Introduction
In today’s world that is largely data-driven, organizations depend on data for their success and survival, and therefore need robust, scalable data architecture to handle their data needs. This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes.
Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organization. For this reason, Snowflake is often the cloud-native data warehouse of choice. With Snowflake, organizations get the simplicity of data management with the power of scaled-out data and distributed processing.
Snowflake is built on top of the Amazon Web Services, Microsoft Azure, and Google cloud infrastructure. There’s no hardware or software to select, install, configure, or manage, and that makes it ideal for organizations that do not want to dedicate resources for setup, maintenance, and support of in-house servers.
What also sets Snowflake apart is its architecture and data sharing capabilities. The Snowflake architecture allows storage and compute to scale independently, so customers can use and pay for storage and computation separately. And the sharing functionality makes it easy for organizations to quickly share governed and secure data in real time.
Using Snowpipe for data ingestion to AWS
Although Snowflake is great at querying massive amounts of data, the database still needs to ingest this data. Data ingestion must be performant to handle large amounts of data., and without that, you run the risk of querying outdated values and returning irrelevant analytics.
Snowflake provides a couple of ways to load data. The first, bulk loading, loads data from files in cloud storage or a local machine. Then it stages them into a Snowflake cloud storage location. Once the files are staged, the “COPY” command loads the data into a specified table. Bulk loading relies on user-specified virtual warehouses that must be sized appropriately to accommodate the expected load.
The second method for loading a Snowflake warehouse uses Snowpipe. Snowpipe continuously loads small data batches and incrementally makes them available for data analysis. Snowpipe loads data within minutes of its ingestion and availability in the staging area. This provides the user with the latest results as soon as the data is available.
Limitations in using Snowpipe
While Snowpipe provides a data ingestion method that is continuous, its limitation is that it is not real-time. Data might not be available for querying until minutes after it’s staged. Throughput can also be an issue with Snowpipe. The writes queue up if too much data is pushed through at one time.
Import Delays
When Snowpipe imports data, it can take minutes to show up in the database and be visible. This is too slow for certain types of analytics, especially when near real-time is required. Snowpipe data ingestion might be too slow for three use categories: real-time personalization, operational analytics, and security.
Real-Time Personalization
Many online businesses employ some level of personalization today. Using minutes- and seconds-old data for real-time personalization can significantly grow user engagement. And that could be hindered by Snowpipe’s limitations in that area.
Operational Analytics
Applications such as e-commerce, gaming, and the Internet of things (IoT) commonly require real-time views of what’s happening. This enables the operations staff to react quickly to situations unfolding in real time. Lack of real-time data using Snowpipe would affect this.
Security
Data applications providing security and fraud detection need to react to streams of data in near real-time. This way, they can provide protective measures immediately if the situation warrants. These could be impacted when Snowpipe is used.
Throughput Limitations
A Snowflake data warehouse can only handle a limited number of simultaneous file imports. You can create 1 to 99 parallel threads. But too many threads can lead to too much context switching. This slows performance. Another issue is that, depending on the file size, the threads may split the file instead of loading multiple files at once. So, parallelism is not guaranteed.
Workarounds prove expensive
To overcome the limitations of speed, you can speed up Snowpipe data ingestion by writing smaller files to your data lake. Chunking a large file into smaller ones allows Snowflake to process each file much quicker. This makes the data available sooner.
Smaller files trigger cloud notifications more often, which prompts Snowpipe to process the data more frequently. This may reduce import latency to as low as 30 seconds. This is enough for some, but not all, use cases. This latency reduction is not guaranteed and can increase Snowpipe costs as more file ingestions are triggered.
One way to improve throughput is to expand your Snowflake cluster. Upgrading to a larger Snowflake warehouse can improve throughput when importing thousands of files simultaneously. But, this again comes at a significantly increased cost.
AWS Glue to Snowflake ingestion
In any data warehouse implementation, customers take an approach of either extraction, transformation, and load (ETL) or extraction, load, and transformation (ELT), where data processing is pushed to the database. For either method, you could either use a hand-coded method or leverage any number of the available ETL or ELT data integration tools.
However, with AWS Glue, Snowflake customers now have a simple option to manage their programmatic data integration processes without worrying about servers, Spark clusters, or the ongoing maintenance traditionally associated with these systems.
AWS Glue provides a fully managed environment that integrates easily with Snowflake’s data warehouse as a service. . With this, developers now have an option to more easily build and manage their data preparation and loading processes with generated code that is customizable, reusable, and portable with no infrastructure to buy, set up, or manage.
Together, these two solutions enable customers to manage their data ingestion and transformation pipelines with more ease and flexibility than ever before.
With AWS Glue and Snowflake, customers get the added benefit of Snowflake’s query pushdown, which automatically pushes Spark workloads, translated to SQL, into Snowflake. Customers can focus on writing their code and instrumenting their pipelines without having to worry about optimizing Spark performance. With AWS Glue and Snowflake, customers can reap the benefits of optimized ELT processing that is low cost and easy to use and maintain.
Conclusion
Snowflake’s scalable relational database is cloud-native. It can ingest large amounts of data by either loading it on demand or automatically as it becomes available via Snowpipe.
Unfortunately, in cases where real-time or near real-time data is important, Snowpipe has limitations. If you have large amounts of data to ingest, you can increase your Snowpipe compute or Snowflake cluster size, but at additional cost.
AWS Glue and Snowflake make it easy to get started and manage your programmatic data integration processes. AWS Glue can be used standalone or in conjunction with a data integration tool without adding significant overhead. With AWS Glue and Snowflake, customers get a fully managed, fully optimized platform to support a wide range of custom data integration requirements.
