Sharing dynamic tables with other Snowflake accounts unlocks collaboration and data exchange possibilities. However, there are some key considerations to keep in mind:

Sharing Mechanism:

• Direct Sharing: You can directly share specific dynamic tables and underlying objects (like the schema) with another Snowflake account within the same region. This grants them read access to the materialized results of the dynamic table.
• Listings (Preview): This option allows you (the provider) to create a curated list of data assets, including dynamic tables, and offer them to other accounts. Recipients can subscribe to the listing and gain access to the shared data objects.

Data Security:

• Access Control: Even with sharing enabled, you can define granular access control for the recipient account. This determines what level of access they have (e.g., read-only) to the shared dynamic table.
• Data Lineage: Sharing dynamic tables doesn't automatically share the underlying source data. Ensure the recipient account has access to the source data itself or the results might be incomplete.

Dynamic Table Considerations:

• Refresh Schedules: The refresh schedule of the dynamic table remains under your control (the provider). However, consider how changes in the schedule might impact the recipient's access to up-to-date data.
• Dependencies: If your dynamic table relies on other tables, ensure the recipient has access to all dependencies within your account or theirs for successful data access.
• Target Lag: Be mindful of the target lag (desired refresh frequency) for the dynamic table. The recipient might experience delays if the lag time is high.

Additional Considerations:

• Data Governance: Establish clear data governance policies around shared dynamic tables, including usage guidelines and data ownership definitions.
• Monitoring and Auditing: Monitor how the shared dynamic table is being used by the recipient account. Utilize Snowflake's auditing features to track access patterns and identify any potential security concerns.

By carefully considering these factors, you can leverage the power of Snowflake's dynamic table sharing to enable secure and efficient data collaboration between accounts.

Sharing dynamic tables with other Snowflake accounts unlocks collaboration and data exchange possibilities. However, there are some key considerations to keep in mind:

Sharing Mechanism:

• Direct Sharing: You can directly share specific dynamic tables and underlying objects (like the schema) with another Snowflake account within the same region. This grants them read access to the materialized results of the dynamic table.
• Listings (Preview): This option allows you (the provider) to create a curated list of data assets, including dynamic tables, and offer them to other accounts. Recipients can subscribe to the listing and gain access to the shared data objects.

Data Security:

• Access Control: Even with sharing enabled, you can define granular access control for the recipient account. This determines what level of access they have (e.g., read-only) to the shared dynamic table.
• Data Lineage: Sharing dynamic tables doesn't automatically share the underlying source data. Ensure the recipient account has access to the source data itself or the results might be incomplete.

Dynamic Table Considerations:

• Refresh Schedules: The refresh schedule of the dynamic table remains under your control (the provider). However, consider how changes in the schedule might impact the recipient's access to up-to-date data.
• Dependencies: If your dynamic table relies on other tables, ensure the recipient has access to all dependencies within your account or theirs for successful data access.
• Target Lag: Be mindful of the target lag (desired refresh frequency) for the dynamic table. The recipient might experience delays if the lag time is high.

Additional Considerations:

• Data Governance: Establish clear data governance policies around shared dynamic tables, including usage guidelines and data ownership definitions.
• Monitoring and Auditing: Monitor how the shared dynamic table is being used by the recipient account. Utilize Snowflake's auditing features to track access patterns and identify any potential security concerns.

By carefully considering these factors, you can leverage the power of Snowflake's dynamic table sharing to enable secure and efficient data collaboration between accounts.

Troubleshooting errors in Snowflake's dynamic table refreshes involves a systematic approach to identify the root cause and implement a resolution. Here's a breakdown of the process:

1. Identify the Error:

• Review Notifications: If you have alerts set up, you'll likely receive notifications about failing refreshes.
• Snowsight UI: Check the "Refresh History" tab of the affected dynamic table in Snowsight for details like error messages and timestamps.

2. Investigate the Root Cause:

• Error Messages: The error message itself often provides valuable clues about the nature of the problem. Look for keywords related to invalid data, syntax errors, or resource limitations.
• Information Schema Functions: Utilize functions like DYNAMIC_TABLE_REFRESH_HISTORY to get a detailed history of the refresh attempts, including error messages for past failures.

