Our team of Data Engineers were responsible for processing and analyzing large files of data from various sources. Some of the sources used included social media platforms, weather monitoring systems, financial databases, customer relationship management systems (CRM) in the form of CSV, text files and stored in AWS S3. Each of these sources generated large files containing millions of records, and the team needed to load those files to Snowflake.
To share the processing burden, the team quickly set up a Spark cluster with many worker nodes. Then, using the Snowflake recommended loading size, they created a script to uncompress the file and a Spark job to load and split the huge file into smaller chunks and store them to HDFS that can satisfy Snowflake's recommended size.
With Apache Spark as their data processing powerhouse, the team was able to efficiently process large files from various sources. But, the team observed that due to the additional resources that Spark jobs demand, such as HDFS and Spark clusters, their setup and maintenance can be costly. Spark job uses HDFS. As a result, when processing smaller datasets resources may not be fully utilized.
The team brainstormed and did some research to determine the best way to split and load my large file into Snowflake. They discovered that using Snowpark makes all of the essential tasks they were performing with Spark jobs possible without the use of HDFS or clusters. This was great news because it indicated that by utilizing Snowflake's built-in computation, the architecture would also be optimized and costs would be reduced.
The team decided to try Snowpark and were pleasantly surprised by how simple it was to use. Without using HDFS, the team was capable of quickly reading the large files from the Snowflake stage, uncompressing the file and splitting it into smaller chunks, storing back to Snowflake stage by eliminating external storage, and loading the splitted chunks into Snowflake.
Team observations of using Snowpark instead of Spark job:
Snowpark is designed for Snowflake:
Apache Spark, which was designed for use with HDFS. As compared to this, Snowpark is exclusively created for Snowflake. Thus, it is a more effective choice for loading data into Snowflake because it has been optimized for the design of Snowflake.
No need of HDFS or Spark cluster:
HDFS and Spark clusters are necessary for Spark jobs to run. Many organizations that lack the resources to set up and maintain these solutions may find this to be a challenge. In comparison, Snowpark doesn't need any of this infrastructure, making it a more affordable option for organizations.
Better utilization of Snowflake compute:
Whenever you are using a Spark job, you need enough resources to support the HDFS and Spark clusters. As a result, when processing smaller datasets resources may not be fully utilized. The built-in computing resources of Snowflake are used by Snowpark, which enables it to scale up or scale down compute power to handle the processing needs of any size dataset.
User-Friendly for developers:
Ascompared to Spark job, Snowpark has a less complex programming style. Because, it is built on SQL, one may use the current SQL knowledge to process data. Moreover, a variety of programming languages, such as Java, Scala, Python, and R are supported by Snowpark which helps increase SQL functionality.
Cost-Effective solution:
Due to the additional resources that Spark jobs demand, such as HDFS and Spark clusters, their setup and maintenance can be costly. The built-in compute capabilities of Snowflake, on the other hand, are used by Snowpark. These resources can scale up the computation when dealing with larger data loads and scale down when dealing with smaller data loads. Which denotes that there are no extra costs associated with using it.
Final words:
It is better to use Snowpark rather than Spark job when it comes to loading large files into Snowflake. As It is optimized as per Snowflake's architecture and doesn't require HDFS or Spark clusters, It provides better utilization of Snowflake's built-in compute, is user-friendly, and is a cost-effective solution. Consider Snowpark if you're looking for a simpler and more efficient approach to processing and loading data in Snowflake. Snowpark is worth considering.
