Name: Why You Need to Use Streaming Data for Real Time Analytics
Start: 2020-04-28T16:00:00Z
End: 2020-04-28T16:00:54.000Z
Location: BrightTALK
Rating: 4.5

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Join Snowflake’s leading experts as they discuss the AI-accelerated future. They will share predictions about taking LLMs from inspiration to real value, adapting to an AI-powered workforce, securing AI in the enterprise, and more.

Regardless of where you are in your data strategy, we have a number of conversations to help you get value from your data today and achieve generative AI preparedness tomorrow.

This webinar covers upcoming trends across:

- Observability
- Autonomous agents
- Governance
- And more in the age of gen AI

Prepare yourself and your team to tackle your 2025 data initiatives.

AI + Data Predictions 2025: How Operationalizing AI Will Drive Technical Advances and Leadership Challenges

Processing geospatial data is often challenging because it comes from diverse sources, is stored in various formats, and uses different coordinate systems to understand spatial relationships between various features. Many companies struggle to activate their geospatial data into insights. Snowflake can support advanced geospatial processing with native SQL functions and Python UDFs.

Join us in this webinar to learn how you can

- Ingest and process geospatial data from GeoTiffs and Shapefiles
- Read geospatial data using Python UDFs
- Join different sources using nearest neighbor approaches
- Handle larger images efficiently with techniques like resampling and filtering
- Visualize geospatial data using Streamlit, QGIS, and H3 functions
 

By the end of this webinar, you will understand how to prepare data from satellite imagery and shapefiles for machine learning models and advanced spatial analysis within Snowflake.

Best Practices for Processing and Visualizing Geospatial Data in Snowflake

Tune in for a technical deep dive into the Snowflake AI Data Cloud. You’ll learn more about how this global network connects you to the most relevant content — from thousands of datasets, to pre-built services and models, to complete applications — all powered by a single platform.

During this event, you’ll learn how the unique architectural design of a single platform can connect your business globally at practically any scale.

The live product demo includes:
- A tour of the Snowflake user interface, Snowsight
- An introduction to virtual warehouses (compute) and scaling the elastic performance engine underpinning the platform
- Key concepts in data ingestion, transformation, and storage within Snowflake
- Ways to protect data with data governance features
- Discussion and demonstration of analysis within Snowsight and through BI tools and Snowflake Notebooks (public preview)
- An introduction to data processing in Python with Snowpark

Snowflake Data Cloud Demo

Looking to migrate your legacy data warehouse to Snowflake? Join this session to hear lessons learned from NAVEX, a risk and compliance management company, that decided to migrate 10,000 SQL Server databases to Snowflake.

You’ll hear best practices to successfully navigate a migration to Snowflake, and specifically learn about:

- Navex’s journey of migrating 10,000 SQL Server databases to Snowflake
- Why the Snowflake AI Data Cloud was the clear choice for a modern data platform
- How they streamlined their migration and reduced complexity while enabling more AI & ML workloads

Key Takeaways from Migrating 10K SQL Server Databases to Snowflake

Data preparation can be a headache with legacy architecture that requires manual environment configuration, job tuning, and cluster management. Snowpark, Snowflake’s developer platform, changes that by making it easier than ever for data engineers and data scientists to transform and process data. Snowpark natively supports SQL, Python, Java, and Scala on a single platform to enable data wrangling for pipelines feeding ML models, analytics, and applications faster and more securely with Snowflake’s elastic processing engine.

Watch this webinar with Snowpark experts on Python best practices to:

- Execute custom Python code with stored procedures and various UDF types for ELT/ETL
- Seamlessly access popular open source packages through the built-in Anaconda integration
- Use Snowpark-optimized warehouses for memory-intensive operations on large datasets
- Implement Sklearn-style transformers with Snowpark to take advantage of Snowflake’s parallelization to scale feature engineering

Best Practices for Python Data Transformations in Snowflake

Dynamic Table 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.

In this instructor-led session, you will learn how to:

- Build continuous and streaming data pipeline using Dynamic tables
- Manage and monitor Dynamic tables
- Create data validation automation and alerts
- Manage Snowpark Python user defined table function (UDTF) and use it in Dynamic tables

Virtual Hands-on Lab: Build Declarative Streaming Data Pipelines with Dynamic Tables

There are two major data engineering challenges with Python — the problem with siloed data and fragmented practices when it comes to building, testing, deploying, releasing and monitoring the Python data pipelines. Snowflake has introduced Snowpark — a set of libraries and code execution environments that run Python and other programming languages directly in Snowflake without the need to move your data. But what about the development practices?

In this session, we will walk through some of the latest announcements to arm you with capabilities to build data pipelines in Python.

Watch the session to learn:

- How you can improve your authoring experiences with Python (and SQL) with Snowflake
- What’s new with Snowpark
- How your can orchestrate and apply DevOps and CI/CD capabilities to your Python pipelines
- Latest capabilities to monitor, troubleshoot and debug your Python code and pipelines

End-to-End Python Data Pipelines with Snowflake

Planning to migrate to a more performant, cost-effective AI data platform?

In this webinar we’ll share best practices, tips, and tricks to approach a legacy Data Warehouse migration to the Snowflake AI Data Cloud. You’ll learn about the following, directly from experts:

- Migration methodology and acceleration tools
- SnowConvert, Snowflake’s native code conversion tooling, and see a comprehensive, live demo
- Mistakes to avoid when migrating, and how to make the process smooth and cost effective

Tips, Tricks, and Tools to Accelerate Migrations to Snowflake

One of the toughest challenges data professionals face today is streaming data for real-time analytics. A main barrier to real-time insights remains the complexity of the data itself, where companies do not have the tools and infrastructure to ingest and process structured and unstructured data. Organizations today need a data warehouse that is able to handle all data types and scale quickly to address growth, without the cost and burden of traditional data warehouses.

Join this webinar to learn how you can:
●Use a data warehouse to manage complexity when integrating different types of data from multiple sources
●Address data growth that surpasses storage and compute limitations
●Access real-time insights to offer better customer service, supply chain efficiencies, and operational improvements

Why You Need to Use Streaming Data for Real Time Analytics

Real Time Analytics

Data Analytics

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Why You Need to Use Streaming Data for Real Time Analytics

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