Dating back to 2014, Snowflake disrupted analytics by introducing the Snowflake Elastic Data Warehouse, the first data warehouse built from the ground up for the cloud with patented architecture that would revolutionize the data platform landscape. Four years later, Snowflake continued to disrupt the data warehouse industry by introducing Snowflake Data Sharing, an innovation for data collaboration that would eliminate the barriers of traditional data sharing methods in favor of enabling enterprises to easily share live data in real time without moving the shared data. This year, in 2022, under the bright Sin City lights, Snowflake intends to disrupt application development by echoing a unified platform for developing data applications from coding to monetization.
Currently, data teams looking to add value, whether it be improving their team’s analytical efficiency or reducing costs to their enterprise’s processes, develop internal data products such as ML-powered product categorization models or C-suite dashboards in whichever flavor their team is savvy in. However, to produce an external data product that brings value to their enterprise, there is only one metric executives truly care about: revenue.
To bridge the value gap between internal and external data products comes the promise of the Snowflake Native Application Framework. This framework will now enable developers to build, distribute, and deploy applications natively in the Data Cloud landscape through Snowflake. Moreover, these applications can be monetized on the Snowflake Marketplace, where consumers can securely purchase, install, and run these applications natively in their Snowflake environments, with no data movement required. It’s important to note that Snowflake’s goal is not to compete with OLTP Oracle DB workloads, but rather to disrupt how cloud applications are built by seamlessly blending the transactional and analytical capabilities Snowflake has to offer.
To round out the Snowflake Native Application Framework, a series of product announcements were made at the Summit:
- Unistore (Powered by Hybrid Tables): To bridge transactional and analytical data in a single platform, Snowflake developed a new workload called Unistore. At its core, the new workload enables customers to unify their datasets across multiple solutions and streamline application development by incorporating all the same simplicity, performance, security, and governance customers expect from the Snowflake Data Cloud platform. To power the core, Snowflake developed Hybrid Tables. This new table type supports fast single-row operations driven by a ground-breaking row-based storage engine that will allow transactional applications to be built entirely in Snowflake. Hybrid Tables will also support primary key enforcement to protect against duplicate record inserts.
- Snowpark for Python: Snowpark is a development framework designed to bridge the skill sets of engineers, data scientists, and developers. Previously, Snowpark only supported Java and Scala, but Snowflake knew what the people wanted – the language of choice across data engineers/scientists and application developers, Python. Allowing Python workloads into Snowflake removes the burden of security, a challenge developers often face within their enterprises.
- Snowflake Worksheets for Python: Though in private preview currently, Snowflake will support Python development natively within Snowsight Worksheets, to develop pipelines, ML models, and applications. This will allow streamlining development features like auto-complete and the ability to code custom Python logic in seconds.
- Streamlit Acquisition: To democratize access to data, a vision both Snowflake and Streamlit share, Snowflake acquired Streamlit, an open-source Python project for building data-based applications. Streamlit helps fill the void of bringing a simplified app-building experience for data scientists who want to quickly translate an ML model into a visual application that anyone within their enterprise can access.
- Large Memory Warehouses: Still in development (out for preview in AWS in EU-Ireland), Snowflake will soon allow consumers to access 5x and 6x larger warehouses. These larger warehouses will enable developers to execute memory-intensive operations such as training ML models on large datasets through open-source Python libraries that will be natively available through Anaconda integration.
On top of all those features released for application development, Snowflake also released key innovations to improve data accessibility, such as:
- Snowpipe Streaming: To eliminate the boundaries between batch and streaming pipelines, Snowflake introduced Snowpipe Streaming. This new feature will simplify stitching together both real-time and batch data into one single system. Users can now ingest via a client API endpoint aggregated log data for IoT devices without adding event hubs and even ingest CDC streams at a lower latency.
- External Apache Iceberg Tables: Developed by Netflix, Apache Iceberg tables are open-source tables that can support a variety of file formats (e.g., Parquet, ORC, Avro). Snowflake will now allow consumers to query Iceberg tables in place, without moving the table data or existing metadata. This translates to being able to access customer-supplied storage buckets with Iceberg tables without compromising on security and taking advantage of the consistent governance of the Snowflake platform.
- External On-Prem Storage Tables: For many enterprises, moving data into the Data Cloud is not a reality due to a variety of reasons, including size, security concerns, cost, etc. To overcome this setback, Snowflake has released in private preview the ability to create External Stages and External Tables on storage systems such as Dell or Pure Storage that can expose a highly compliant S3 API. This will allow customers to access a variety of storage devices using Snowflake without worrying about concurrency issues or the effort of maintaining compute platforms.
Between the Native Application Framework and the new additions for data accessibility, Snowflake has taken a forward-thinking approach on how to effectively disrupt the application framework. Developers should be keen to take advantage of all the new features this year while understanding that some key features such as Unistore and Snowpipe Streaming will have bumps along the road as they are still under public/private preview.
Wondering is Snowflake is the right choice for your organization? Read our high-level overview of Snowflake here or contact us to discuss your organization’s data and analytics needs.
