You can use data warehouses or data lakes as a repository for data management and analytics tasks. Both of these solutions have their advantages and disadvantages. A data warehouse is the best if your organization works only with structured data. Data lake is a suitable choice if your work is based entirely on raw or unstructured data. 

However, if you use both structured and unstructured data, it can be challenging to store data at two different destinations. It can result in data silos and increases expense on the storage and processing of data. 

A new class of data architecture has been developed to eliminate the above issues. This hybrid solution combines the best elements of a data lake and a warehouse and is called a data lakehouse. The low-cost storage and data management capabilities have made Snowflake a very popular data lakehouse. 

This article provides comprehensive information on Snowflake data lakehouse. It also explains the steps to build a data lakehouse and its use cases in various real-world scenarios.

What is Data Lakehouse?

A data lakehouse is a unified platform offering the best features of a data lake and a data warehouse. It is a type of data management architecture that allows you to store data in structured, semi-structured, and unstructured forms like a data lake. A data lakehouse also performs data warehouse tasks, such as data extraction, cleaning, transformation, and maintenance of data security, for efficient data analytics. 

Some prominent features of data lakehouse are as follows:

  • Data lakehouse is a low-cost data storage solution that accepts all data formats, including unstructured data. 
  • It provides data management features like schema matching, data governance, ETL, and other data processing capabilities. 
  • Data lakehouse supports ACID (Atomicity, Consistency, Isolation, and Durability) transactions to ensure data consistency when several users refer to your data simultaneously. 
  • It offers end-to-end streaming services through real-time data ingestion. 
  • Data lakehouse facilitates enhanced query performance and seamless handling of workloads through independent scaling of compute and storage resources. 

Is Snowflake a Data Lakehouse?

Snowflake’s versatile nature offers the benefits of both a data lake and a warehouse. It is essentially a data warehouse but has evolved over time to incorporate several features that make it a suitable data lakehouse.

For instance, earlier, you could store only structured or semi-structured data, but now, the platform also accepts unstructured data. This feature eliminates the need to use different platforms for storing different data types and also resolves the issue of data silos. 

Snowflake supports Apache Iceberg tables, which you can use like standard Snowflake tables to manage your workloads. It enables you to independently scale the compute and storage resources to enhance query performance.

Additionally, Snowflake offers robust security features such as granular access control, auditing, and encryption to ensure data governance. It has become a very efficient data lakehouse solution now because of this multi-functionality. 

Difference Between Snowflake Data Warehouse and Data Lake

Here is a brief Snowflake data lake vs. data warehouse comparison: 

FeaturesSnowflake Data WarehouseSnowflake Data Lake
Data StorageAs a warehouse, Snowflake enables you to store only structured and semi-structured data. You can store structured, semi-structured, and unstructured data while using Snowflake data lake. 
Schema Snowflake warehouse supports schema-on-write, ensuring schema alignment between source and destination platforms before the data transfer. As a data lake, Snowflake supports schema-on-read, and you can align schemas after the data transfer. 
Data Integration and ProcessingYou have to first clean and process data before integrating it into the Snowflake warehouse. You do not have to process data before ingestion in the data lake. 
UsageIt is highly useful for data analysis and reporting. It is useful for advanced data analysis as it supports the storage of raw data, which can be used to train models for AI and machine learning applications. 
CostsThe cost of using Snowflake as a warehouse is high because of the usage of compute resources. The cost of using Snowflake as a data lake is lower than that of the warehouse. 

How to Build a Data Lakehouse using Snowflake?

The steps below explain how to implement a data lakehouse in Snowflake:

Step 1: Create a Snowflake Account

Log in or sign up for Snowflake if you do not already have an account. You can initially opt for a free trial to explore the platform. 

Step 2: Set up Data Lake Storage

First, you must create a storage bucket in AWS S3 or Azure Blob Storage and set up access policies to restrict access to the storage buckets. 

Step 3: Data Ingestion

It involves collecting and transferring data from multiple sources to Snowflake. You can use Snowpipe for continuous or real-time data ingestion. Alternatively, you can also use third-party data ingestion platforms like Hevo Data to ingest data to Snowflake. 

Hevo Data is a no-code ELT platform that provides real-time data integration and a cost-effective way to automate your data pipeline workflow. With over 150 source connectors, you can integrate your data from multiple sources and transform it before loading it into a Snowflake data lakehouse.

You can also leverage other robust features, such as auto schema mapping, incremental data loading, and recoverability, to enhance your data. 

Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

Step 4: Data Processing and Transformation

Now, you can process and transform the ingested data into your desired format. For this, you can leverage Snowpark, which enables you to transform data using programming languages such as Python, Java, or Scala. 

Step 5: Data Governance and Security

This step comprises several components, as explained below:

  • Multifactor Authentication: Snowflake enables you to set up multifactor authentication using Google authenticator or SMS. You can set up this using the following command:
  • Role-based Access Control: Setting up role-based access control allows you to grant permission to different domains in your organization based on their roles. For instance, you can set up various accessibilities for admin, data analyst, or data engineer. Here is the command to set up role-based access control for data analysts:
CREATE ROLE analyst_role;

GRANT USAGE ON WAREHOUSE <warehouse_name> TO ROLE analyst_role;
  • Data Masking: It is a technique to protect sensitive data by making it unusable for unauthorized users. You can use the following command for it: 
CREATE MASKING POLICY credit_card_masking_policy AS (val VARCHAR) RETURNS VARCHAR ->



ELSE val


Step 6: Data Analysis

Now, you can query and analyze the data stored in Snowflake. You can use different Snowflake querying features and functions for specific analytics. Thus, by following these steps, you can effectively use Snowflake for data lakehouse operations. 

