ThoughtSpot supports Amazon Redshift Serverless

As companies go all in on the cloud to dominate the decade of data, agility, flexibility, and ease of use are critical to success. That’s why we’re so excited to announce ThoughtSpot’s support for Amazon Redshift Serverless which allows customers to leverage the Modern Analytics Cloud to run and scale analytics on Amazon Redshift without having to provision and manage any data warehouse infrastructure.

Using Snowpark As Part Of Your Machine Learning Workflow

Teams working on data science initiatives are tasked with deriving new insights from massive amounts of data. To accomplish this, teams work with compute environments that require heavy operational overhead, which means most of their time is spent extracting and processing features for machine learning model training and inference. Pairing Snowflake’s near-unlimited access to data and elastic processing engine with the most popular programming languages can change that, so more time can be spent on model development.

Improving a day in the life of: Data Scientist - How ClearML is actually used.

ClearML in the real world, without the marketing fluff. Watch along as we show how ClearML integrates with this audio classification use case. Get lots of tips, tricks and inspiration on the use of the experiment manager and remote agents for use in your own day-to-day life as a data scientist. Chapters.

Leadership Tips: Keeping Data Teams Focused & Engaged | Data Legends Podcast

How do you keep your data team focused and build something that people will derive value from? In this episode, we cover team engagement, how to evaluate tech, and the influence of consumer analytics on innovation. Listen to our conversation with Raheem Daya, Sr. Software Development Manager, Envision Engineering at Amazon Web Services: How to set a clear vision and keep your data team engaged How to decide what tech to invest in and build How consumer tech is democratizing data

A Guide to Free Data Integration Tools

In the 21st-century business world, data reigns supreme. This means that how organizations to store, organize, move, and automate their valuable company data is crucial for overall success. When it comes to data management, having a great data integration solution in place is key. The process of data integration allows users to combine data from multiple sources, creating a data pipeline.

Why Drag and Drop Analytics are Important for Seamless BI Reporting

Drag and drop analytics are more interactive and user-friendly compared to traditional, high code business intelligence (BI) solutions. They allow users without programming experience to easily explore the data and don't require coding knowledge, with a drag and drop user interface to conveniently enhance functionality of any dashboard report. In this post, you will find the importance of drag and drop analytics for more seamless reporting and user experiences when analyzing business-critical data.

Using Snowflake and Dask for Large-Scale ML Workloads

Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud warehouse. For machine learning workloads, the most attractive of these options is the Snowflake Connector for Python.

AT&T Connects the World in the Data Cloud

To be one of the most connected companies in the world, AT&T chose the Snowflake Data Cloud for their digital transformation and data democratization. With Snowflake, AT&T has a single source of truth of their data across the organization and through Snowflake data collaboration, AT&T's business partners can seamlessly access AT&T's data to improve their customer experience.