Asset managers each have their own investment style and culture, and finding the right tools to match their internal workflows can be challenging. We’re seeing many asset managers building custom solutions to serve their needs and help scale business practices across their firms. Leaders are turning to Continuus to help build and implement Streamlit apps in Snowflake to solve workflow challenges, connect data sources, and help them surface insights faster.
Our Native app team is not exclusively building in Streamlit, as we also develop solutions in Power BI, Tableau, and Angular, but the growth of Streamlit is taking off. This trend mirrors findings in a recent Gartner study, where they expect 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions. Simply put, asset managers are moving data and workloads to Snowflake and it makes sense to build visualizations and workflows directly on top of that data.
In addition to its native integration within Snowflake, Streamlit provides simplified deployment, open-source architecture, and ease of integration with other Python ecosystems. Let’s explore these benefits a bit further:
Snowflake Native
Snowflake should be viewed as platform that can improve collaboration and break down data silos, and Streamlit can unlock that power. Any organization that has their data in Snowflake can quickly and seamlessly build Streamlit dashboards. There’s no need to add copies of data or pipelines to other visualization tools, which reduces complexity in your data ecosystem.
“Anyone that is a Snowflake customer that is bringing data onto the Snowflake Data Cloud should be looking at Streamlit as a potential consumption solution for end users to visualize data, report on data, interact with data, and even be able to create and edit data.”
- Matt Moeser, Continuus Founder and CEO
Streamlit can light up AI features to activate LLM’s wrapped under Snowflake’s Arctic suite to give asset managers the power to chat with their data or sift through massive, previously unconnected data sets to surface insights. We see very few limitations in terms of data and AI integrations and have found the ease of integrating AI within Streamlit to be extremely powerful.
Simplified Deployment
Streamlit simplifies the deployment process so you can easily share your apps via Streamlit Sharing, deploying them on cloud platforms or within internal networks. Power BI, Tableau, and Angular deployments can be more complex and require specific infrastructure or services.
Ken Mott, Principal Consultant at Continuus, puts it simply: “All Streamlit applications are web-based, making them easily accessible from any device with an internet connection. Asset managers can share applications with clients, colleagues, or stakeholders without requiring them to install any initial kind of software.” Role-based access controls, as well as the visual UI for updating apps in Snowflake, makes deploying updates transparent and seamless.
Open-Source
Streamlit is open-source and therefore free to use, which can be a significant advantage for individuals, small teams, or even larger organizations looking to minimize costs. While Power BI and Tableau are commercial products that require licenses and contracts, Streamlit gives you more flexibility to entry.
If firms don’t have the bandwidth to build an app themselves, Continuus’ Native App team can be a partner in the process. We work to understand the workflow and data sources and provide a transparent “time and materials” pricing model for the solution. At the end of the engagement, we help train the users and support team, so firms walk away with a custom app solution that they now own in-house and have the freedom to enhance as their investment strategy evolves.
Python Integration
Streamlit natively integrates with the Python ecosystem, making it easy to use alongside libraries like Pandas, NumPy, Matplotlib, and machine learning frameworks. The Python trend isn’t slowing; Snowflake's Data Trends for 2024 shows usage of Python grew a staggering 571%, which is considerably more than any other language year-over-year. While Power BI and Tableau can integrate with Python and R, the process is not as seamless and requires additional setup.
Another trend we’re seeing is many buy-side firms, big and small, investing in employee growth and development around Python and hiring developers with Python skills. Python’s growth can be attributed to its readability with a syntax like English, versatility specifically around machine learning, and its large support community. It is a natural evolution for an asset manager to build workflow solutions using Streamlit and Python and to make this the backbone to support the business.
Overall, Streamlit is helping firms solve workflow challenges, break down data silos, monetize data sets, and even chat with their data in a very meaningful way. Ultimately, it solves the “swivel chair problem” where business users need to go to a Bloomberg terminal, a FactSet workstation, and Salesforce all in the same few minutes to gather the information they need. Streamlit pulls the disparate data sources into one screen seamlessly.
We’re encouraging our clients to take a look at the power of Streamlit by reviewing some of the apps we’ve built, like the Continuus Cargo Shipping App, powered by FactSet data, in the Snowflake Marketplace. Reach out to Continuus if you’d like to learn more about how asset managers are leveraging Streamlit to improve efficiency.
Sources:
Gartner Identifies Top Trends Shaping the Future of Data Science and Machine Learning
Data-Trends-2024-Financial-Services-final.pdf (snowflake.com)
