Kiro - Core Features

What is Kiro

Kiro is an innovative AI-powered IDE that revolutionizes software development through intelligent assistance and structured workflows. Built on a familiar VS Code-like interface, it combines traditional development tools with advanced AI capabilities to support the entire software development lifecycle.

Agent Hooks

Agent Hooks in Kiro provide powerful automation capabilities that streamline development workflows by executing predefined actions in response to specific events in your IDE. Hooks automatically handle routine tasks like documentation updates, test generation, and code validation. Hooks help maintain consistency while allowing developers to focus on more complex development challenges.

Agent Steering

Agent Steering in Kiro provides a powerful way to guide AI behavior through persistent project knowledge stored in markdown files. By defining project standards, conventions, and architectural decisions, steering files ensure consistent code generation and recommendations across all interactions. 

Model context protocol (MCP)

Model Context Protocol (MCP) extends Kiro's capabilities by connecting to specialized servers that provide additional tools and context. MCP functions as a communication framework that enables Kiro to access specialized capabilities and data resources that exist outside of the LLM employed. A prime example is the AWS Documentation MCP server, which integrates seamlessly with Kiro to provide direct access to AWS documentation, including search functionality and personalized recommendations.

Snowflake - Notes

WPRKSPACES - In September 2025, Snowflake introduced Workspaces, which combines the functionality of Worksheets, Notebooks, File Manager, Query History, Results / Output, and the Database Explorer into a new, integrated development environment.

Workspaces will be the new default editor and replace Worksheets.

SHOW SCHEMAS IN ACCOUNT - All schemas from all databases are shown (based on current role).

SHOW TABLES IN ACCOUNT - All tables from all databases are shown (based on current role).

After table creation, Snowflake made some changes to the SQL behind the scenes are,

• Snowflake converted the TEXT data type to VARCHAR
• Snowflake added a comma and digit to represent the number of decimals in each NUMBER column.

VALIDATE IF THERE IS NO TYPO - e.g. sql as below

select count(*) as schemas_found, '3' as schemas_expected 

from GARDEN_PLANTS.INFORMATION_SCHEMA.SCHEMATA

where schema_name in ('A','AA','AAA'); 

Snowflake - Warehouse

In Snowflake, data is held in databases and any processing of data is done by something called a "warehouse."

Default three compute warehouses,

1. COMPUTE_WH owned by ACCOUNTADMIN.
2. SNOWFLAKE_LEARNING_WH owned by ACCOUNTADMIN
3. SYSTEM$STREAMLIT_NOTEBOOK_WH. That warehouse will be used by Snowflake to do any work required by streamlit apps and notebooks you create and run. You will not use this warehouse directly, only Snowflake will use it, on your behalf.

Scaling Up & Down : Changing the size of an existing warehouse is called scaling up or scaling down

Scaling In & Out : Warehouse is capable of scaling out in times of increased demand

Note : 

• Snowflake Warehouses do not hold data
• Opposite of scaling out is snapping back
• Cluster just means a "group" of servers.
• The number of servers in a warehouse is different, based on size (XS, S, M, etc)
• A cluster can hold multiple servers.

Snowflake - Authentication & Authorization

 Authentication  (Identity) : Proven through username & password

Authorization (Access) : Access through RBAC role assignments

Account Admin (See & Do Everything)

                Security Admin (Security administrator can manage security aspects of the account.)

                    User Admin (User administrator can create and manage users and roles)

                Sys Admin (Create DB, Warehouses, Schemas, Views)

           Public

Note: 

• Other than this, ORG ADMIN is the most powerful
• Discretionary Access Control (DAC)
• If you change your system role to another role, when you log out and log back in, your role will revert to the default

Snowflake - Databases

Every time you create a database, Snowflake will automatically create two schemas for you.

• The INFORMATION_SCHEMA schema holds a collection of views.  
• The INFORMATION_SCHEMA schema cannot be deleted (dropped), renamed, or moved.

• The PUBLIC schema is created empty and you can fill it with tables, views and other things over time.
• The PUBLIC schema can be dropped, renamed, or moved at any time. 

Note: 

• By default the database created with ACCOUNTADMIN role.
• ACCOUNTADMIN owns the SYSADMIN role, so it has ownership rights also, but indirectly.

Airflow - DAG Dependencies

• Define the order in which tasks should run
• Tasks can be upstream (run before) or downstream (run after)
• Declared after creating the tasks

Methods to declare

• Recommended

task1>>task2>>[task3,task4]

• Alternative

task1.set_downstream(task2)

task3.set_upstream(task2)

Snowflake - Cost Optimization

1. Reduce auto-suspend to 60 seconds
2. Reduce virtual warehouse size
3. Ensure minimum clusters are set to 1
4. Consolidate warehouses
• Separate warehouse by workload, requirement & not by domain
5. Reduce query frequency
• At many organizations, batch data transformation jobs often run hourly by default. But do downstream use cases need such low latency? Check with business before set up the frequency.
6. Only process new or updated data
7. Ensure tables are clustered correctly
8. Drop unused tables
9. Lower data retention
• The time travel (data retention) setting can result in added costs since it must maintain copies of all modifications and changes to a table made over the retention period.
10. Use transient tables
11. Avoid frequent DML operations
12. Ensure files are optimally sized
• To ensure cost effective data loading, a best practice is to keep your files around 100-250MB. 
• To demonstrate these effects, 
• If we only have one 1GB file, we will only saturate 1/16 threads on a Small warehouse used for loading. 
• If you instead split this file into ten files that are 100 MB each, you will utilize 10 threads out of 16. This level parallelization is much better as it leads to better utilisation of the given compute resources
13. Leverage access control
14. Enable query timeouts
15. Configure resource monitors