5X vs Snowflake: Beyond Data Warehousing

Snowflake is a great data warehouse. But is it a complete data readiness platform? Let's find out.
Last updated:
July 23, 2024
Jagdish Purohit

Jagdish Purohit

Data Content & SEO Lead

The most crucial metric for data teams isn't the volume of data collected, query execution speed, or pipeline uptime. It's data readiness.

Data readiness ensures your clean, structured, and centrally modeled data is readily available for a number of use cases: traditional BI, advanced analytics, data activation, and increasingly, AI. It's the foundation for AI and Large Language Models (LLMs) – after all, your AI is only as good as the data it learns from.

Snowflake is a warehousing powerhouse. It excels at storing and managing large amounts of data, and is making strides with new initiatives. But is it a complete data readiness platform? 

This article compares 5X with Snowflake from a data readiness perspective and explores how you can get the best of both worlds.

The five layers of a data-ready system are:

1. Ingestion

2. Warehouse

3. Modeling 

4. Orchestration

5. Business Intelligence

So how do 5X and Snowflake stack up on these? Let’s find out!

Snowflake

snowflake architecture

10%

Ingestion

  • Offers some native ingestion tools (COPY INTO, Snowpipe, Kafka Connector) that work with sources like Google Analytics.
  • Requires additional configuration and custom scripting for complex data sources with non-standard formats or APIs.
  • Integrates with third-party data transfer services for broader source coverage.
100%

Warehouse

  • Excellent storage and compute capabilities, ideal for handling large, complex datasets.
  • Offers various storage options (e.g., micro-partitions, clustering) to optimize performance and cost for diverse workloads.
60%

Modeling

  • Limited native modeling capabilities.
  • Does not offer dbt for SQL-based modeling.
  • Relies on Snowpark (to query, process, and transform data using Python) for advanced data transformations.
  • Offers connector for Apache Spark.
  • Direct integration of advanced analytics (forecasting, anomaly detection) into data models via Snowflake Cortex.
20%

Orchestration

  • Basic built in scheduling capabilities with Snowflake Tasks.
  • Requires vendor ecosystem for complex scheduling capabilities.
10%

Business intelligence

  • Offers basic charts for visualization.
  • Lacks native BI tools for data visualization and exploration.
  • Integrates with external BI tools like Looker, Tableau, Power BI.

How 5X complements Snowflake’s warehousing capabilities

100%

Ingestion

  • Offers 500+ pre-built connectors from all of the most used data sources.
  • Hours, day implementations for custom connector development for the long tail of connectors.
  • Support for Apache Iceberg Tables in S3 or other flat storage.
100%

Warehouse

  • Works on top of Snowflake
  • Also works with multiple other warehouses, including GBQ, Redshift, and Databricks.
100%

Modeling

  • Integrates with dbt for enterprise-grade data modeling.
  • Offers features like lineage tracking, version control, and modular transformations.
  • Also supports SQL, Python, and notebooks for transformation flexibility.
100%

Orchestration

  • Offers Dagster to ship pipelines quickly with 1-click scheduling.
  • Enterprise grade scheduling and DAGS with easy to use UI.
  • Prebuilt templates to accelerate dev time.
100%

Business intelligence

  • Compatible with any BI tool.
  • Provides Superset as an inbuilt option in the platform.
  • Deep integrations and provisioning Power BI, Looker, Sigma and Tableau from 5X.

