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1. Snowflake Iceberg Tables Now Generally Available

Snowflake Iceberg Tables have moved to general availability, offering full storage interoperability with the Apache Iceberg open table format. This feature facilitates easier governance and collaboration on Iceberg data stored externally, enhancing the flexibility of data lakehouses, data lakes, and data meshes. Over 300 customers have already adopted Iceberg in its public preview, highlighting its potential to broaden Snowflake's data footprint.

2. Advancements in Snowflake Cortex AI

Snowflake introduced several enhancements to Cortex AI, including:

• • Cortex Analyst: Built with Meta's Llama 3 and Mistral Large models, this tool allows businesses to build applications securely on top of Snowflake's analytical data.
• • Cortex Search: Leveraging Neeva's retrieval and ranking technology, it facilitates the development of apps against documents and text-based datasets.
• • Cortex Guard: Aiming to ensure model safety, it filters and flags harmful content, including violence and hate speech.
• • Document AI: This feature, powered by Snowflake Arctic-TILT multimodal LLM, will soon allow users to extract data from documents such as invoices and contracts.

3. Introduction of Polaris Catalog

The Polaris Catalog is a vendor-neutral, open catalog implementation for Apache Iceberg, providing cross-engine interoperability and greater flexibility. It will become open-sourced within 90 days, supporting a variety of engines, including Apache Flink, Apache Spark, and Trino.

4. Private Preview of Snowflake Horizon Updates

Snowflake launched a private preview of an internal model marketplace within Snowflake Horizon. This marketplace enables users to publish and curate models, applications, and data products for internal use ensuring controlled access and preventing unintended external sharing. Other upcoming features include AI model sharing and AI-powered object descriptions.

5. AI & ML Improvements

• • Snowflake AI & ML Studio: A no-code interactive interface for AI development, now in private preview.
• • Cortex Fine-Tuning: In public preview, allowing customization of pre-trained models for specialized tasks.
• • ML Lineage: In private preview, offering traceability across ML life cycles.
• • Feature Store: Now in public preview, for creating, managing, and serving ML features.

6. Snowflake Native Apps with Snowpark Container Services — Preview

Snowflake introduced the integration of the Native App Framework with Snowpark Container Services on AWS. This integration provides developers with configurable GPU and CPU instances for various applications, from computer vision to geospatial data analysis. Over 160 Snowflake Native Apps are now available in the marketplace.

7. Developer Tool Enhancements

Several updates aimed at developers include:

• • Snowflake Notebooks: Snowflake Notebooks is now in preview, offering an interactive, cell-based programming environment for Python and SQL within Snowsight. It enables exploratory data analysis, machine learning model development, and other data science tasks all in one place.
• • Snowpark pandas API: Allows the use of pandas syntax for AI and pipeline development within Snowflake. The Snowpark pandas API is now in preview, allowing you to run pandas code directly on Snowflake data. This API offers a pandas-native experience with Snowflake's scalability and security, handling larger datasets without rewriting pandas pipelines.
• • Database Change Management: A public preview feature for DevOps, including Git integration.
• • Python API and CLI: Soon to be generally available, facilitating CI/CD practices.

8. Expanded Cloud Footprint and Governance

Snowflake announced a new data boundary for the EU, ensuring regional data residency and compliance. Additionally, a Department of Defense (DoD) environment meeting IL4 security controls will be available, highlighting Snowflake's commitment to robust data governance and security.

9. Snowflake Trail

The new Trail set of observability capabilities was unveiled, providing developers with tools to monitor, troubleshoot, and optimize workflows. Trail integrates with platforms like Grafana, Metaplane, and Slack, adhering to OpenTelemetry standards.

10. Snowflake Cortex Fine-Tuning — Preview

Cortex Fine-Tuning, now in preview, lets users adapt pre-trained models for specialized tasks. This managed service fine-tunes popular large language models using your data within Snowflake, enhancing model performance for specific use cases.

The preview of Snowpark Native Apps with Snowpark Container Services enables running containerized services within Snowflake Native Apps. This feature supports provider IP protection, security, data sharing, monetization, and integration with compute resources.

11. Snowpark Python Local Testing Framework — General Availability

The Snowpark Python local testing framework is now generally available. This emulator allows you to test Python code locally with Snowpark Python DataFrames, facilitating development and CI pipeline integration without needing a Snowflake account connection.

12. Universal Search and Snowsight Updates

Universal Search, now generally available, allows users to search for content across Snowflake storage, external Iceberg storage, and third-party providers. Snowsight also received a dark mode feature, enhancing user experience in low-light conditions.

