Snowflake as a Data Source
Founded in 2012, Snowflake is a SaaS cloud-based data warehouse company, enabling users to store
and analyse the data that is transferred into Snowflake’s data warehouse. It was credited as being
one of the first cloud-based data warehouses, aiding businesses with the flexibility and scalability of
their data. Snowflake bridges the gap between a data lake and a data warehouse, providing users
with the benefits and advantages of both.
Snowflake runs over an AWS or MS Azure cloud infrastructure that employs hybrid architecture and
enables your business to use big data for a deeper understanding. Whilst it certainly has an
important role as a data warehouse, Snowflake can also be used as a source of data. This means that
the data that is generated by Snowflake can then be taken, processed, and sent to your own data
warehouse or storage process.
In order for you to be able to take this data into your business’s data storage facility, it must be
extracted from Snowflake as a data source and loaded into the new location. At some point during
the process, it also needs to be transformed into a format that you can use. Gravity Data’s ETL or ELT
tool can carry this process out for you.
ETL Your Snowflake Data
Extract, Transform, Load – or Extract, Load, Transform is an essential part of the process. It enables
you to use the data that is generated by Snowflake in your own business’s storage system. With
Gravity’s ETL or ELT tool, you can do this simply, easily, and securely, enabling you to gain useful
insights and use big data in making important business decisions.
Snowflake Data Source Use Cases
Snowflake is a powerful data warehouse tool for businesses but is also a valuable source of data.
Some of its use cases include:
- Access and analyse data from SaaS applications, external data sources, ERP, cloud storage,
CRM, on-premise applications, and databases
- Secure data sharing
- Decouples storage and compute capabilities
- Real-time data access
- Scalable storage solution
- Can analyse a number of data structures including Avro, CSVs, JSON, Parquet, and XML
Features of Snowflake
The data that is generated by Snowflake can be essential for making important business decisions.
Some of its features include:
- Seamless integration with platforms such as Amazon S3, Google Cloud, and Microsoft Azure
- Hybrid architecture
- SOC 2 Type II certified
- Supports PHI data for HIPAA users
- Allows encryption across networks
- Enhanced ANSI-compliant SQL engine
- Python, NodeJS, ODBC, JBDC connectors
You can visit the Snowflake website here for more information.Snowflake ETL - Try it free
Create Snowflake data pipelines
Why use Gravity to build real-time data pipelines with Snowflake
- Simple: No code set-up of your data pipelines for Snowflake
- Secure: Enterprise security and accreditations to keep your data safe
- Reliable: Gravity’s real-time engine supports high throughput of Snowflake and manages failures elegantly to ensure your data arrives at the destination
- Scalable: High volume with autoscaling to support your real-time data pipelines