3. Debug and Resolve:

• SQL Logic: If the error points towards an issue within the SQL statement of the dynamic table, you can use standard SQL debugging techniques to identify and fix syntax errors or logical mistakes within the transformation logic.
• Insufficient Permissions: Ensure the user or role refreshing the table has proper permissions to access all underlying source data and tables involved in the dependency chain.
• Resource Constraints: If the error suggests resource limitations (e.g., timeouts, memory issues), consider optimizing the SQL query or adjusting the refresh schedule to reduce load during peak usage times.
• Schema Changes: Be aware of potential schema changes in upstream tables that might impact the dependent dynamic table. Update the dependent table's SQL statement to adapt to the new schema, if necessary.

4. Rollback and Retry (Optional):

• In case the refresh error corrupts data, leverage Snowflake's time travel functionality to revert the table to a previous successful state.
• Once you've addressed the root cause, retry the refresh manually or wait for the next scheduled refresh to occur.

5. Advanced Debugging Techniques:

• Snowflake Support: For complex issues, consider contacting Snowflake support for assistance. They can provide deeper insights into system logs and offer additional troubleshooting guidance.
• Explain Plans: Utilize Snowflake's EXPLAIN PLAN statement to analyze the query plan for the dynamic table's SQL statement. This can help identify potential inefficiencies or bottlenecks within the transformation logic.

Remember:

• Document the error, troubleshooting steps taken, and the resolution implemented for future reference.
• Regularly monitor your dynamic tables to proactively identify and address potential issues before they significantly impact your data pipelines.

By following these steps and best practices, you can effectively troubleshoot and resolve errors encountered during dynamic table refreshes in Snowflake, ensuring the smooth functioning and data quality of your data pipelines.

Here are some best practices for managing dependencies between dynamic tables in Snowflake:

1. Define Clear Dependencies:

• Explicitly define the data lineage within your pipeline. This means clearly outlining which dynamic tables depend on the output of others.
• Leverage clear naming conventions for tables and columns to enhance readability and understanding of dependencies.

2. Utilize Target Lag Effectively:

• Set realistic target lag times for each dynamic table based on the data update frequency and your data freshness requirements.
• Stagger refresh schedules strategically, ensuring upstream tables refresh before dependent tables. This avoids situations where dependent tables try to process data that isn't ready yet.

3. Monitor Lag Times and Refresh History:

• Proactively monitor the actual lag times of your dynamic tables compared to the target lag. This helps identify potential delays and bottlenecks in the pipeline.
• Use Snowflake's Information Schema functions and monitoring tools to analyze refresh history and identify any recurring issues.

4. Break Down Complex Pipelines:

• For intricate data pipelines, consider breaking them down into smaller, more manageable stages represented by individual dynamic tables. This improves modularity and simplifies dependency management.
• Avoid creating overly complex chains of dependent tables, as it can make troubleshooting and debugging more challenging.

5. Utilize Materialized Views (Optional):

• In some scenarios, materialized views can be strategically placed within your pipeline to act as intermediate caching layers. This can help optimize performance by reducing the frequency dependent tables need to refresh based on the same source data.

6. Implement Error Handling and Rollback Mechanisms:

• Design your pipeline to handle potential errors during refresh attempts. This might involve retry logic or rollback mechanisms to prevent cascading failures across dependent tables.
• Consider using Snowflake's time travel functionality to revert a dynamic table to a previous successful state if a refresh introduces errors.

7. Document Your Pipeline:

• Document your data pipeline clearly, including the dependencies between dynamic tables, refresh schedules, and any custom error handling logic. This documentation becomes crucial for future maintenance and troubleshooting.

By following these best practices, you can effectively manage dependencies between dynamic tables, ensuring your Snowflake data pipelines run smoothly, deliver high-quality data, and are easier to maintain over time.

