Learn the most cost-effective solution for loading numerous small JSON files from IoT devices into Snowflake tables. Explore external tables, pipes, streams, and COPY commands to optimize your data ingestion process.
Table of Contents
Question
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?
A. An external table
B. A pipe
C. A stream
D. A copy command at regular intervals
Answer
D. A copy command at regular intervals
Explanation
The most cost-effective way to bring the large number of small JSON files into a Snowflake table is:
D. A copy command at regular intervals
Using a COPY command at regular intervals is the most efficient approach for this scenario. Here’s why:
- COPY commands can directly load data from cloud storage (e.g., Amazon S3, Azure Blob Storage) into Snowflake tables.
- Snowflake automatically scales resources during the COPY operation, ensuring optimal performance.
- COPY commands can handle large volumes of small files efficiently by leveraging Snowflake’s parallel processing capabilities.
- Regular intervals allow for batching the files, reducing the overhead of individual file processing.
The other options have limitations:
- External tables provide a metadata layer over external data but don’t physically load the data into Snowflake.
- Pipes and streams are used for continuous data loading and change data capture, respectively, but are not designed for bulk loading small files.
Therefore, using COPY commands at regular intervals strikes the right balance between cost-effectiveness and performance when ingesting a large number of small JSON files into Snowflake.
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