Join this live instructor-led Virtual Hands-on Lab, where we guide you through simple steps to unify your data, transform it without being a SQL expert, and ask natural language questions using Amazon Q. Even if you are new to BI reporting or short on time, learn how to generate and share insights with your business teams in the shortest possible time.

What You Will Explore and Learn

* Ingest your data into Snowflake in just a few simple steps.
* Transform and enhance your data with third-party data from the Snowflake Marketplace.
* Load your curated data from Snowflake into Amazon QuickSight using native integration.
* Ask business questions in natural language with Amazon Q, including ML-driven insights.
* Watch QuickSight generate reports for you, with easy customisation options.
* Publish dashboards to share insights quickly with your business users.

Join us for a live guided tour of key functionality in Snowflake. An instructor will walk you step-by-step through how to get started with sample code and data using the free trial. You can follow along and get your questions answered in real-time and is the perfect primer to get hands on for new Snowflake users.

What You Will Explore and Learn
* How to navigate Snowsight, the Snowflake user interface, and other connection options
* Insights into data ingestion and transformation in Snowflake
* How to work with different data types in Snowflake including semi-structured and unstructured data
* Setting up virtual warehouses and getting started with good financial governance
* Basic data governance and security concepts
* Access data from data sharing and the Snowflake Marketplace
* Creating and sharing data analysis

Join this instructor-led virtual hands-on lab to learn how to build an end-to-end pipeline using Snowflake pandas API, scheduling it with Serverless Tasks, all in Snowflake Notebooks.

What You Will Explore and Learn
* how to create Snowpark Pandas dataframe from a Snowflake table
* how to aggregate data to create new features
* how to Join Snowpark Pandas dataframes
* how to save a Snowpark Pandas dataframe into a Snowflake table
* how to create a serverless task to schedule the feature engineering

Faced with constant pressure to deliver real-time insights that empower businesses to make informed decisions and stay ahead of the competition? Struggling with the technical challenges of managing data silos, diverse formats, ingestion processes, transformation, and complex reporting tools—all while striving to deliver insights faster? Learn how we make it easy and seamless to handle any data, interact with it using natural language, and leverage Gen AI to build the reports your business needs in minutes.

You managed to consolidate all the data into one place, creating a single source of truth for the company. However, simply centralising the data isn’t enough to drive meaningful outcomes. You’ve convinced the IT teams and data experts across the lines of business to focus on diagnostic analytics – building reports and BI dashboards on top of the data. This will help visually uncover trends, identify issues, and enable the company to take corrective actions based on these insights.

You now have over 700 dashboards created by users to analyse data, and department heads are flooding you with weekly reports leaving you completely overwhelmed! While this level of democratisation initially seemed promising, the sheer volume has become unmanageable. On top of that, IT teams are stretched thin, struggling to keep up with the constant requests from various departments to create and maintain dashboards. Clearly, there has to be a more efficient way to interact with both structured and unstructured data. Meanwhile, your data analysts and business users are eager to join the Snowflake bandwagon.

More personas are now working with data, which is a step in the right direction, and you’re thrilled to see this progress. However, diagnostic analytics is only getting you so far in terms of outcomes. You want to explore how Machine Learning can drive business value. Moving into predictive analytics could help the business make proactive decisions based on historical data. Only one challenge is, creating a model is easy but how can you take it into production?

Now that you’re diving deeper into data, analytics, and ML models, you’ve hit a critical roadblock: governance. Private data is at risk of being misused and it could fall into the hands of malicious actors or even benign actors who may not know better. What you should have done earlier in step 1, you have to do it now and fast. To build trust in your data and ML initiatives, you must act swiftly to implement robust frameworks that safeguard sensitive information and maintain compliance.

Your models are running smoothly, delivering insights you never imagined, and your business teams across sales, marketing, and finance are making real-time decisions powered by machine learning. This success has grabbed the attention of your C-suite, who now wants to showcase these capabilities to the board. They want to highlight how easy it is to interact with machine learning models without needing to worry about the underlying complexities. Can you quickly create an app to demonstrate the value?

Join Us for the Final Session of the Day with AWS: Simplifying Gen AI

Your business teams need instant insights to make real-time decisions and stay competitive. Yet, challenges like data silos, complex ingestion, and mastering multiple tools slow things down. Data analysts work tirelessly to bridge the gap, while Gen AI holds promise—but also introduces complexity and production challenges.

What if you could leverage Gen AI without the steep learning curve or hurdles, delivering actionable insights in minutes while effortlessly bridging business needs and technical challenges? Learn the steps to achieve all of this (from ingesting, transforming, enhancing, securing, and delivering business insights) easily and in minutes, with all the challenges around data handling simplified.

You created the models and the apps but your models are slowly deteriorating because of concept drift where your data isn’t enough to drive predictions. You have to look at other sources of data and integrate it with your own data to get more contextually rich insights. Is there a way you can buy some data off the shelf or maybe even get some free data to help you with better data, better features and then eventually better predictions?

While your ML models and data from the Marketplace are driving great results for the business, the slowest link is holding everything back. You operate within a large ecosystem of suppliers, vendors, and agents who share data with you via FTP, which isn’t the most secure or reliable way to handle their data. The challenge is that not all of them are on Snowflake, but you want to use the platform to bring it all together without having to worry about governance, security, or privacy.

Things are starting to get complicated. Snowflake isn’t the only platform you use for your data needs; you have other technologies as well, and you need to ensure that you can work with those platforms while leveraging the power of Snowflake. How can you ensure interoperability without sacrificing the simplicity of the platform?

Wait… your finance and procurement teams just called. You think you know what they’re going to ask – how much is this rapid transformation costing the business? You’ve always thought that time is money, but how can you save on both? Plus, you need to prepare for your upcoming meeting with the board, where you’ll be discussing cost optimisation across the business in a big way.

Ok, that was an interesting conversation. Your board now tells you to start thinking of data from a strategic perspective, shifting from just cost optimisation to turning data into a revenue stream. While cost optimisation is important, you also need to start turning data into a revenue stream. How can you start doing that? You realise you’ve built something remarkable, and perhaps it’s time to take it to market.

What’s next? There are so many things on the horizon, how should you prepare for the future?

Dynamic Table (General Availability) is a new table type that drastically simplifies continuous data pipelines for transforming both batch and streaming data. With Dynamic Tables, Snowflake simplifies the creation and maintenance of data pipelines. Join us for a live, guided tour to explore how this innovation can enhance your workflows.

What You Will Explore and Learn
– How to Build Data Pipeline using Dynamic Tables
– Using Snowpark with Dynamic Tables and performing data validations with Dynamic Tables
– Monitoring Dynamic Tables

We have covered the basics of Snowpark, here is your chance to dive in! The Lab looks at Python Data Engineering over using Spark against a legacy file-based data lake. During the lab, you will get hands-on experience with these features and tools and understand how they benefit you as a data engineer!