Data is at the heart of everything for consumer companies, and then comes big data, which is a massive collection of high volume data. Big data continues to grow exponentially over time and it requires advanced tools and methods to store, process and utilise it efficiently. As big data grows, consumer businesses and companies are embracing cloud data warehouses. This is where Snowflake, one of the world’s top cloud data warehouse companies comes into play.
Offered as a SaaS (Software-as-a-Service) product, Snowflake works as a fully managed, end-to-end solution for data warehousing, data analytics, data engineering and data application development. Due to its ability of supporting concurrent workflows simultaneously, Snowflake is a powerful platform for putting data to use, as and when required.
For data-centric companies, Snowflake enables storage, computing and analysis of data workloads as one single copy, which helps multiple users and administrators to stay on the same page and follow the same source of truth. Further, due to Snowflakes’ hybrid architecture for storage and computing, multiple users do not have to compete for resources, and all workloads can leverage the potential of the platform concurrently without any usage limits or scale limitations.
Let us try to understand what makes Snowflake unique multi-cluster architecture different from traditional data warehouse architectures – fundamentally, Snowflake combines the benefits of shared disk architecture (for sharing shared data on single storage) as well as shared nothing architecture (for sharing a part of data in each node). This way, Snowflake can process the massive data queries parallely.
Snowflake’s data warehouse comprises of three key layers:
- Database Storage
- Query Processing
- Cloud Services
Now let us understand the advantages of using Snowflake cloud data warehouse solution:
- High Performance and Speed: Due to the always-on and available nature of cloud, businesses can load and use data faster, even at higher volumes.
- Ease of Use: Snowflake provides data administrators a simple and intuitive user interface, enabling users to access and process data using a multi-cluster architecture.
- Better Flexibility: Snowflake allows users to access both services and warehouse at the same time, thus it allows users to choose the required use-case at will.
- Cost-Effective: Snowflake does not count idle time in its pricing. It only considers usage time along with the computing and data storage cost, making it cost-effective.
- Scalable: Snowflake allows instant scaling of data storage and usage needs while allowing concurrent, simultaneous workflows without redistributing data.
- Seamless data sharing: Snowflake allows data sharing between users among themselves and also with other consumers through reader accounts.
Now that we know about Snowflake, its platform and benefits, let us dive deep into what Snowflake ETL is and why it is a superior solution to traditional ETL alternatives.
So what is ETL?
ETL stands for Extract, Transform, Load) is a process used for pulling data from one or multiple sources, and then using it for deriving insights and analytics.
Extract is when the data is extracted from a source, in a raw form. At this stage, data is simply pulled from the desired source or application, and the filtration starts later.
Transform is when the extracted data is transferred or modified to meet a suitable format
Load is when the transformed data is fed into a data warehouse for further use.
In the modern age, database systems and applications consolidate data from various sources to be utilised by businesses for making data-informed decisions. This means Snowflake’s multi-cluster architecture allows computing and storage of data independently and instantly.
ETL for Snowflake
Snowflake ETL is a three step process, which includes:
- Extracting Data: this step involves deriving data from a source and creating data files. The files can be a variety of formats such as – XML, CSV, JSON, Parquet.
- Loading Data: this step involves fetching the data into storage. The storage types can vary between – internal storage or in a Snowflake managed storage location in the cloud.
- Copying Data: is the step where data is copied into a Snowflake database table using various commands. Snowflake also supports bulk loading of data.
Preparing your business data to work with Snowflake
If your business is ready to embrace the power of a cloud data warehouse solution like Snowflake, you are on the right path. Now all you need is a trusted partner to transition your business data to the cloud seamlessly and securely.
Snowflake continues to remain the future of data warehousing platform for big data companies and data centric companies. Further, Snowflake is built from the ground up to enable complex data pipeline building a breeze without impacting the source data.
This is where Bryteflow comes in to help businesses like yours. Bryteflow is a modern Snowflake ETL tool which makes the process of loading data into Snowflake easy, fast and real-time.
With Bryteflow, your business can integrate data from multiple sources, without having to write any special code. This means your business can run its own self-serving data analysis unit in the cloud. No hardware or software required.
Visit Bryteflow.com for a free trial today!