Using the Snowpark API, you can query and manipulate data by writing code that uses objects (like a DataFrame) rather than SQL statements. Snowpark is designed to make building complex data pipelines easy, allowing you to interact with Snowflake directly without moving data. When you use the Snowpark API, the library uploads and runs your code in Snowflake so that you don’t need to move the data to a separate system for processing.

The session begins with a review of pain points faced with legacy SIEM solutions and how Snowflake and security partners solve this problem. We will also explore different Cybersecurity Partners that further enhance your security and data needs built on Snowflake

New to Snowflake? Wondering how hard it is to go from zero to a new level of scale and performance? Join in to learn how to navigate the Snowflake interface while introducing you to some of the core capabilities, the journey starts here!

Snowflake’s Data Marketplace is a data goldmine that runs deep and wide, yet many of our demos are little more than catalog fly-overs with brief stops on the COVID and weather forecast datasets. Learn how to confidently show off Marketplace’s business value and turn low volume “data desert” accounts into high consumption Stable Edges!

We walk through a live Snowflake end to end solution of a real world industry example, harnessing 1100+ data sets available from the Data Marketplace. You will learn how build a real world solution and expose concepts that can be applied to any industry.

Our example solution for this walkthrough will be leveraging the Snowflake Data Cloud to produce risk scores that can be used by Healthcare teams to design targeted interventions across their user populations to improve health outcomes. This use case is focused on members with transportation risk factors specifically – being able to understand which members are at most risk of missing their appointment and also have transportation barriers.

Snowflake Data Clean Rooms enable multiple parties to answer questions about data while preventing drill down to PII, to overlap and join data without copying, moving or sharing the underlying data, and to perform these analyses on large amounts of data with high performance and scalability.

This lab will demonstrate how two parties can run analyses across Snowflake accounts, securely joining data on PII fields without revealing that PII to each other.

Learn how to build an end to end data engineering pipeline within Snowflake using the latest features like Schema detection, unstructured data support, observability dashboard for ingestion, multi language support (Snowpark) for various developer persona within the team (SQL, Java, Python etc).

Join us for a deep dive into Snowflake Data Governance capabilities. You will learn how Snowflake Governance features map to CDMC controls. Additionally the deep dive will focus on newer features such as Anonymization, Classification and Lineage. Learn how these capabilities work through a practical lab and how they augment existing Snowflake data governance features.

Learn how to apply Snowflake features to build pipelines for various requirements. From acquisition to ingestion, modelling and information delivery – we are going to build a use case and talk about design options that should help you position Snowflake as a best platform for solving broad data engineering problems!

In this session we will dive deep into Snowpark Python for machine learning with time series. We will use s demo as a basis and start with a deep dive on how Snowpark simplifies exploratory data analysis as well as batch inference with UDFs. From there we will embark on a “choose your own adventure” series where participants will build MLops on top of the existing code for feature engineering pipelines, explainability and drift detection.

In this session we will demonstrate how to perform cross-cloud replication of account level objects such as users, roles, grants, warehouses. We will also discuss multiple databases and shares using the new replication group construct. We will also perform group failover and fail-back with client redirection to show how to achieve business continuity when a cloud region outage occurs.

You have the best data platform in the world, how can you keep it that way? In this sessions we look at key skills such as identifying areas in which performance can be improved and analysing workloads with poor performance causing excess consumption. We will also look at supporting data and usage growth by identifying warehouses that would benefit from scaling up or out from within Snowflake.

Learn how to use Snowflake’s UI Snowsight to visualise trends, identify outliers and discover insights into your Snowflake data warehouse. In this session we will show you how to import worksheets from the classic console, organise into relevant groupings, chart trends and share data with your colleagues. We will also demo some of the relevant dashboard views, give you keyboard shortcuts to streamline setup and discuss various ways to optimise analytic views for you and your organisation.

Snowflake powers a huge variety of applications across many industries and use-cases. In this demo we will create a data application and API that uses Snowflake as its analytical engine. We will then load test against custom API endpoints and scale the backend both vertically and horizontally to see the impact. Finally, we will increase the API’s efficiency and performance using a materialization.

Using the Snowpark API, you can query and manipulate data by writing code that uses objects (like a DataFrame) rather than SQL statements. Snowpark is designed to make building complex data pipelines easy, allowing you to interact with Snowflake directly without moving data. When you use the Snowpark API, the library uploads and runs your code in Snowflake so that you don’t need to move the data to a separate system for processing.

Explore how organisations have made their journey to processing PII in the Snowflake Data Cloud. What challenges did you face? What solutions did you use to overcome those challenges? And how does it look to use the Snowflake Data Cloud to process the world’s most valuable assets?

Learn the benefit of using Snowflake for your Data Lake to simplify your cloud architecture. In this demo we will walk through how we store and process structured, semi-structured and unstructured data within your Data Lake using UDFs. We will also discuss the data governance required to proactively track and identify data dependencies to ensure views and analytics are supported through a trusted data source.

Processing unstructured data is difficult challenge for users with siloed data that traditionally relies on custom code to analyse data. Moreover, connecting unstructured data with structured content in meaningful ways is equally challenging for both administrators and data analytics teams. In this demo we will store and process unstructured data such as images, audio or video files using Snowflake. We will then build analytic dashboards using Snowsight focused on trends and insights of the sample unstructured data.