Snowflake offers multiple tools and techniques to monitor the refresh history and identify potential issues with dynamic tables. Here are some key methods:

1. Snowsight UI:

• Refresh History Tab: For a quick overview, navigate to the specific dynamic table in Snowsight. The "Refresh History" tab displays information like:
  • Last successful refresh time.
  • Target lag time (desired refresh frequency).
  • Longest actual lag time (identifies potential delays).

2. Information Schema Functions:

Snowflake provides powerful Information Schema functions to delve deeper into dynamic table refresh history and dependencies. Here are two important ones:

• DYNAMIC_TABLE_REFRESH_HISTORY: This function delivers detailed historical data about a dynamic table's refreshes. You can query it to identify:

  • Timestamps of past refresh attempts.
  • Success or failure status of each refresh.
  • Any error messages associated with failed refreshes.

• DYNAMIC_TABLE_GRAPH_HISTORY: This function provides a broader perspective by showcasing the entire data pipeline dependency graph. It reveals:

  • Scheduling state (RUNNING/SUSPENDED) of all dynamic tables involved.
  • Historical changes in table properties over time.
  • Potential bottlenecks or issues within the chain of dependent tables.

3. Alerts and Notifications:

Snowflake allows you to set up alerts to be notified automatically when issues arise. You can configure alerts to trigger based on conditions like:

• Failed Refresh Attempts: Receive notifications if a dynamic table refresh fails consecutively for a certain number of times.
• Excessive Lag Time: Get alerted if the actual lag time significantly exceeds the target lag time, indicating potential delays in data updates.

4. Custom Monitoring Dashboards:

For comprehensive monitoring, you can leverage Snowflake's integration with BI tools to create custom dashboards. These dashboards can visualize various metrics like refresh history, success rates, and lag times, allowing you to proactively identify and troubleshoot issues within your dynamic table pipelines.

By combining these techniques, you can gain valuable insights into the health and performance of your dynamic tables in Snowflake. Regular monitoring helps ensure your data pipelines are functioning smoothly and delivering up-to-date, reliable data for your analytics needs.

Snowflake's dynamic tables can exist in various states that reflect their current status and operational condition. Here's a breakdown of the key states:

Scheduling State (SCHEDULING_STATE):

• RUNNING: The dynamic table is currently scheduled to refresh at regular intervals.
• SUSPENDED: The refresh schedule is temporarily paused. This can happen manually or automatically due to errors.

Refresh State (DYNAMIC_TABLE_STATE_HISTORY):

• INITIALIZING: The dynamic table is being created for the first time.
• ACTIVE: The table is successfully created and operational. Within this state, there are sub-states:
  • SUCCEEDED: The most recent refresh completed successfully.
  • SKIPPED: A scheduled refresh was skipped due to reasons like upstream table not being refreshed or load reduction for performance reasons.
  • IMPACTED: The dynamic table itself might be functional, but upstream dependencies might be experiencing issues, potentially impacting data accuracy.
• FAILED: The most recent refresh attempt encountered an error. The table might still contain data from the previous successful refresh.

Additional States:

• CANCELLED: A currently running refresh was manually stopped.

How to View Dynamic Table States:

You can utilize Snowflake's system functions to get insights into the current and historical states of your dynamic tables. Here are two commonly used functions:

• DYNAMIC_TABLE_STATE_HISTORY: This function provides detailed information about the refresh history of a dynamic table, including timestamps and states like SUCCEEDED, FAILED, or SKIPPED.
• DYNAMIC_TABLE_GRAPH_HISTORY: This function offers a broader view of your entire data pipeline, showcasing the scheduling state (RUNNING or SUSPENDED) of all dynamic tables and their dependencies.

By understanding these states and leveraging the available functions, you can effectively monitor the health and performance of your dynamic table pipelines in Snowflake.

Chaining dynamic tables is a powerful feature in Snowflake that allows you to build intricate data pipelines by connecting multiple transformations. Here's how it works:

• Sequential Processing: You can define a dynamic table that queries the results of another dynamic table. This enables you to perform a series of transformations in a defined order.

Imagine a scenario where you have raw sales data in a staging table. You can:

1. Create a dynamic table (Table 1) to clean and filter the raw data.
2. Create another dynamic table (Table 2) that queries the results of Table 1 and performs further transformations like aggregations or calculations.

