To create an enterprise application for scanning Oracle databases and generating a profile of columns, sizing, and metadata, the baseline codebase should focus on database connectivity, metadata extraction, and report generation. Here’s a step-by-step guide and recommendations for your baseline:
1. Baseline Tech Stack
Language and Framework
• Python (widely used for database interaction and metadata profiling).
• Framework: Flask/FastAPI for a lightweight web service, or Django for a more full-fledged enterprise app.
Database Connectivity
• cx_Oracle: Oracle’s Python library for connecting to Oracle databases.
• SQLAlchemy: ORM that supports metadata inspection, which can be combined with cx_Oracle for enhanced functionality.
Frontend (Optional for Web UI)
• React.js, Angular, or Vue.js for an interactive web interface.
• Material UI or Bootstrap for enterprise-grade UI components.
Libraries for Profiling
• Pandas: Data profiling and analytics.
• Dataprep or ydata-profiling: For generating column-wise statistics and profiling.
Deployment
• Containerize using Docker.
• Use Kubernetes or AWS ECS for deployment in the cloud.
• CI/CD: GitHub Actions or Jenkins.
2. Features to Build
• Connection Module: Allow users to securely connect to Oracle databases.
• Schema Discovery: Fetch all schemas, tables, and columns.
• Metadata Extraction: Gather column data types, lengths, constraints, and nullability.
• Profiling and Sizing:
• Column distribution.
• Data types and average sizes.
• Null values percentage.
• Export Reports: CSV, Excel, or JSON formats.
• Interactive UI (Optional): Filters, sort options, and visualization.
3. Sample Baseline Code
Database Connection and Metadata Extraction (Python)
import cx_Oracle
def connect_to_oracle(user, password, dsn):
try:
connection = cx_Oracle.connect(user, password, dsn)
print("Connection successful!")
return connection
except cx_Oracle.DatabaseError as e:
print(f"Error: {e}")
return None
def fetch_table_metadata(connection, schema_name):
query = f"""
SELECT table_name, column_name, data_type, data_length, nullable
FROM all_tab_columns
WHERE owner = :schema_name
"""
cursor = connection.cursor()
cursor.execute(query, {'schema_name': schema_name.upper()})
results = cursor.fetchall()
metadata = [
{
"table_name": row[0],
"column_name": row[1],
"data_type": row[2],
"data_length": row[3],
"nullable": row[4]
}
for row in results
]
return metadata
# Usage
dsn = cx_Oracle.makedsn("host", 1521, service_name="orcl")
connection = connect_to_oracle("username", "password", dsn)
if connection:
metadata = fetch_table_metadata(connection, "SCHEMA_NAME")
for item in metadata:
print(item)
Profiling Example with Pandas
import pandas as pd
def profile_columns(dataframe):
profile = {
"column_name": [],
"non_null_count": [],
"unique_count": [],
"data_type": [],
"avg_length": [],
}
for column in dataframe.columns:
profile["column_name"].append(column)
profile["non_null_count"].append(dataframe[column].count())
profile["unique_count"].append(dataframe[column].nunique())
profile["data_type"].append(dataframe[column].dtype)
profile["avg_length"].append(
dataframe[column].astype(str).str.len().mean()
)
return pd.DataFrame(profile)
# Example
data = {"col1": [1, 2, 3], "col2": ["a", "b", None]}
df = pd.DataFrame(data)
print(profile_columns(df))
4. Tools for Further Development
• SQLAlchemy for introspecting database structures:
from sqlalchemy import create_engine, MetaData
engine = create_engine("oracle+cx_oracle://user:password@host:1521/dbname")
metadata = MetaData()
metadata.reflect(bind=engine)
print(metadata.tables)
• FastAPI for creating a REST API around the profiling functionality.
5. Example Applications to Study
• Open-source tools like SQLAlchemy-Utils (metadata utilities).
• Profiling tools like dbt for schema analysis.
Starting with the above code and tools should provide you with a solid foundation for your application. Let me know if you want more specific help!