AVA Database Documentation
A high-performance analytical database system designed for advanced data analytics, machine learning, and statistical computing. Built for speed, scalability, and ease of use.
Key Features
Lightning Fast
Optimized C++ core with AVX-512 vectorization and multi-threading support for maximum performance.
Learn moreAdvanced Analytics
Built-in support for regression analysis, statistical functions, and machine learning operations.
Learn moreSmart Compression
Advanced LZMA-based compression reduces storage costs while maintaining query performance.
Learn moreMultiple APIs
Native bindings for Python, R, Java, and C# with comprehensive SQL support.
Learn moreDistributed Computing
Scale horizontally with distributed query execution and data partitioning.
Learn moreEnterprise Ready
Comprehensive licensing, security features, and enterprise support options.
Learn moreQuick Start Example
# Import AVA Python API
import avapy
# Load data from CSV
avapy.LoadTable("data.csv", "sales_data")
# Perform SQL query
result = avapy.asql("""
SELECT region,
AVG(sales) as avg_sales,
COUNT(*) as total_records
FROM sales_data
GROUP BY region
ORDER BY avg_sales DESC
""")
# Run linear regression
avapy.asql("""
CREATE REGRESSION MODEL sales_model
AS SELECT price, quantity, promotion
FROM sales_data
PREDICT revenue
""")
# Get predictions
predictions = avapy.asql("""
SELECT *, PREDICT(sales_model) as predicted_revenue
FROM new_sales_data
""")
# Load AVA R package
library(avaR)
# Connect to AVA database
con <- ava.connect()
# Load data
ava.load(con, "data.csv", "sales_data")
# Query data
result <- ava.query(con, "
SELECT region,
AVG(sales) as avg_sales,
COUNT(*) as total_records
FROM sales_data
GROUP BY region
ORDER BY avg_sales DESC
")
# Run regression
model <- ava.regression(con,
formula = revenue ~ price + quantity + promotion,
data = "sales_data"
)
# Make predictions
predictions <- predict(model, "new_sales_data")
-- Load data into AVA
.load "data.csv" as sales_data
-- Analyze sales by region
SELECT region,
AVG(sales) as avg_sales,
COUNT(*) as total_records,
SUM(revenue) as total_revenue
FROM sales_data
GROUP BY region
ORDER BY avg_sales DESC;
-- Create regression model
CREATE REGRESSION MODEL sales_model
AS SELECT price, quantity, promotion
FROM sales_data
PREDICT revenue;
-- Use model for predictions
SELECT *,
PREDICT(sales_model) as predicted_revenue,
revenue - PREDICT(sales_model) as residual
FROM new_sales_data;
Ready to get started?
Install AVA and start building high-performance data applications today.