By chaining these tables, you create a multi-step pipeline where the output of one table becomes the input for the next.

• Benefits of Chaining:

  • Modular Design: Break down complex transformations into smaller, manageable steps represented by individual dynamic tables.
  • Improved Maintainability: Easier to understand and troubleshoot issues when the logic is segmented into clear stages.
  • Reusability: Reuse intermediate results from chained tables in other parts of your data pipeline.

Here's an analogy: Think of each dynamic table as a processing unit in an assembly line. You can chain these units together to perform a series of tasks on the data, ultimately leading to the desired transformed output.

• Example: Chained Dynamic Tables

Imagine you want to analyze website traffic data. You can create a chain of dynamic tables:

1. Table 1: Filters raw website logs based on specific criteria (e.g., valid requests).
2. Table 2: Groups the filtered data by page and calculates metrics like page views and unique visitors.
3. Table 3: Joins Table 2 with user data from another table to enrich the analysis with user information.

This chained pipeline transforms raw logs into insightful website traffic analysis data.

Overall, chaining dynamic tables empowers you to build complex and scalable data pipelines with a clear, modular structure.

Dynamic tables shine in several data transformation pipeline scenarios where automation, maintainability, and efficiency are key. Here are some prime use cases:

• Simplified Transformations: If your data transformations involve standard SQL operations like joins, aggregations, and filtering, dynamic tables offer a clear and concise way to define the logic. No need for complex scripting.
• Automated Updates: For data that changes frequently, dynamic tables with automatic refresh schedules ensure your transformed data is always up-to-date without manual intervention.
• Reduced Development Time: By using a declarative approach with SQL, dynamic tables can significantly speed up development compared to writing and maintaining custom transformation scripts.
• Improved Maintainability: The logic for transforming data is encapsulated within the SQL statement, making it easier to understand, document, and maintain compared to scattered scripts.
• Incremental Updates: For large datasets, dynamic tables can optimize performance by refreshing only the changed data since the last update, reducing processing time and costs.

Here are some specific examples:

• Sales Data Analysis: Transform raw sales data into reports with metrics like total sales, average order value, and customer segmentation.
• Financial Reporting: Aggregate financial data from various sources for automated generation of reports and dashboards.
• Log Data Processing: Filter and transform log data to identify trends, analyze user behavior, or detect anomalies.

However, dynamic tables might not be the best choice for all scenarios:

• Complex Transformations: If your data transformations require custom logic beyond standard SQL capabilities, traditional programming languages might be more suitable.
• Fine-grained Control: If you need precise control over individual data points within the transformed table, dynamic tables (being read-only) might not be ideal.

Overall, dynamic tables are a powerful tool for simplifying and automating data transformation pipelines, particularly for scenarios that benefit from a declarative approach and require frequent updates.

A dynamic table definition in Snowflake consists of two main components:

1. SQL Statement: This is the heart of a dynamic table, defining the logic for transforming and filtering the source data. It's written in standard SQL and can include joins, aggregations, filtering clauses, and other transformations. This query essentially defines the structure and content of the resulting dynamic table.

2. Refresh Schedule: This determines how often Snowflake refreshes the dynamic table to reflect changes in the underlying data sources. You can specify various options for scheduling, including:

   • Continuous: The table updates continuously as changes occur in the source data (useful for real-time scenarios).
   • Scheduled: You define a specific schedule for automatic refreshes (e.g., hourly, daily).
   • Manual: You trigger the refresh manually whenever needed.

Optional Components:

• Retention Period: You can define a time frame for how long Snowflake keeps historical versions of the dynamic table data.
• Clustering Key: This helps Snowflake optimize query performance by clustering data based on frequently used columns.

Here's an example of a dynamic table definition with all the components:

CREATE OR REPLACE DYNAMIC TABLE sales_analysis
REFRESH => ON DEMAND  -- Manual refresh
AS
SELECT
  customer_id,
  SUM(order_amount) AS total_sales,
  AVG(discount_rate) AS average_discount
FROM orders
GROUP BY customer_id;

In this example:

• The SQL statement calculates total sales and average discount per customer.
• The refresh schedule is set to manual, meaning you'll need to trigger the refresh yourself.

