Built-in Sample Datasets

clickzetta_sample_data is a public dataset built into Singdata Lakehouse via the data sharing mechanism. It is available out of the box for every tenant — no application or import required, and it does not count against your storage quota.

Query it directly in SQL using three-part naming:

SELECT * FROM clickzetta_sample_data.tpch_100g.orders LIMIT 10;

It contains 5 schemas covering performance benchmarking, business analytics practice, and AI vector retrieval:

Schema	Data Content	Scale
tpch_100g	TPC-H standard benchmark, supply-chain order data	100 GB, 600 million rows in lineitem
tpcds_10tb	TPC-DS standard benchmark, retail multi-channel sales data	10 TB, 28.8 billion rows in store_sales
ecommerce_events_history	E-commerce user behavior event stream	110 million rows in history table, 370 million rows in live table
nyc_taxi_tripdata	New York City ride-hailing trip records	1.49 billion rows
clickzetta_doc_kb	Singdata product documentation vector knowledge base	Contains 1024-dimensional embeddings

tpch_100g

Data source: TPC-H is the industry-standard database benchmark, simulating a supply-chain scenario with 8 tables covering orders, parts, suppliers, customers, and more.

Scale: 100 GB, 8 tables total.

Table	Row Count	Description
lineitem	600 million	Order line items, the largest table
orders	150 million	Order master table
customer	15 million	Customer information
supplier	1 million	Supplier information
part	2 million	Part information
partsupp	8 million	Part-supplier relationships
nation	25	Country dimension
region	5	Region dimension

Use cases:

• Test SQL query performance and compare response times across different VCluster sizes
• Learn multi-table JOIN, aggregation, window function, and other SQL syntax
• Validate index effectiveness (Bloomfilter, inverted index)

Example query: Calculate annual revenue by region (TPC-H Q5)

SELECT n.n_name, SUM(l.l_extendedprice * (1 - l.l_discount)) AS revenue FROM clickzetta_sample_data.tpch_100g.customer c JOIN clickzetta_sample_data.tpch_100g.orders o ON c.c_custkey = o.o_custkey JOIN clickzetta_sample_data.tpch_100g.lineitem l ON o.o_orderkey = l.l_orderkey JOIN clickzetta_sample_data.tpch_100g.supplier s ON l.l_suppkey = s.s_suppkey JOIN clickzetta_sample_data.tpch_100g.nation n ON c.c_nationkey = n.n_nationkey JOIN clickzetta_sample_data.tpch_100g.region r ON n.n_regionkey = r.r_regionkey WHERE r.r_name = 'ASIA' AND o.o_orderdate >= '1994-01-01' AND o.o_orderdate < '1995-01-01' GROUP BY n.n_name ORDER BY revenue DESC;

tpcds_10tb

Data source: TPC-DS is a more complex retail benchmark than TPC-H, simulating multi-channel (store, catalog, web) sales scenarios with 24 tables and 99 standard queries.

Scale: 10 TB, 24 tables total.

Table	Row Count	Description
store_sales	28.8 billion	Store sales line items
catalog_sales	14.4 billion	Catalog sales line items
web_sales	7.2 billion	Web sales line items
store_returns	2.88 billion	Store return records
inventory	1.31 billion	Inventory records
customer	65 million	Customer information
item	402,000	Product information
date_dim	73,000	Date dimension
Other 16 tables	—	Dimension tables (store, promotion, address, etc.)

Use cases:

• Large-scale stress testing to validate query performance at the 10 TB level
• Test execution plans for complex multi-table JOINs and subqueries
• Compare different VCluster sizes under heavy queries

Example query: Calculate quarterly sales by store

SELECT s.s_store_name, d.d_year, d.d_qoy, SUM(ss.ss_net_paid) AS total_sales FROM clickzetta_sample_data.tpcds_10tb.store_sales ss JOIN clickzetta_sample_data.tpcds_10tb.store s ON ss.ss_store_sk = s.s_store_sk JOIN clickzetta_sample_data.tpcds_10tb.date_dim d ON ss.ss_sold_date_sk = d.d_date_sk WHERE d.d_year = 2001 GROUP BY s.s_store_name, d.d_year, d.d_qoy ORDER BY d.d_qoy, total_sales DESC LIMIT 20;

ecommerce_events_history

Data source: From a Kaggle public dataset, recording user behavior events on a multi-category e-commerce platform, including browsing, adding to cart, and purchasing.