How to Build an Open Data Lakehouse on Snowflake with Apache Iceberg

Snowflake Data Lakehouse: Apache Iceberg
Apache Iceberg

Apache Iceberg is an open-source table format for managing data in data lakes. Its primary function is to track how you manage the data files that make your table. It maintains metadata files that contain all the information about your data files, such as schemas, partitions, or locations.

The Iceberg table also enables you to use SQL commands to rewrite, update, merge, or delete data from the table. Using it, you can handle massive data loads on a petabyte scale in Snowflake. Additionally, Iceberg tables support schema evolution, partition evolution, and time travel to enhance your data lake architecture. 

You can use Iceberg to build Snowflake data lakehouse architecture. To do this, you need to understand how the Iceberg table catalog works. It is a collection of tables that keeps the record of all the metadata of your data tables. You can easily retrieve this metadata from the catalog. It also enables you to perform SQL operations like updating or removing data within any table. 

When using Iceberg tables with Snowflake, you can decide where to store the metadata of your tables. You can use Snowflake or any other external service (AWS, Azure, or GCP) as an Iceberg table catalog. The external catalog options can be used when you only want Snowflake to read data from these services.

You use catalog integration with an external service as a catalog. It is an object that stores all metadata information of the Snowflake table in external services. 

Snowflake Data Lakehouse: Iceberg table Catalog Integration
Iceberg table Catalog Integration

If you are using Snowflake on AWS, you have to set the CATALOG_SOURCE parameter as GLUE in the CREATE CATALOG INTEGRATION command. It enables Snowflake to retrieve metadata from Iceberg tables in the AWS Glue Data Catalog.

The other tools, like Spark, can continue to write to the tables during this process. The OBJECT_STORE catalog integration is done when you use Snowflake with other cloud storage services, such as Azure or GCP. You can read data from the Iceberg tables in these platforms using Snowflake. 

Snowflake Data Lakehouse: Snowflake as Iceberg Catalog
Snowflake as Iceberg Catalog

It is better to use Snowflake as an Iceberg table catalog source as it provides easier access for reading and writing data. You do not need catalog integration while using Snowflake as an Iceberg catalog. To achieve this, you only need to set the CATALOG parameter to SNOWFLAKE in the CREATE ICEBERG TABLE com. 

You can also convert the external services Iceberg table catalog to the Snowflake Iceberg catalog. Snowflake allows you to do this using simple SQL commands. It converts the Iceberg table’s catalog source from GLUE or OBJECT_STORE to SNOWFLAKE without copying or rewriting the files. Even after conversion to Snowflake as Iceberg catalog, your data resides in the open format cloud storage.

The use of Iceberg tables and information of metadata improves data management, governance, and query performance of Snowflake data lakehouse. After Snowflake Iceberg integration, you can follow the steps mentioned in the earlier section to build a data lakehouse in Snowflake. 

Snowflake Data Lakehouse Use Cases

Some important use cases of Snowflake data lake integration are as follows:

  • Advanced Analytics: As a raw data repository, data lakehouse allows you to train and deploy complex machine learning models. You can use these models to perform advanced data analytics, such as sentiment analysis, recommendations, prediction, anomaly detection, and classification, to gain valuable customer insights. 
  • Business Intelligence: Snowflake data lakehouse facilitates the use of BI tools like Tableau, Power BI, or Looker to create interactive reports and dashboards. These reports help you better and faster understand market trends and customer preferences.
  • Data Monetisation: You can use Snowflake data lakehouse to process, analyze, and sell data for economic benefits. The purchasers can use your data to conduct segmentation, market analysis, or other data analysis purposes. 


This blog provides holistic information on what is Snowflake data lakehouse. It explains how to implement data lakehouse in Snowflake and its use cases. Data ingestion is a critical step in building a Snowflake data lakehouse. You can use Hevo data, a zero-code data ingestion tool, to load data into Snowflake.

It offers a vast library of connectors, robust data transformation, and security features to facilitate efficient data ingestion. You can schedule a demo to leverage these advantages today!


  1. What is the difference between Snowflake and AWS Data Lake?
  • Both Snowflake and AWS offer data lake services. However, the major difference between the two is that Snowflake is a complete data lake solution in itself.
  • On the other hand, AWS provides a combination of services such as S3, Glue, or Redshift for data lake solutions. 
  1. What are some other examples of data lakehouse services?
  • There are various data lakehouse services besides Snowflake, such as AWS Data Lakehouse, Databricks Deltalake, Azure Data Lakehouse, and Oracle Data Lakehouse. 
Sarad Mohanan
Software Engineer, Hevo Data

With over a decade of experience, Sarad has been instrumental in designing and developing Hevo's fundamental components. His expertise lies in building lean solutions for various software challenges. Sarad is passionate about mentoring fellow engineers and continually exploring new technologies to stay at the forefront of the industry. His dedication and innovative approach have made significant contributions to Hevo's success.

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