13. New Geospatial Functions in Preview

Four new functions for GEOGRAPHY objects are now available in preview:

• • H3_TRY_COVERAGE: A special version of H3_COVERAGE that returns NULL if an error occurs when attempting to return an array of IDs (INTEGER values) identifying the minimal set of H3 cells that completely cover a shape.
• • H3_TRY_COVERAGE_STRINGS: Similar to H3_TRY_COVERAGE but returns hexadecimal IDs (VARCHAR values).
• • H3_TRY_POLYGON_TO_CELLS: Returns an array of INTEGER values of the IDs of H3 cells with centroids contained by a Polygon, returning NULL if an error occurs.
• • H3_TRY_POLYGON_TO_CELLS_STRINGS: Similar to H3_TRY_POLYGON_TO_CELLS but returns VARCHAR values.
With innovations in AI, data governance, and developer tools, Snowflake continues to drive forward the capabilities of its platform, ensuring customers can leverage data more effectively and securely. The future looks promising as Snowflake expands its offerings and strengthens its ecosystem, providing powerful solutions.

Many teams can build AI agents as experiments, but turning them into something that works reliably, safely, and at scale for real business use is much harder. To solve this, the Vertex AI Agent Builder from Google Cloud helps organizations build, run, monitor, and secure AI agents from start to finish. In short, Google is making it easier to go from "we built a demo agent" to "we run trusted AI agents in production."

What is Tableau Sum and Running Sum?

Sum

SUM is one of the commonly performed functions in Tableau. The Tableau Sum function seeks out the Sum of records under consideration. It is the total of the values present in a field. The screenshot provided below exhibits the total sum of sales for each of the three categories as given in Sheet 1.

In the sample dataset shown below, the sum of sales is shown corresponding to each of the corresponding values in the dimension "Category". For example, "Furniture" has a total sale of 754,748, which could be comprising of furniture related products such as Tables, Chairs etc.

Running Sum

A RUNNING SUM is a cumulative total in a row or column from the first value to the final value in the respective row or column. For instance, in the example given below, the cumulative values of Furniture and Office Supplies stand at 1,486,641 and that value when added to Technology’s value of 839,893 gives 2,326,534.

You can summarise or modify the granularity of your data using aggregate functions. An aggregate part combines the values of multiple lines to provide a single value. Examples of aggregate functions apart from sum are measurements based on Count, Count Distinct, Fixed Calculations, and other standard integration functions.

Every time you include a measure in your view, an aggregate is automatically applied to that measure. Depending on the context of the view, different aggregation techniques are used. Analysts can well utilise these features to simplify the whole complex process of data analysis, and organisations can harness them for insightful decisions.

What is Tableau Desktop:

Tableau Desktop is a desktop-based version of the software that is installed locally on your computer. It provides a user-friendly interface that allows users to create interactive visualizations, reports, and dashboards by dragging and dropping data onto the canvas. Tableau Desktop supports a wide range of data sources, including spreadsheets, databases, and cloud-based services. It also provides advanced features such as calculated fields, table calculations, and data blending. Users can save their work locally or publish it to Tableau Server or Tableau Online.

For the purpose of learning and exploring dashboards developed by various users online, Tableau Public can also be utilized. This free-to-use feature allows users to create interactive visualizations and publish them on the web. It is designed to help users of all skill levels, from beginners to advanced analysts, to explore, create, and share data visualizations online.

Key features of Tableau Desktop

• • Powerful and flexible: Offers a full range of features for data analysis and visualization, allowing for deep customization.
• • Fast performance: Enables quick data exploration and visualization creation due to local processing power.
• • Offline functionality: Work on your visualizations even without an internet connection.
• • Customization options: Provides control over server configuration and some customization options.

What is Tableau Cloud (formerly Tableau Online):

Tableau Online is a cloud-based version of the software that allows users to access and share their Tableau visualizations, reports, and dashboards from anywhere with an internet connection. It provides the benefit of being able to collaborate with other users in real time. Users can connect to various data sources and perform advanced data analysis using features like calculated fields and table calculations. Tableau Cloud also provides a range of sharing options, including embedding dashboards in websites and sharing them via email.

Tableau Pulse is available on Tableau Cloud, empowering every employee with intelligent, personalized, and contextual insights delivered in the flow of work. Tableau Pulse leverages generative AI to present insights in both natural language and visual formats, simplifying the process of identifying critical metrics, gaining actionable insights, posing questions, and connecting data to real-world business scenarios. Integrated with the AI capabilities of the Einstein Trust Layer, a secure AI architecture, Pulse ensures that teams can harness the power of generative AI while safeguarding their customer data.