Overall, a dynamic table definition provides a concise and declarative way to define how you want your transformed data to look, along with how often it should be updated.

The key difference between dynamic tables and traditional tables with manual Data Manipulation Language (DML) lies in how the data is managed and updated:

Traditional Tables with DML:

• Data Manipulation: You directly manipulate the data within the table using DML statements like INSERT, UPDATE, and DELETE. This gives you full control over individual data points.
• Manual Updates: You need to write code or scripts to transform and update the data. This can be complex and time-consuming for intricate transformations.
• Separate Workflows: Data transformation is a separate process from data storage. You might need to schedule scripts or jobs to keep the transformed data updated.

Dynamic Tables:

• Immutable Data: The content of a dynamic table is based on a pre-defined SQL query. You cannot directly modify the data within the table itself.
• Automatic Updates: Dynamic tables refresh automatically based on a schedule or when the underlying data changes. Snowflake handles the transformation logic defined in the query.
• Declarative Approach: You define the desired transformed data through a SQL statement. Snowflake takes care of the entire transformation pipeline.

In essence:

• Traditional tables offer granular control over data but require manual effort for transformations.
• Dynamic tables simplify workflows by automating transformations and updates based on a defined query.

Snowflake's dynamic tables are a game-changer for data engineering workflows by offering a declarative approach to data pipelines. Here's the breakdown:

• What are they?

  Dynamic tables are a special type of table that act like materialized views of a defined SQL query. Instead of creating a separate table and writing code for transformations, you define the target table as dynamic and specify the transformation logic within the SQL statement.

• How do they simplify things?

  • Declarative approach: You define the desired outcome (transformed data) using a SQL query, and Snowflake handles the pipeline execution and refreshes. No more scripting complex transformations.
  • Automatic updates: Dynamic tables automatically refresh based on changes in the underlying data sources. This ensures your data is always up-to-date.
  • Incremental refreshes: For optimized performance, dynamic tables can update only the new or changed data since the last refresh, instead of reprocessing everything.

In essence, dynamic tables streamline data engineering by:

• Reduced coding: Less code means less time spent writing and debugging complex transformation scripts.
• Simplified pipeline management: Snowflake takes care of scheduling and orchestration, freeing you from manual intervention.
• Improved efficiency: Automatic refreshes and incremental updates ensure your data pipelines run smoothly and efficiently.

Overall, dynamic tables empower data engineers to focus on data strategy and analysis, rather than getting bogged down in the complexities of data pipeline development and maintenance.

I wrote an in depth article to cover this in more detail here: https://snowflakesolutions.net/snowflake-solutions-center-by-dataops-live/
but the simple answer is that the Snowflake Solutions Center also known as the SSC within Snowflake for Sales Engineers and Field CTOs, is a catalog of demos and proof of concept solutions which is powered by our partner Dataops.live.

The Snowflake Solutions Center allows Snowflake itself and its Field CTOs, Solution Architects, and Sales Engineers to be MUCH MUCH More agile in their Customer and Prospect solutions deployment.

Our view is this is game changer for truly AUTOMATING Data Solutions Concepts. Key aspects of the Snowflake Solution Center are:
* Capability for Snowflake Sales Engineers and Solution Architects to Search and Find Industry and Vertical Solutions.
* Deploy Solutions easily within seconds or minutes.
* Allow GOVERNANCE of the solutions to make sure they are validated with CORRECT Code and Data.
* Detailed Analytics of usage and deployment of solutions.
* Full integration of environments - Streamlit, Docker, React, etc.
* Guaranteed quality and deployment of the solution.
* Fully Dataops ease of deployment, environment is baked into the dataops - No dependency hell.

I wrote an in depth article to cover this in more detail here: https://snowflakesolutions.net/snowflake-solutions-center-by-dataops-live/
but the simple answer is that the Snowflake Solutions Center also known as the SSC within Snowflake for Sales Engineers and Field CTOs, is a catalog of demos and proof of concept solutions which is powered by our partner Dataops.live.

