ETL (extract, load, transform) is a decades-old, three-step process for pulling data from a single or multiple data sources to a data warehouse where you may represent it differently. You may use ETL for various applications, including analytics and driving actionable insights.
The process involves three stages:
- Extract: First, the data is extracted, in raw form, from homogeneous or heterogeneous data sources.
- Transform: The data is transformed to a format suitable for the destination.
- Load: Lastly, the data is loaded into a data warehouse, ready to be used.
Evolution of the Modern Data Warehouses
With the expansion of databases and application systems in the early 2000s, businesses started realizing the need for a true data warehouse. Such a data warehouse could replace the inconsistent and fragmented data storage and instead provide a consolidated view of all the critical data required for strategic decision making.
A Data Warehouse stores and consolidates data from a variety of sources, and that data is utilized by modern Business Intelligence tools to support informed decisions in a competitive and ever-evolving global economy.
With the rise of the cloud and development of Big Data, however, businesses began shifting their data warehouses to the cloud instead of on-premise servers. In fact, the cloud is at the very heart of the modern data warehousing that we see today.
Today, the cloud has enabled businesses to leverage virtually unlimited storage at a little cost, on-demand scalability, and the freedom to pay for only used resources.
Now that we know the endless possibilities that the cloud presents, the next step is choosing a data warehouse option that can truly leverage the performance, scalability, and flexibility of the cloud at a reasonable price.
Snowflake is a data warehouse built specifically for the cloud. Snowflake has a unique architecture which allows compute and storage to scale independently, instantly.
The multi-cluster data architecture allows as many compute clusters to work on a single data set as required without compromising the performance. In fact, it scales up and down automatically depending on the data volume and concurrency demands.
Moreover, the best part is: infrastructure, availability, scalability, and security are all automatically taken care of.
That allows businesses to focus on digging deeper into the data instead of managing it. Read more about Snowflake here.
ETL for Snowflake: An Overview
Essentially, Snowflake ETL is a 3-step process which includes:
- Extracting data from a source and creating data files. The data files could be CSV, JSON, Parquet, XML, and several other formats as well as a mixture of several formats.
- Loading the files into an internal or external stage. Data can be staged in an internal, Snowflake managed location or an existing Amazon S3 bucket or Microsoft Azure blob.
- Copying data into a Snowflake database table. Data is loaded into the Snowflake table using the COPY INTO command.
The process may look quite simple and straightforward at first glance; however, other considerations may make manual ETL to Snowflake a lot more complicated.
For instance, performance optimization, error handling, and reliability when loading data files into external staging areas need consideration. Snowflake does not support inconsistent source and destination structure. Moreover, of course, the entire Snowflake ETL process must be repeated for each data source.
When to Say ‘No’ to traditional ETL
ETL has been around ever since the beginning of data analytics and data warehousing. It must have been the obvious choice a few years back, but now, with the virtually limitless storage and computational powers of the cloud, analysts need to access the data as soon as possible and take charge of what data they need to keep. This speed and flexibility could be achieved by pushing the transformation chokepoint to the end of the process.
This alternative approach is known as ELT. In ELT, transformations are carried out in SQL as and when needed which allows data to be loaded quickly with minimal preprocessing. ELT requires a rather simple architecture and provides analysts with the freedom to decide how the data transforms.
The growing popularity of ELT is evident from the keenness of several of the popular, third-party Snowflake ETL tools to shift to an ELT approach. You may also read the comparison of ETL vs ELT for more.
Simplifying Snowflake ELT with Blendo
Using third-party Snowflake ETL tools, like Blendo, can make the process of loading data into Snowflake completely hassle-free.
Blendo is a modern Snowflake ETL tool – that makes importing analytics-ready data in Snowflake easy and straightforward. In fact, with Blendo, it is a simple 3-step process without any underlying considerations:
- Connect the Snowflake data warehouse as a destination.
- Add a data source.
- Blendo will automatically import all the data and load it into the Snowflake data warehouse.
With Blendo, you can integrate any data source, within minutes, without having to write any code, designed to offer self-serve analytics-ready data in modern data warehouses. Don’t hesitate to contact us and try Blendo for free.