Beyond the Data Warehouse: Real-Life Business Value Showcased at Snowflake Summit 2019
5月 22, 2019 | 3 Min Read
Author: Snowflake Staff
Customers are expanding the role Snowflake plays in their data architecture by building modern data platforms for a variety of analytics use cases such as BI reporting and ad hoc querying as well as data science use cases such as forecasting, personalization, and anomaly detection.
In our “Beyond the Data Warehouse” track at Summit, learn how Snowflake customers are successfully implementing their data lake strategy and finding solutions to business problems for the use cases mentioned above.
Customers use Snowflake in one of two ways:
- As a modern data lake that is a replacement for their legacy data lake platform
- As a superlative processing engine to complement their existing data lake platform
In this track’s sessions, Snowflake customers and product managers will show you how to use Snowflake to supercharge your existing data lake investment or use Snowflake as a replacement for your legacy system.
Moreover, Snowflake customers will show you how they are using popular industry tools to run machine learning and data science use cases on their data stored in Snowflake.
Migrating Off Hadoop
Yieldmo is an example of a customer who replaced its legacy data lake with Snowflake.
In a session titled “Yieldmo: Building the Modern Data Lake With Snowflake,” Indu Narayan, VP Data, will detail how Yieldmo successfully migrated its data lake from Hadoop to Snowflake.
Today, Yieldmo’s Snowflake data lake supports mission-critical business functions such as analytics and BI reporting as well as distributed machine learning pipelines. Attend this session to learn how Yieldmo migrated off Hadoop, as well as the benefits of its data lake on Snowflake.
Devon Energy’s Modern Data Lake
Devon Energy had hard-to-handle, underperforming solutions: an underused data lake and a cumbersome data warehouse.
For another example of using Snowflake as a data lake, attend a session by Larry Querbach, Enterprise Data Architect, which details how Devon Energy consolidated systems into a single repository.
The session, titled “Devon: From Many Pools to Just One Modern Data Lake,” details how Devon Energy built a modern, cloud data lake using Snowflake, Databricks, and Attunity. The new data lake has more closely aligned the IT team with business users.
Complementing an Existing Data Lake
Prior to implementing Snowflake, Risk Management Solutions (RMS), the world’s leading catastrophe risk modeling company, struggled with burst compute on its data lake to provide ad hoc query capabilities for its analysts. A big challenge for RMS was the schema of the parquet files and the query patterns were variable and unknown; hence, ingesting the terabytes of data in Snowflake was not helpful. Learn how RMS is using Snowflake to solve these problems and maintain a high level of servicefor its analysts.
Optimizing Time to Value with Machine Learning on Snowflake
Time is money. This age-old saying is particularly true for Harmoney, a marketplace lending platform providing personal loans to consumers in New Zealand and Australia.
Harmoney needed to quickly find a solution to avert a previously unseen credit issue that had serious implications for the business. Therefore, time to value was a key requirement for the project. It chose two SaaS vendors, Snowflake and DataRobot, to be the foundation of its solution. The combination of Snowflake and DataRobot enables Harmoney to rapidly produce and deploy machine learning (ML) models, which are executed in near real time.
In this session, titled “Averting Credit Issues With Predictive Modeling,” Andrew Cathie, Chief Data Scientist at Harmoney, explores the business problem, how Harmoney applied an ML model to help minimize the recurrence of the problem, the practical issues associated with deploying this model, performance monitoring, and ongoing refinements.
Snowflake Product Announcements
In addition to attending customer sessions, attend sessions presented by the Snowflake product team to hear some exciting news! Snowflake product managers will announce details of three new features that will soon be available on Snowflake.
Sessions covered by the Snowflake product team include:
- Overview of AI, ML, and Data Science in the Snowflake Ecosystem
- Using TensorFlow with Snowflake Secure Data Sharing
- End-to-End Machine Learning with Snowflake and XGBoost
- How to Unify Your Data Lake With Snowflake
- Deep Dive on Multi-Cloud External Stages in Snowflake
In addition to the sessions mentioned in this post, attend this track’s sessions to hear from additional Snowflake customers: Expedia, ASICS, Strava, ShopRunner, Malwarebytes, and more. Visit the session track page now to see the full list of sessions.
See you there!