The Snowflake Solutions Center allows Snowflake itself and its Field CTOs, Solution Architects, and Sales Engineers to be MUCH MUCH More agile in their Customer and Prospect solutions deployment.

Our view is this is game changer for truly AUTOMATING Data Solutions Concepts. Key aspects of the Snowflake Solution Center are:
* Capability for Snowflake Sales Engineers and Solution Architects to Search and Find Industry and Vertical Solutions.
* Deploy Solutions easily within seconds or minutes.
* Allow GOVERNANCE of the solutions to make sure they are validated with CORRECT Code and Data.
* Detailed Analytics of usage and deployment of solutions.
* Full integration of environments - Streamlit, Docker, React, etc.
* Guaranteed quality and deployment of the solution.
* Fully Dataops ease of deployment, environment is baked into the dataops - No dependency hell.

I wrote an in depth article to cover this in more detail here
but the simple answer is that the Snowflake Solutions Center also known as the SSC within Snowflake for Sales Engineers and Field CTOs, is a catalog of demos and proof of concept solutions which is powered by our partner Dataops.live.

The Snowflake Solutions Center allows Snowflake itself and its Field CTOs, Solution Architects, and Sales Engineers to be MUCH MUCH More agile in their Customer and Prospect solutions deployment.

Our view is this is game changer for truly AUTOMATING Data Solutions Concepts. Key aspects of the Snowflake Solution Center are:
* Capability for Snowflake Sales Engineers and Solution Architects to Search and Find Industry and Vertical Solutions.
* Deploy Solutions easily within seconds or minutes.
* Allow GOVERNANCE of the solutions to make sure they are validated with CORRECT Code and Data.
* Detailed Analytics of usage and deployment of solutions.
* Full integration of environments - Streamlit, Docker, React, etc.
* Guaranteed quality and deployment of the solution.
* Fully Dataops ease of deployment, environment is baked into the dataops - No dependency hell.

The issues most likely to affect businesses in the next one to five years center on the adoption of large language models
and generative AI, and the eternal priority of security and risk management.

- The shift towards DevOps/DevSecOps emphasizes moving testing and remediation left in the software development lifecycle, reducing human error in production through automation.
- Automation in the production environment minimizes opportunities for human error that cybercriminals exploit as entry points.
- Cyber attackers are targeting developer environments for potential human mistakes, posing a challenge for security teams to defend against, given the inherent chaos and experimentation in development.
- Despite the difficulty in creating baselines for acceptable development activity, Mario Duarte expresses confidence that, with time, security teams will effectively counter these shift-left attacks using a combination of human efforts, machine learning, and AI.

- Ramaswamy highlights advancements in self-driving cars, emphasizing their transformative impact on transportation.
- The ongoing revolution in battery design is noted for its potential to enhance the range of electric vehicles, impact stationary power storage, and influence the electrical grid's role in addressing climate change.
- Biotech breakthroughs, exemplified by the rapid development of the COVID-19 vaccine, are discussed. Ramaswamy mentions the project to map human RNA, seen as crucial for solving diseases and detecting and curing cancers.
- Snowflake looks forward to a future where data governance and analysis contribute to successful business outcomes and positive societal change.
- The overall sentiment is optimistic about the exciting developments in various fields, indicating a bright future.

1. AI will be a huge boon to cybercriminals
before it becomes a help to security teams.

- Cybercriminals will gain an advantage with advanced AI tools before their targets can implement AI defenses.
- Legitimate businesses face hurdles in adopting new technologies due to costs, regulatory requirements, and reputational risks, giving cybercriminals an initial advantage.
- Mario Duarte anticipates a leveling of the playing field over time, but expects challenges and vulnerabilities during the transition.

2. Generative AI will make lowbrow
cyberattacks smarter:

- The deployment of advanced AI by cybercriminals raises concerns about potential sci-fi-level malevolence and sophisticated attacks.
- Mario Duarte emphasizes that, initially, cybercriminals are likely to leverage basic, effective attacks, with phishing remaining a significant threat.
- Generative AI is expected to enhance the success of phishing attacks, potentially catching people off guard.

