How Software Companies Can Build Scalable Embedded Analytics Apps with Snowflake
Okt 14, 2020 | 2 Min Read
Snowflake Thought Leadership
Customers of B2B companies rely on insights from applications to grow their business, secure their infrastructure, make business decisions, and more. Unless your B2B company offers a rich set of analytics within its product, your customers likely demand nightly data dumps from your application so they can analyze application data with their own BI stack. Although this approach once met the needs of some audiences, many software companies are now gaining a competitive advantage by embedding analytics directly into their apps.
Requiring customers to rely on separate BI tools to gain insights from your application has significant limitations. For one thing, traditional BI tools lack the context needed to deliver the best insights, and data is often stale before it reaches the user because of the time needed to move, extract, and transform it so it can be queried.
Embedded analytics—which integrate rich data insights within the context of an application so customers can see visualizations in their workflow without going to a separate product—are a better solution. For example, a sales automation application might include a predicted sales forecast based on different “what if” scenarios that are specific to the user’s territory. This forecast would be presented within the normal sales planning workflow of the app, rather than through offline reports from the BI team.
By natively embedding analytics, you can differentiate your application from that of competitors whose customers are still reliant on nightly data dumps and offline reports. You can also create new revenue streams. (For example, B2B software companies can offer embedded analytics as an add-on service to customers.)
Developing and scaling embedded analysis applications has challenges, and maintaining them can add significant operational burden. As the number of users increases, applications hit concurrency limits and performance degrades, and reports that normally run in seconds or minutes could take hours to execute during peak load. Building an embedded analytics application can also divert limited resources that are better spent on innovation.
Snowflake’s platform can help overcome those challenges. For more details, download our white paper, How Snowflake Enables You to Build Scalable Embedded Analytics Apps.
Deliver Virtually Unlimited Concurrency as Your Application Grows
At the core of Snowflake’s platform is a unique multi-cluster, shared data architecture that instantly and automatically adds compute resources, which all share the same tables without contention for resources. This capability enables embedded analytics applications to support a virtually unlimited number of simultaneous users.
Deliver Consistently Fast Analytics Regardless of Load
Unlike traditional analytics solutions that slow down as the number of users increases, Snowflake delivers consistent performance, even with variable and unpredictable load. Snowflake isolates workloads into independent virtual warehouses that are provisioned on demand and don’t contend for resources. There is no longer a need to dedicate expensive infrastructure to ensure your application meets performance SLAs for your biggest customers.
Protect Your Product Margins
Because Snowflake scales compute resources up and down automatically, you don’t have to forecast demand and statically provision capacity for the expected peak load. This prevents you from paying for idle resources when demand is low, which hurts product margins. It also improves the customer experience by preventing slowdowns or crashes during peaks.
Deliver Fresh Insights on All Your Data, Structured and Semi-Structured
With traditional data warehouse technologies, semi-structured data such as JSON requires complex data pipelines that delay time to insight and require ongoing maintenance. With Snowflake, semi-structured data is supported natively using a proprietary VARIANT data type. JSON data loads directly into a relational column where it can be immediately queried along with structured data using standard SQL.
Minimize Operational Burden
Analytics applications built on legacy data warehouse technologies require significant administration such as frequent software patches, manual optimizations, and regular backups. Snowflake is offered as a service and removes nearly all the administration overhead, so your team can focus on innovation. With Snowflake, there is no infrastructure to provision or manage and no indexes to create, and optimizations are automatic.
To learn more about how Snowflake’s platform enables application developers to differentiate their applications with embedded analytics that deliver a great experience for their customers, download our white paper, How Snowflake Enables You to Build Scalable Embedded Analytics Apps.