Snowflake vs 5X: A comparison on data readiness level

Feature

Snowflake

5X

Warehouse
  • Great warehouse with excellent storage and compute capabilities
  • Various options for performance and cost optimization (micro-partitions, clustering)
  • Uses columnar storage for efficient data compression and retrieval
  • Integrates with Snowflake (leverages storage and compute)
  • Same features and cost for data storage as Snowflake as 5X works on top of it
  • Multi-cloud support (connect to GBQ, Redshift, Databricks for storage flexibility)
Ingestion
  • Limited native ingestion tools: COPY INTO (bulk data loading from local or cloud storage), Snowpipe (continuous loading from cloud storage), Kafka Connector (real-time streaming from Kafka topics)
  • Requires additional configuration for complex data sources with non-standard formats or APIs.
  • May need custom scripting for advanced data transformations during ingestion.
  • Pre-built connectors for various data sources (databases, cloud storage, SaaS applications) offer out-of-the-box integrations with common data sources
  • Supports custom connector development for niche sources or data transformations during ingestion. This allows for tailored data acquisition from non-standard APIs or formats.
Modeling
  • Limited native modeling capabilities.
  • Supports basic transformations (e.g., filtering, joining) using SQL
  • Snowpark offers Python integration for in-Snowflake data processing using frameworks like scikit-learn and XGBoost.
  • Current Spark functionality is limited, restricting large-scale data processing.
  • Advanced Analytics with Snowflake Cortex allows integrating ML-based forecasting and anomaly detection functions directly into data models using SQL.
  • dbt supports SQL, Python notebooks for transformation flexibility, offering a wider range of options compared to Snowflake's limited native capabilities.
  • Native support for Notebooks for analyst productivity
  • You can use Snowflake Spark & Snowflake Cortex through 5X
Orchestration
  • Comes with Snowflake Tasks to execute scheduled SQL statements.
  • Requires external enterprise-grade orchestration tools like Airflow for more complex workflows.
  • Offers Dagster for rapid pipeline deployment (one-click scheduling, pre-built templates)
  • Scheduling based on cron timings or event triggers
  • Set up preferences for your Slack channel and emails to run alerts and notify the added sources.
BI
  • Lacks a native BI tool
  • Integrates with various external BI tools
  • Provides Superset as an in-built option in the platform.
  • Provides Superset as an in-built option in the platform.
Advanced capabilities
App development
Snowflake Container Services for deploying custom applications (Java, Python, Scala) within Snowflake.
Using 5X on top of Snowflake, you can build a container app on Snowflake and deploy it in 5X.
AI & Machine Learning
  • Data Science Workbench (collaborative environment for data science), AutoML (automated model building), UDFs (Python/R for custom functions), planned NVIDIA GPU integration
  • Snowflake Cortex offers access to industry-leading LLMs for data exploration, summarization, and more.
  • Snowflake ML to streamline end-to-end ML workflows
  • 5X supports these features by working on top of Snowflake.
  • 5X’s AI chat for BI (coming soon)
Advanced data management
Supports Iceberg tables that use the Apache Parquet file format.
Supports Apache Iceberg.

Other considerations

Total cost of ownership (TCO)

Building data pipelines in Snowflake often requires multiple services for different ingestion methods (Snowpipe, Kafka Integration, etc.), leading to unexpected charges. Plus, separate tools for modeling (dbt, Snowpark) and orchestration (Airflow, custom scripts) add to the overall cost.


5X’s integrated platform typically reduces TCO by 30-50% compared to a piecemeal approach. A single platform simplifies billing and eliminates the need to manage multiple service costs from Snowflake and various data tools.

Integrated services offering

Snowflake requires additional resources for building and managing data pipelines. This can involve hiring external consultants or building an in-house data engineering team (data salaries are getting expensive), resulting in significant costs.


5X’s integrated services are approximately 25% of the cost of US-based consultancies and 70% of the cost of building and scaling an in-house team in America.

So, what now?

Snowflake is a great data warehouse, but it falls short in the broader spectrum of data readiness—ingestion, modeling, orchestration, and business intelligence.

5X, on the other hand, provides a more comprehensive data readiness platform that ensures your data is clean, accessible, and actionable. Using 5X on top of Snowflake, you can create a powerful and complete data stack.

Snowflake is expanding its ecosystem with initiatives like advanced AI use cases for GPU and the Cortex suite of AI and ML features. These features can accelerate AI adoption on top 5X + Snowflake, once the foundation for data readiness has been built.

Remove the frustration of setting up a data platform!

Building a data platform doesn’t have to be hectic. Spending over four months and 20% dev time just to set up your data platform is ridiculous. Make 5X your data partner with faster setups, lower upfront costs, and 0% dev time. Let your data engineering team focus on actioning insights, not building infrastructure ;)

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Excited about the 5X + Preset integration? We are, too!

Here are some next steps you can take:

  • Want to see it in action? Request a free demo.
  • Want more guidance on using Preset via 5X? Explore our Help Docs.
  • Ready to consolidate your data pipeline? Chat with us now.

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