Key features of Tableau Cloud

• • Cloud-based convenience: Accessible from anywhere with an internet connection, facilitating remote work and collaboration.
• • Scalability and ease of use: Highly scalable to fit various team sizes and offers a user-friendly interface for sharing insights.
• • Reduced IT burden: Tableau handles maintenance and upgrades, freeing up your IT resources.
• • Always up-to-date: Ensures access to the latest features and functionality without manual updates.

Tableau Desktop, Tableau Server, and Tableau Cloud: A Comparison

Let’s dig deeper into the capabilities of Tableau Desktop, Server, and Cloud, exploring how each solution can empower organizations to make use of the full potential of their data and drive actionable insights:

1. Tableau Desktop

What does Tableau Desktop Offer You:

• • Fast performance.
• • Offers a wide range of features for data analysis and visualization, including data blending, calculations, and advanced chart types.
• • Enables complete customization of dashboards and views to meet specific needs.
• • Provides a familiar desktop application experience for many users.

Benefits of Tableau Desktop:

• • Leverages local processing power for quick data manipulation and visualization creation.
• • Ideal for working with large datasets or complex calculations.
• • Enables smooth interaction with dashboards and visualizations.

Tableau Desktop Use Cases:

• • Individual data analysts and creators exploring complex datasets.
• • Building custom dashboards and reports for specific departments or projects.
• • Creating prototypes and mockups for data visualizations.

2. Tableau Server

What Does Tableau Server Offer You:

• • Secure on-premises deployment.
• • Collaboration and sharing.
• • Scalability for on-premises.
• • Customization options.

Benefits of Tableau Server:

• • Data remains on your own infrastructure, ideal for organizations with strict data security and compliance requirements.
• • Enables real-time collaboration on visualizations with colleagues.
• • Can be scaled to accommodate growing teams and increasing data volumes.
• • Offers some control over server configuration and customization of user roles and permissions.

Tableau Server Use Cases:

• • Financial institutions and healthcare organizations with sensitive data.
• • Sales teams collaborating on sales performance dashboards.
• • Large enterprises with a high volume of data users.
• • IT administrators managing user access and security settings.

3. Tableau Cloud (formerly Tableau Online)

What Does Tableau Cloud Offer You:

• • Cloud-based convenience.
• • Scalability and ease of use.
• • Reduced IT burden.
• • Always up-to-date.

Benefits of Tableau Cloud:

• • Accessible from any device with an internet connection, facilitating remote work and collaboration.
• • Highly scalable to fit various team sizes, from small teams to large enterprises.
• • Tableau handles server maintenance, upgrades, and security patches.
• • Provides access to the latest Tableau features and functionalities like Tableau Pulse and AI capabilities for next-gen experiences

Tableau Cloud Use Cases:

• • Remote teams working on data analysis projects together.
• • Small and medium-sized businesses looking for a quick and easy BI solution.
• • IT departments with limited resources to manage on-premises BI infrastructure.
• • Organizations that need to stay current with the latest data visualization trends.

Tableau Pricing

Tableau provides flexible pricing options tailored to different organizational needs. Pricing is based on factors such as headcounts, licensing type, and specialized roles. Users can opt for individual licenses suited to their specific requirements.

For per-user licenses, Tableau offers three distinct options, allowing users to select the most suitable plan for their needs. Additionally, Tableau provides flexibility in licensing models, including role-based, usage-based, or core-based options for embedded analytics.

Tableau offers three pricing editions ranging from $15 to $70, catering to various budgetary constraints. Users can explore the features and capabilities of each edition through a free trial before deciding. Evaluate the different pricing editions to determine which aligns best with your budget and requirements.

Tableau Creator

Includes: Tableau Desktop, Tableau Prep Builder, Tableau Pulse, and one Creator license on Tableau Cloud Pricing: $70.00 (1 User Per Month)

Tableau Viewer

Includes: Tableau Pulse and one Viewer license of Tableau Cloud Pricing: $15.00 (1 User Per Month) Learn More: https://www.tableau.com/pricing/teams-orgs

Book a Tableau Free Trial

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Disclaimer: The pricing information provided in this blog for Tableau products and services is subject to change at any time without notice. The pricing may vary based on factors such as promotions, changes in pricing policies, or updates from Tableau. We recommend visiting the official Tableau website or contacting Tableau sales representatives for the most current pricing information. This blog is not responsible for any discrepancies or changes in pricing.