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Version: 5.x-preview

VeloDB Cloud Quick Start

This guide walks you through getting started with VeloDB Cloud in minutes. You'll create a warehouse, load a 6-million-row SSB-Flat benchmark dataset, and run analytical queries.

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No credit card required. VeloDB Cloud offers a 14-day free trial for SaaS warehouses.

1. Create Your Account​

Go to velodb.cloud and sign up using your preferred method:

  • Google - Single sign-on with your Google account
  • Microsoft - Single sign-on with your Microsoft account
  • Email - Traditional email and password registration

Sign up for VeloDB Cloud

2. Choose Your Deployment Mode​

After signing in, you'll see two deployment options:

ModeDescriptionFree Trial
SaaSFully managed warehouse in VeloDB's cloud14 days
BYOCDeploy in your own AWS/Azure account30 days free service

For this quickstart, click Start 14-day Free Trial under SaaS.

Choose deployment mode

3. Create Your Warehouse​

Configure your warehouse with these settings:

SettingRecommended Value
Warehouse Namevelodb-quickstart
Cloud PlatformAWS
RegionSelect the region closest to you
ZoneAny available zone
Admin PasswordYour secure password
warning

Important: Remember your admin password. You'll need it to connect to the warehouse.

The initial cluster comes with:

  • 4 vCPU, 32 GB RAM compute
  • 200 GB cache storage

Click Create to provision your warehouse.

Create warehouse configuration Warehouse initialization settings

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VeloDB provisions your warehouse in 3-5 minutes. Once complete, the status changes to Running.

Warehouse provisioning

4. Connect to Your Warehouse​

Navigate to Connections in the sidebar to find your connection details:

Connection information

Connection Methods​

Use the built-in Query editor in the VeloDB Console sidebar for browser-based SQL access. No setup required.

Verify Connection​

SHOW DATABASES;

Expected output:

+--------------------+
| Database           |
+--------------------+
| information_schema |
+--------------------+

5. Load the SSB-Flat Benchmark Dataset​

We've prepared a public SSB-Flat SF1 dataset (6 million rows) for you to explore. This denormalized Star Schema Benchmark dataset is ideal for analytical queries.

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No credentials needed! The sample data buckets are publicly accessible from VeloDB.

Preview Data from S3​

Before loading, preview the data directly from S3. Select the tab matching your warehouse region for best performance:

SELECT
    lo_orderkey,
    lo_orderdate,
    c_name,
    c_nation,
    lo_revenue,
    p_brand
FROM S3(
    "uri" = "s3://velodb-import-data-us-east-1/ssb-flat-sf1/ssb_flat_001.parquet",
    "format" = "parquet",
    "s3.endpoint" = "s3.us-east-1.amazonaws.com",
    "s3.region" = "us-east-1"
)
LIMIT 5;

Create Database and Table​

-- Create database
CREATE DATABASE IF NOT EXISTS ssb;
USE ssb;

-- Create the SSB flat table
CREATE TABLE IF NOT EXISTS ssb_flat (
    lo_orderkey        BIGINT NOT NULL,
    lo_linenumber      BIGINT NOT NULL,
    lo_custkey         BIGINT NOT NULL,
    lo_partkey         BIGINT NOT NULL,
    lo_suppkey         BIGINT NOT NULL,
    lo_orderdate       DATE NOT NULL,
    lo_commitdate      DATE NOT NULL,
    lo_orderpriority   VARCHAR(15) NOT NULL,
    lo_shippriority    BIGINT NOT NULL,
    lo_shipmode        VARCHAR(10) NOT NULL,
    lo_year            INT NOT NULL,
    lo_month           INT NOT NULL,
    lo_weeknum         INT NOT NULL,
    d_datekey          BIGINT NOT NULL,
    d_dayofweek        VARCHAR(10) NOT NULL,
    d_month            VARCHAR(10) NOT NULL,
    d_yearmonth        VARCHAR(10) NOT NULL,
    lo_quantity        BIGINT NOT NULL,
    lo_extendedprice   DOUBLE NOT NULL,
    lo_discount        DOUBLE NOT NULL,
    lo_revenue         DOUBLE NOT NULL,
    lo_supplycost      DOUBLE NOT NULL,
    lo_tax             DOUBLE NOT NULL,
    c_custkey          BIGINT NOT NULL,
    c_name             VARCHAR(25) NOT NULL,
    c_nation           VARCHAR(15) NOT NULL,
    c_region           VARCHAR(12) NOT NULL,
    c_city             VARCHAR(10) NOT NULL,
    c_mktsegment       VARCHAR(10) NOT NULL,
    s_suppkey          BIGINT NOT NULL,
    s_name             VARCHAR(25) NOT NULL,
    s_nation           VARCHAR(15) NOT NULL,
    s_region           VARCHAR(12) NOT NULL,
    s_city             VARCHAR(10) NOT NULL,
    p_partkey          BIGINT NOT NULL,
    p_name             VARCHAR(22) NOT NULL,
    p_brand            VARCHAR(9) NOT NULL,
    p_category         VARCHAR(7) NOT NULL,
    p_mfgr             VARCHAR(6) NOT NULL,
    p_color            VARCHAR(11) NOT NULL,
    p_type             VARCHAR(25) NOT NULL,
    p_size             BIGINT NOT NULL,
    p_container        VARCHAR(10) NOT NULL,
    INDEX idx_p_type (p_type) USING INVERTED PROPERTIES("parser" = "english")
)
DUPLICATE KEY(lo_orderkey)
DISTRIBUTED BY HASH(lo_orderkey) BUCKETS 48;

