Building a Data Warehouse in the Cloud using Snowflake

Building a Data Warehouse in the Cloud using Snowflake

Given that they necessitate intricate integrations involving both code and technology, on-premises data warehouses continue to be under intense pressure. Establishing and managing data warehousing processes is essential for a business’s success when the majority of a company’s assets are in the cloud.

Companies of all sizes can transition to a modern cloud data warehouse by creating a data warehouse in the cloud using Snowflake.

Snowflake for Data Warehouse

What is Snowflake?

Snowflake is a parallel processing data environment that lives in the cloud. Data warehousing, data lakes, data engineering, data science, data application development, secure sharing, and consumption of real-time/shared data are all supported by Snowflake on a single platform. Let’s read more about Snowflake, the benefits offered to it, and more.

Advantages of Snowflake


  • Snowflake’s adaptable compute nodes (warehouses) can be configured to fit the data volume as required, enabling quick updates to the data warehouse.
  • Users can immediately obtain data insights from the model by swiftly querying Snowflake from a reporting tool.
  • Establishing partitioning procedures enables Snowflake to reduce queries for increased performance, which will enhance querying.


Snowflake offers a variety of connections to different tools to enable visualization and analysis, including but not limited to Power BI, C#, Python, and Analysis Services. Snowflake can connect to various cloud service providers to capture data from the business.


Snowflake’s administration is simple to control and accessible, giving the firm more time to disperse the data.


The data is only exposed to the portions of a business that specifically require it — thanks to strong security that can be implemented on several levels. Row-level security is possible with Snowflake because it can directly connect to a reporting service and reveal data. Additionally, it can offer read-only accounts for exposing particular databases for various business divisions.


As worksheets can be readily shared between employees, stored in folders, and used to support the implementation of Continuous Integration and Continuous Deployment (CI/CD) procedures, Snowsight, the Snowflake web interface, offers a collaborative workplace. Snowsight also provides simple ways to set up dashboards for quick analysis of significant data points and access the technologies used in a data warehouse.

Historical Data

A business can look back in time or restore data to an earlier state by using Snowflake, which preserves historical data to enable the ability to view snapshots of data from the past.

Data lake for source data organization in Snowflake

Snowflake and data lakes

Snowflake provides a number of connections to raw data files, including files from external cloud storage services and local storage.

A company can easily upload data into Snowflake tables by using the [COPY INTO] command and Snowflake stages. Businesses may simply move data from raw files to staging tables for usage in the data warehouse thanks to the [COPY INTO] command’s adjustable parameters, which include pattern matching, customised file formatting, and incremental loading.

Automating data loads using Snowflake

There are two workable ways for automating data loading into Snowflake:

Stored procedures

In Snowflake, [COPY INTO] commands can be created and executed dynamically by stored procedures using JavaScript. Businesses may tightly regulate when staging tables are updated with the most recent data when used in conjunction with a metadata framework.

Snowflake pipes (Snowpipe)

Stages that run a single [COPY INTO] statement are connected to snowpipes. Since Snowpipe can automatically ingest data as it enters the data lake, greater real-time data ingestion into Snowflake and the staging tables is possible. (Note: Internal stages cannot be used with auto ingest. A [REST API] endpoint must be used in order to use Snowpipe for internal stages.

In order to reduce overall load times into the associated staging tables, a cloud data warehouse platform in Snowflake will probably use a combination of the two loading options, using stored procedures to load files once per day and Snowpipes to ingest data as it lands in the external stage (like streaming data).

Identifying new data using streams

With Snowflake’s stream feature, new (and deleted) rows can be traced in relation to the table to which they are assigned. The related streams may then pinpoint the precise modifications that took place, even though the [COPY INTO] command will merely upload new files.

Because streams resemble tables and can be queried similarly, data warehouse algorithms can use them to gradually update the data in the dimensional model. The rows are eliminated from the stream upon execution of a Data Manipulation Language (DML) command against them.

When working with massive volumes of data, the data warehouse must incorporate streams to ensure that the extract, transform, and load (ETL) cycle runs quickly.

Optimizing tasks for orchestration in Snowflake

What is a task?

A task in Snowflake is a pre-defined SQL statement or call to a stored procedure that can be scheduled to run at particular times or following other tasks.

The SQL statements required to load the data into the staging tables, transform the data as necessary, and update the tables in the data warehouse can be directed acrylic graphed (DAG) by a company utilizing tasks.

To meet the requirements of the data warehouse, tasks can be configured in a variety of ways, including:

  • If more real-time data is required, snowflake pipes’ (Snowpipe) scheduling can be configured to run at predetermined intervals or on specific days and times.
  • Tasks can determine if they should run by checking for new data in defined streams.
  • The foundation of creating the data warehouse is the usage of tasks in Snowflake, which enables automated procedures.

How can Polestar Solutions help

A well-constructed data warehouse is a key asset to the data-driven business. Building a data warehouse in the cloud using Snowflake enables businesses to reap the benefits of proven methodologies while scaling for continued growth. Utilizing the techniques described in this article will allow a business to support and enhance analytics as they shift to using Snowflake.

An essential tool for a data-driven business is a well-built data warehouse. Businesses can optimize industry-proven processes while scalably building a data warehouse in the cloud with Snowflake.

As a Snowflake Partner, Polestar Solutions helps businesses optimize the performance, security, shareability, and scale of their cloud data warehouse. Our professionals identify underperforming areas, and business challenges, and unleash hidden insights so that you can focus on strengthening relationships with your customers.



As an AI & Data Analytics powerhouse, PolestarSolutions helps its customers bring out the most sophisticated insights from their data in a value oriented manner

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Polestar Solutions | Data analytics company

As an AI & Data Analytics powerhouse, PolestarSolutions helps its customers bring out the most sophisticated insights from their data in a value oriented manner