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

Kafka - Topics, Partitions & Offset

KAFKA - EVENT PROCESSING SYSTEM

• No need to wait for response
• Fire and Forget
• Real time processing (Streams)
• High throughput & Low latency

 

Topics 

    - Particular stream of data

    - Can be identified by name

        e.g. Tables in a database

    - Support all type of messages

    - The sequence of message is called, data stream

    - You cannot query topics, instead use kafka producers to send data and kafka consumers to read the data

    - Kafka topics are immutable, Once data is written to a partition, it cannot be changed

    - Data is kept for a limited time (default is one week - configurable)

Partitions

    - Topics are split into partitions

    - Messages within each partitions are ordered

Offset

    - Each message within a partition gets an incremental id, called offset

Producers

    - Write data to topics

    - Producers know to which partition to write

Kafka Connect

    -Getting data in and out of kafka

Step-by-Step to Start Kafka

• Step 1: Start ZooKeeper
• This will keep running in the terminal. In a new terminal window
• Step 2: Start Kafka Server (Broker)
• Step 3: Create a Kafka Topic
• Step 4: Start Producer
• Type messages here to send to Kafka.
• Step 5: Start Consumer (in a new terminal)
• You will see the messages you type in the producer appear here.

Architecture

Data Sharing

 

1. Create Share

CREATE SHARE my_share;

---

2. Grant privileges to share

GRANT USAGE ON DATABASE my_db TO SHARE my_share;
GRANT USAGE ON SCHEMA my_schema.my_db TO SHARE my_share;
GRANT SELECT ON TABLE my_table.myschema.my_db TO SHARE my_share;

---

3. Add consumer account(s)

ALTER SHARE my_share ADD ACCOUNT a123bc;

---

4. Import share

CREATE DATABASE my_db FROM SHARE my_share;

Materialized View & Warehouse

Materialized View

To know the usage history,

    SELECT * FROM information_schema.materialized_view_refresh_history();

    SELECT * FROM information_schema.materialized_view_refresh_history(materialized_view_name             =>  'mname'));

    SELECT * FROM snowflake.account_usage.materialized_view_refresh_history;

Warehouse

Resizing: Warehouses can be resized even when query is running or when suspended.

It impact only future queries, not the running one.

Scale Up vs Scale Out: 

    Scale Up (Resize) - More complex queries

    Scale Out - More User (More queries)

Micro - Partitions & Clustering

Micro - Partitions

• Immutable - Can't be changed
• New data - Added in new partitions

Clustering

Get the clustering key details from the existing tables,

    SELECT * FROM information_schema.tables WHERE clustering_key IS NOT NULL;

To know about the cluster in detail,

    SELECT SYSTEM$CLUSTERING_INFORMATION ('table_name');

To know about the particular column cluster in detail,

    SELECT SYSTEM$CLUSTERING_INFORMATION ('table_name','(column_name)');

To know about the clustering depth,

    SELECT SYSTEM$CLUSTERING_DEPTH ('table_name');

CACHING in Snowflake

Result Cache

• Stores the results of a query (Cloud Services)
• Same queries can use that cache in the future
• Table data has not changed
• Micro-partitions have not changed
• Query doesn't include UDFs or external functions
• Sufficient privileges & results are still available
• Very fast result (persisted query result)
• Avoids re-execution
• Can be disabled by using
• USE_CACHED_RESULT parameter
• If query is not re-used purged after 24 hours
• If query is re-used can be stored up to 31 days

Tip : Result cache is resides in the CLOUD SERVICES layer

Data Cache

• Local SSD cache
• Cannot be shared with other warehouses
• Improve performance of subsequent queries that use the same data
• Purged if warehouse is suspended or resized
• Queries with similar data ⇒ same warehouse
• Size depends on warehouse size

Tip : Data cache is resides in the QUERY PROCESSING layer

Metadata Cache

• Stores statistics and metadata about objects
• Properties for query optimization and processing
• Range of values in micro-partition
• Count rows, count distinct values, max/min value
• Without using virtual warehouse
• DESCRIBE + system-defined functions
• Called as "Metadata store"
• Virtual Private Edition: Dedicated metadata store

Tip : Result cache is resides in the CLOUD SERVICES layer

Query History

In 3 ways we will ab able to view the query history,

1. Using SNOWSIGHT( Web UI)

2. Using INFORMATION_SCHEMA

    SELECT * FROM TABLE (information_schema.query_history()) ORDER BY start_time;

3. Using ACCOUNT_USAGE

    SELECT * FROM snowflake.account_usage.query_history;

UNLOADING

Syntax:

COPY INTO @stage_name FROM (SELECT col1, col2, col3 FROM table_name)

FILE_FORMAT = (TYPE = CSV)

HEADER = TRUE

Additional Parameters:

SINGLE

Use the SINGLE parameter to specify whether the file will be split into multiple files. The default is set to FALSE which means the data will be split across multiple files