Load Data​

tip

For best performance, use the S3 bucket in the same region as your warehouse.

INSERT INTO ssb.ssb_flat
SELECT * FROM S3(
    "uri" = "s3://velodb-import-data-us-east-1/ssb-flat-sf1/*.parquet",
    "format" = "parquet",
    "s3.endpoint" = "s3.us-east-1.amazonaws.com",
    "s3.region" = "us-east-1"
);
info

Loading 6 million rows takes approximately 30 seconds to 1 minute.

Verify Load​

SELECT COUNT(*) AS total_rows FROM ssb.ssb_flat;

Expected output:

+------------+
| total_rows |
+------------+
|    6000000 |
+------------+

6. Run Analytical Queries​

Now let's run some analytical queries on your dataset.

Query 1: Real-Time Aggregation​

Aggregate revenue and discount metrics by customer region:

SELECT
    c_region,
    COUNT(*) AS order_count,
    SUM(lo_revenue) AS total_revenue,
    AVG(lo_discount) AS avg_discount
FROM ssb.ssb_flat
GROUP BY c_region
ORDER BY total_revenue DESC;

Query 2: Window Functions - YoY Growth​

Calculate year-over-year revenue growth using window functions:

SELECT
    lo_year,
    SUM(lo_revenue) AS yearly_revenue,
    LAG(SUM(lo_revenue)) OVER (ORDER BY lo_year) AS prev_year_revenue,
    ROUND(
        (SUM(lo_revenue) - LAG(SUM(lo_revenue)) OVER (ORDER BY lo_year))
        / LAG(SUM(lo_revenue)) OVER (ORDER BY lo_year) * 100,
        2
    ) AS yoy_growth_pct
FROM ssb.ssb_flat
GROUP BY lo_year
ORDER BY lo_year;

Query 3: Complex BI with CTE​

Use Common Table Expressions to analyze regional profit tiers:

WITH regional_stats AS (
    SELECT
        c_region,
        s_region,
        SUM(lo_revenue) AS revenue,
        SUM(lo_supplycost) AS cost,
        SUM(lo_revenue - lo_supplycost) AS profit
    FROM ssb.ssb_flat
    GROUP BY c_region, s_region
)
SELECT
    c_region,
    s_region,
    revenue,
    profit,
    CASE
        WHEN profit > 1000000000 THEN 'High'
        WHEN profit > 500000000 THEN 'Medium'
        ELSE 'Low'
    END AS profit_tier
FROM regional_stats
ORDER BY profit DESC;

Query 4: Self-Join Period Comparison​

Compare revenue between 1997 and 1993 using a self-join:

SELECT
    t1.c_nation,
    t1.revenue_1997,
    t2.revenue_1993,
    ROUND((t1.revenue_1997 - t2.revenue_1993) / t2.revenue_1993 * 100, 2) AS growth_pct
FROM (
    SELECT c_nation, SUM(lo_revenue) AS revenue_1997
    FROM ssb.ssb_flat
    WHERE lo_year = 1997
    GROUP BY c_nation
) t1
JOIN (
    SELECT c_nation, SUM(lo_revenue) AS revenue_1993
    FROM ssb.ssb_flat
    WHERE lo_year = 1993
    GROUP BY c_nation
) t2 ON t1.c_nation = t2.c_nation
ORDER BY growth_pct DESC;

Query 5: Full-Text Search​

Search for products containing "STEEL" in the product type using inverted index:

SELECT
    p_type,
    p_brand,
    COUNT(*) AS product_count,
    SUM(lo_revenue) AS total_revenue
FROM ssb.ssb_flat
WHERE p_type MATCH 'STEEL'
GROUP BY p_type, p_brand
ORDER BY total_revenue DESC
LIMIT 10;

Next Steps​