Scale: 2 tables.

Table	Row Count	Description
ecommerce_events_multicategorystore	110 million	Historical event snapshot table
ecommerce_events_multicategorystore_live	370 million	Continuously updated live event table with change_tracking enabled; supports creating a Table Stream

Field descriptions:

Field	Type	Description
event_time	varchar	Event occurrence time
event_timestamp	timestamp_ltz	Event timestamp
event_type	varchar	Event type: view / cart / purchase
product_id	varchar	Product ID
category_id	varchar	Category ID
category_code	varchar	Category path, e.g. electronics.smartphone
brand	varchar	Brand
price	decimal(10,2)	Product price
user_id	varchar	User ID
user_session	varchar	Session ID
event_date	date	Event date (partition key)

Use cases:

• Funnel analysis (view → add to cart → purchase conversion rate)
• User retention and repeat purchase analysis
• Category and brand sales ranking
• Incremental data processing practice based on Table Stream

Example query: Calculate the conversion funnel by event type

SELECT event_type, COUNT(DISTINCT user_id) AS users, COUNT(*) AS events FROM clickzetta_sample_data.ecommerce_events_history.ecommerce_events_multicategorystore GROUP BY event_type ORDER BY events DESC;

nyc_taxi_tripdata

Data source: Publicly released trip data from the New York City Taxi and Limousine Commission (TLC), covering ride-hailing trips from platforms such as Uber and Lyft (FHVHV: For-Hire Vehicle High Volume).

Scale: 1 table, 1.49 billion rows.

Key fields:

Field	Type	Description
hvfhs_license_num	varchar	Platform license number (HV0003=Uber, HV0005=Lyft)
pickup_datetime	timestamp_ltz	Pickup time
dropoff_datetime	timestamp_ltz	Dropoff time
trip_miles	double	Trip distance (miles)
trip_time	bigint	Trip duration (seconds)
base_passenger_fare	double	Base passenger fare
tips	double	Tips
driver_pay	double	Driver earnings
shared_request_flag	varchar	Shared ride request (Y/N)
wav_request_flag	varchar	Wheelchair-accessible vehicle request (Y/N)

Use cases:

• Time-series aggregation analysis (trip volume by hour, by day of week)
• Large-table aggregation performance testing
• Platform comparison analysis (Uber vs Lyft)
• Geospatial data analysis (combined with pickup/dropoff zone fields)

Example query: Average trip distance and fare by platform

SELECT hvfhs_license_num, COUNT(*) AS trips, ROUND(AVG(trip_miles), 2) AS avg_miles, ROUND(AVG(trip_time) / 60, 1) AS avg_minutes, ROUND(AVG(base_passenger_fare), 2) AS avg_fare FROM clickzetta_sample_data.nyc_taxi_tripdata.fhvhv_tripdata GROUP BY hvfhs_license_num ORDER BY trips DESC;

clickzetta_doc_kb

Data content: A vector knowledge base of Singdata Lakehouse product documentation. Document content is converted into 1024-dimensional vectors using Alibaba Cloud DashScope's text embedding model, for use in semantic retrieval and AI question answering.

Scale: 1 table, dashscope_clickzetta_elements.

Key fields:

Field	Type	Description
id	string	Unique record ID
type	string	Element type (Title / NarrativeText / Table, etc.)
filename	string	Source document filename
text	string	Raw text content
embeddings	vector(float, 1024)	1024-dimensional vector representation of the text
element_type	string	Document element classification
documents_source	string	Document source identifier
date_processed	timestamp_ltz	Vector processing timestamp

Use cases:

• Experience vector similarity retrieval (the cosine_distance function)
• Build a RAG (Retrieval-Augmented Generation) Q&A system based on product documentation
• Learn how to use the AI_EMBEDDING function together with vector indexes

Example query: Retrieve document fragments most relevant to "dynamic table" using vector similarity

SELECT filename, type, text, cosine_distance(embeddings, AI_EMBEDDING('ai_gateway_conn:text-embedding-v4', 'What is a dynamic table')) AS distance FROM clickzetta_sample_data.clickzetta_doc_kb.dashscope_clickzetta_elements ORDER BY distance ASC LIMIT 5;

Related Documentation

• Get Started with TPC-H Queries Using Sample Data
• TPC-H Performance Benchmark
• Table Stream
• Vector Index
• AI_EMBEDDING Function