3. Cyberattackers will continue to shift left.

- The shift towards DevOps/DevSecOps emphasizes moving testing and remediation left in the software development lifecycle, reducing human error in production through automation.
- Automation in the production environment minimizes opportunities for human error that cybercriminals exploit as entry points.
- Cyber attackers are targeting developer environments for potential human mistakes, posing a challenge for security teams to defend against, given the inherent chaos and experimentation in development.
- Despite the difficulty in creating baselines for acceptable development activity, Mario Duarte expresses confidence that, with time, security teams will effectively counter these shift-left attacks using a combination of human efforts, machine learning, and AI.

- The rapid advancements and broad capabilities of generative AI and Large Language Models (LLMs) pose challenges for security teams.
- Chief Information Security Officers (CISOs) must guide the responsible adoption of these powerful tools to prevent immediate concerns, like proprietary data exposure, and mitigate long-term risks.
- Companies, including Apple, Amazon, and JPMorgan, have restricted certain AI applications due to potential data risks, emphasizing the need for responsible alternatives to prevent frustrated staff from resorting to workarounds and shadow IT.
- CISOs play a crucial role in striking a balance between making innovation accessible and limiting the risks associated with compromising sensitive data, regulatory issues, and reputational damage.

1. LLMs will be secured in-house.

- Christian Kleinerman and James Malone discussed the AI supply chain for businesses to construct secure large and not-quite-large language models.
- Security challenges arise when maintaining generative AI tools and Large Language Models (LLMs) within security perimeters, questioning trust in external data sources and open source models.
- Mario Duarte, Snowflake’s VP of Security, highlights concerns about potential misconfigurations, user errors, and the lack of experience in maintaining and securing LLM-based tools.
- The threat of bad data deliberately introduced by adversaries is another security concern, with inaccurate outputs from data tools posing a form of social engineering and falling within the realm of cybersecurity.

2. The AI data supply chain will be a target
of attack. Eventually.

- Examining the vulnerability of data, it's crucial to realistically assess the risk of adversaries injecting false or biased data into foundational Large Language Models (LLMs).
- The potential threat involves a scenario where adversaries engage in a long game, conducting a propaganda operation to manipulate content in LLMs, creating misinformation about nation-state conflicts, election integrity, or political candidates.

3. Gen AI will improve intruder detection.

- Addressing a key issue in IT security, the time lapse between a security breach and its detection is a significant concern, with median dwell time reported at around two weeks.
- Anoosh Saboori, Head of Product Security, anticipates significant improvements in automated detection of intruder activities through the application of AI in various security aspects.
- AI's role includes enhancing the user experience with security products, accelerating anomaly detection, automating responses, and conducting forensic analysis. Generative AI is expected to excel in recognizing and flagging malicious or inconsistent behavior based on behavioral data, such as deviations from an employee's baseline activities.

- The current focus in generative AI revolves around understanding how to effectively adopt and harness this powerful technology.
- Experts are exploring additional concerns that executives and consumers should consider beyond just embracing generative AI.

1. CEOs need to think about regulation.

- Business leaders are currently focused on leveraging generative AI and maintaining a balance between speed and attention to detail.
- As regulations like the EU's AI Act become more prevalent, leaders are increasingly concerned with ensuring compliance to avoid regulatory fines and reputational damage in their current and future AI applications.
- Jennifer Belissent, a Principal Data Strategist at Snowflake, emphasizes the importance of addressing regulatory concerns in the rapidly evolving landscape of AI adoption.

2. And data governance.

- The volume of data is increasing, attracting more parties and tools for data handling, posing a growing challenge in data governance.
- Fine-tuning Large Language Models (LLMs) with proprietary data raises concerns about the emergence of sensitive information, amplified by AI's inherent lack of control and visibility.
- Christian Kleinerman, SVP of Product, emphasizes the heightened importance of data lineage and provenance governance in addressing these challenges.