MAX_FILE_SIZE

To define the file size

COPY - Parameters

CopyOption	Description	Values
ON_ERROR	Specifies the error handling for the load operation	CONTINUE | SKIP_FILE | SKIP_FILE_num | 'SKIP_FILE_num%' | ABORT_STATEMENT
SIZE_LIMIT	Specifies the maximum size (in bytes) of data to be loaded	<num>
PURGE	Remove files after successful load	TRUE | FALSE
RETURN_FAILED_ONLY	Return only files that have failed to load	TRUE | FALSE
MATCH_BY_COLUMN_NAME	Load semi-structured data into columns in matching the columns names	CASE_SENSITIVE | CASE_INSENSITIVE | NONE
ENFORCE_LENGTH	Truncate text strings that exceed the target column length	TRUE | FALSE
TRUNCATECOLUMNS	Truncate text strings that exceed the target column length	TRUE | FALSE
FORCE	Load files even if loaded before	TRUE | FALSE
LOAD_UNCERTAIN_FILES	Load files even if load status unknown	TRUE | FALSE

INSERT OVERWRITE

• Specifies that the target table should be truncated before inserting the values into the table.
• To use the OVERWRITE option on INSERT, you must use a role that has DELETE privilege on the table because OVERWRITE will delete the existing records in the table.

E.g.

INSERT OVERWRITE INTO table_name

  SELECT * FROM src_table_name

  WHERE city = 'abcd';

[COPY] File Format Parameters

Property	Property Type
TYPE	String
RECORD_DELIMITER	String
FIELD_DELIMITER	String
FILE_EXTENSION	String
SKIP_HEADER	Integer
DATE_FORMAT	String
TIME_FORMAT	String
TIMESTAMP_FORMAT	String
BINARY_FORMAT	String
ESCAPE	String
ESCAPE_UNENCLOSED_FIELD	String
TRIM_SPACE	Boolean
FIELD_OPTIONALLY_ENCLOSED_BY	String
NULL_IF	List
COMPRESSION	String
ERROR_ON_COLUMN_COUNT_MISMATCH	Boolean
VALIDATE_UTF8	Boolean
SKIP_BLANK_LINES	Boolean
REPLACE_INVALID_CHARACTERS	Boolean
EMPTY_FIELD_AS_NULL	Boolean
SKIP_BYTE_ORDER_MARK	Boolean
ENCODING	String

Combining Streams & Tasks

CREATE TASK my_task 

WAREHOUSE = my_wh

SCHEDULE = '15 MINUTE'

WHEN SYSTEM$STREAM_HAS_DATA('my_stream_name')

AS 

INSERT INTO my_tgt_table (time_col) VALUES (CURRENT_TIMESTAMP);

Data Sampling Methods

"Data sampling" refers to selecting a subset of data from a larger dataset, typically for testing, analysis, or performance purposes.

• ROW or BERNOULLI
• Every ROW is chosen with percentage p
• More "Randomness"
• Smaller tables
• e.g. SELECT * FROM table_name SAMPLE ROW (<p>) SEED(15); 
• BLOCK or SYSTEM
• Every BLOCK is chosen with percentage p
• More "Effectiveness"
• Larger tables
• e.g. SELECT * FROM table_name SAMPLE SYSTEM(<p>) SEED(15);

    Here, <p> Returns approximately p% of the table rows randomly.

Snowflake - Important one word questions and answers

Maximum length of a VARIANT data type ::: 16 MB uncompressed

-#-#-#-

What is unstructured data ::: Does not fit into any pre-defined data models,

• video files
• audio files
• documents

-#-#-#-

Snowflake share URL ::: below are the supported,

• Scoped URL 
• Temporary URL, expires in 24 hours
• e.g. SELECT BUILED_SCOPED_FILE_URL(@stage_name,'logo.png');
• File URL 
• Permanent one
• e.g. SELECT BUILD_STAGE_FILE_URL(@stage_name,'logo.png');
• Pre Signed URL 
• HTTPS URL used to access file via a web browser
• e.g. SELECT GET_PRESIGNED_URL(@stage_name,'logo.png',60);
• here, 60 denotes the seconds to expiry

-#-#-#-

Directory table ::: Stored metadata about staged files

By default it is not enabled, enabling syntax as below

e.g. CREATE OR REPLACE STAGE my_internal_stage

  FILE_FORMAT = my_json_format

  DIRECTORY = ( ENABLE = TRUE );

e.g. To query the directory table

    SELECT * FROM DIRECTORY(@my_internal_stage);

Note: At first the data will not be visible, you need to manually refresh and see the data,

ALTER STAGE my_internal_stage REFRESH;

-#-#-#-

Streams ::: Record (DML) changes made to a table

3 columns will be newly added,

1. metadata$action
2. metadata$update
3. metadata$row_id

STALE ::: 

Stream becomes stale(no longer available) when offset is outside the data retention period of the source table.

The column STALE_AFTER indicating when the stream is predicted to become stale.

TIME TRAVEL ::: Undrop fails if an object with the same name already exists.

FAIL SAFE :::

• Protection of historical data in case of disaster
• No user interaction & recoverable only by snowflake
• Non configurable 7 day period
• Period starts immediately after Time Travel period ends
• Contributes to storage cost

TIME TRAVEL & FAIL SAFE - STORAGE COST

Use below queries to get the information,

SELECT * FROM snowflake.account_usage.storage_usage;

SELECT * FROM snowflake.account_usage.table_storage_metrics;