dashboards with built-in Kibana Open Distro.

Elasticsearch

Elasticsearch is based on a distributed system model. A node is an instance of Elasticsearch running on a single VM.

An Elasticsearch cluster consists of one or more nodes that work together to manage and store data. Users get data (i.e. JSON documents) into Elasticsearch using a log shipper like Logstash. Data that lands in Elasticsearch is indexed and stored on a data node. Indices may be divided into self-contained units of data known as shards.

Some shards handle indexing and search operations while others provide fault tolerance and ensure high availability.

Data Storage

Databricks

With Databricks, data is stored in customer-managed cloud object storage (e.g. GCP, Amazon S3, or Azure Blob Storage) . Databricks uses the proprietary Databricks File System (DBFS) to access data in cloud object storage. The DBFS provides a unified namespace, support for file and directory operations, and integration with Delta Lake to enable ACID transactions and scalable metadata management.

Snowflake

With Snowflake, customers can choose between storing their data inside Snowflake or in their own public cloud storage.

ChaosSearch

ChaosSearch customers must land their data in Amazon or Google cloud object storage to enable indexing, querying, and analytics.

Elasticsearch

With Elasticsearch, ingested data is indexed and stored across multiple nodes that make up the Elasticsearch cluster.

Supported Data Structures

Databricks

Databricks can be used to query and analyze structured, unstructured, and semi-structured data.

Snowflake

Snowflake can be used to query and analyze structured and semi-structured data.

ChaosSearch

ChaosSearch can index, query and analyze structured, unstructured, and semi-structured data.

Elasticsearch

Elasticsearch was designed to index JSON documents. JSON is a semi-structured data format where a document consists of fields that are name-value pair objects.

Internal Data Model

Databricks

Databricks customers can create tabular databases (tables and views) as well as non-tabular databases (volumes) that can be used to store, organize, and access files in any format. This includes structured, unstructured, and semi-structured data.

Snowflake

As with other data warehouse platforms, data in Snowflake is saved in a columnar format.

ChaosSearch

ChaosSearch employs a proprietary data model and unique data representation that delivers high compression with no loss of fidelity and enables multi-model data access with support for relational queries, full-text search, and Gen AI.

Elasticsearch

Data in an Elasticsearch index is saved as a JSON document.

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A JSON document consists of attribute-value pairs and arrays. Elasticsearch was designed to index JSON documents for full text search.

Supported Query Languages

Databricks

Databricks offers support for multiple query languages, including SQL, Scala, Python, and R.

Snowflake

Snowflake supports the most common standardized version of SQL (ANSI).

ChaosSearch

ChaosSearch supports SQL, full-text search, and Gen AI queries.

Elasticsearch

Elasticsearch supports multiple query languages, including Query DSL, EQL, KQL, SQL, Painless, and Elasticsearch Query Language (ES|QL).

Use Cases

Databricks

• Data processing scheduling and management
• Building visualizations and dashboards
• ML modeling and tracking
• Data security, governance, high availability, and disaster recovery
• Gen AI solutions

Snowflake

• Business intelligence
• Data warehousing
• Batch and streaming analytics
• Financial reporting and analysis

ChaosSearch

• Cloud observability
• Security analytics
• User behavior monitoring and analysis
• ELK stack replacement
• Complemening Grafana
• Embedded database/analytics

Elasticsearch

• Search engines
• Log analytics workloads
• Autocomplete
• Spellcheck
• Crawling and document processing

Databricks vs. Snowflake vs. ChaosSearch vs. Elasticsearch: Which One is Right for You?

When it comes to Databricks vs. Snowflake vs. ChaosSearch vs. Elasticsearch, organizations should choose the cloud database solution that best fits their unique needs and circumstances.

Databricks offers a flexible platform with diverse use cases, but a steep learning curve makes it less user-friendly and more challenging to adopt than alternative database solutions. Snowflake is great for supporting data warehousing and BI use cases, but has a much higher TCO than alternative solutions - especially at scale. Elasticsearch is ideal for use cases that require full-text search, but cluster performance tends to degrade as Elastic indices increase in size.

ChaosSearch is well-suited for organizations that want a true multi-model data platform to cost-effectively store, index, analyze, and retain large volumes of log and event data.

Ready to learn more?

Download and view our Chaos LakeDB white paper for more information and insights into ChaosSearch capabilities and use cases.