3. Consumers will (and should) demand transparency.

- The average consumer may not currently consider the impact of AI on their lives, often being indifferent to how businesses use their data.
- With Large Language Model (LLM)-powered AI increasingly influencing decisions like loans, job interviews, and medical procedures, there's a growing demand for transparency into how data models affect individuals.
- Jennifer Belissent underscores the shift in consumer expectations as AI plays a more significant role in decision-making processes affecting their lives.

Data scientists, data engineers and BI
analysts are in for a fun ride.

1. Data engineering will evolve—and be highly valued—in an AI world.

- Data engineers' concerns about job displacement by AI are unfounded; instead, their skills will be highly valued.
- The expertise of data engineers is crucial for organizing data and ensuring its proper intake, a prerequisite for leveraging the power of Large Language Models (LLMs).
- Data engineers will play a vital role in connecting with LLMs through data pipelines, automating value extraction. Their expertise will evolve to solve unique challenges and oversee work now handled routinely by generative AI.

2. Data scientists will have more fun.

- Data scientists face evolving challenges with the advent of generative AI, shifting from mundane tasks like sentiment analysis to addressing new issues like contextual data input and minimizing hallucination in Large Language Models (LLMs).
- Generative AI is expected to make data science jobs more appealing by automating repetitive tasks, leading to increased interest among students.
- To adapt, data science leaders must adjust their skill sets, transitioning from traditional roles to selecting and integrating external vendors of AI models. The role of data scientists as accurate intermediaries between raw data and consumers remains crucial.

3. BI analysts will have to uplevel.

- Analysts currently create reports and answer executive queries. In the future, executives will prefer self-service interaction with summarized data, freeing analysts for more profound work.
- Snowflake CIO Sunny Bedi sees this shift as inevitable, urging analysts to enhance skills. The choice between dashboards and natural language querying highlights a trend of upleveling roles.
- BI professionals, adapting to self-service trends, move beyond dashboards to address complex issues, contributing to their professional development.

4. Developers expect to be 30% more efficient using generative AI assistants.

- Bedi's dev team estimated 30% of their code could be handled by a gen AI tool, potentially a game changer in efficiency.
- Beyond initial efficiency gains, AI-generated code offers reusability, enhancing overall project efficiency.
- Testing and quality assurance could be assisted by AI agents, leading to faster, higher-quality deployments, though coding skills remain essential.
- In the near term, AI tools focus on quickly executing tasks, but predicting AI output supervision needs beyond five years is uncertain due to rapid advancements.

Streamlit founders Amanda Kelly and Adrien Treuille
are bullish on the future of open source software, both
in terms of how it will affect generative AI and LLM
projects, and how those AI technologies will affect the
broader open source movement.

1. The open source ecosystem around generative
AI will parallel and rival the corporate ecosystem.

- Meta's open-sourcing of LLaMA and LLaMA 2 in the last six months has led to remarkable innovation in the open source community.
- The availability of these models to academics and the open source community has resulted in the rapid repurposing of Large Language Models (LLMs) for various applications.
- Anticipation of ongoing significant developments in LLMs and large generative models driven by the open source community.
- Expectation of a combination of existing models becoming open source and the emergence of new technologies like Low Rank Adaptation (LoRA).
- LoRA enables academics to fine-tune existing models more efficiently and with reduced memory consumption.
- Surprising genuine levels of innovation occurring outside corporate structures in the realm of 70 billion parameter models.
- The collaborative nature of open source projects, contributes to better results through transparency, diverse perspectives, and passionate contributions. The openness fosters more conversations and leads to better decision-making processes.

2. Generative AI will help the larger open
source movement, beyond just AI, accelerate
and democratize.

- The open source community will gain from generative AI not only in AI-specific projects but across various efforts due to the efficient elimination of tedious human tasks.
- According to Treuille, a significant cost in developing open source is the human-intensive work related to documentation, bug handling, communication, and responding to requests.
- Large Language Models (LLMs) are expected to accelerate open source development by assisting with these human-intensive tasks, ultimately making smaller teams more efficient and powerful.