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Databases Tools for Data Analytics

Project description

DB Analytics Tools

Databases Analytics Tools is a Python open source micro framework for data analytics. DB Analytics Tools is built on top of Psycopg2, Pyodbc, Pandas, Matplotlib and Scikit-learn. It helps data analysts to interact with data warehouses as traditional databases clients.

Why adopt DB Analytics Tools ?

  • Easy to learn : It is high level API and doesn't require any special effort to learn.
  • Real problems solver : It is designed to solve real life problems of the Data Analyst
  • All in One : Support queries, Data Integration, Analysis, Visualization and Machine Learning

Core Components

# Component Description How to import
0 db Database Interactions (Client) import db_analytics_tools as db
1 dbi Data Integration & Data Engineering import db_analytics_tools.integration as dbi
2 dba Data Analysis import as dba
3 dbviz Data Visualization import db_analytics_tools.plotting as dbviz
4 dbml Machine Learning & MLOps import db_analytics_tools.learning as dbml

Install DB Analytics Tools


DB Analytics Tools requires

  • Python
  • Psycopg2
  • Pyodbc
  • Pandas
  • SQLAlchemy
  • Streamlit

DB Analytics Tools can easily installed using pip

pip install db-analytics-tools

Get Started

Setup client

As traditional databases clients, we need to provide database server ip address and port and credentials. DB Analytics Tools supports Postgres and SQL Server.

# Import DB Analytics Tools
import db_analytics_tools as db

# Database Infos & Credentials
ENGINE = "postgres"
HOST = "localhost"
PORT = "5432"
DATABASE = "postgres"
USER = "postgres"
PASSWORD = "admin"

# Setup client
client = db.Client(host=HOST, port=PORT, database=DATABASE, username=USER, password=PASSWORD, engine=ENGINE)

Data Definition Language

query = """
----- CREATE TABLE -----
drop table if exists public.transactions;
create table public.transactions (
    transaction_id integer primary key,
    client_id integer,
    product_name varchar(255),
    product_category varchar(255),
    quantity integer,
    unitary_price numeric,
    amount numeric


Data Manipulation Language

query = """
----- POPULATE TABLE -----
insert into public.transactions (transaction_id, client_id, product_name, product_category, quantity, unitary_price, amount)
	(1,101,'Product A','Category 1',5,100,500),
	(2,102,'Product B','Category 2',3,50,150),
	(3,103,'Product C','Category 1',2,200,400),
	(4,102,'Product A','Category 1',7,100,700),
	(5,105,'Product B','Category 2',4,50,200),
	(6,101,'Product C','Category 1',1,200,200),
	(7,104,'Product A','Category 1',6,100,600),
	(8,103,'Product B','Category 2',2,50,100),
	(9,103,'Product C','Category 1',8,200,1600),
	(10,105,'Product A','Category 1',3,100,300);


Data Query Language

query = """
----- GET DATA -----
select *
from public.transactions
order by transaction_id;

dataframe = client.read_sql(query=query)
   transaction_id  client_id product_name product_category  quantity  unitary_price  amount
0               1        101    Product A       Category 1         5          100.0   500.0
1               2        102    Product B       Category 2         3           50.0   150.0
2               3        103    Product C       Category 1         2          200.0   400.0
3               4        102    Product A       Category 1         7          100.0   700.0
4               5        105    Product B       Category 2         4           50.0   200.0

Implement SQL based ETL

ETL API is in the integration module db_analytics_tools.integration. Let's import it ans create an ETL object.

# Import Integration module
import db_analytics_tools.integration as dbi

# Setup ETL
etl = dbi.ETL(client=client)

ETLs for DB Analytics Tools consists in functions with date parameters. Everything is done in one place i.e on the database. So first create a function on the database like this :

query = """
create or replace function public.fn_test(rundt date) returns integer
language plpgsql
	raise notice 'rundt : %', rundt;

	--- EXTRACT ---

	--- TRANSFORM ---

	--- LOAD ---

	return 0;


Run a function

Then ETL function can easily be run using the ETL class via the method

# ETL Function
FUNCTION = "public.fn_test"

## Dates to run
START = "2023-08-01"
STOP = "2023-08-05"

# Run ETL, start_date=START, stop_date=STOP, freq="d", reverse=False)
Function    : public.fn_test
Date Range  : From 2023-08-01 to 2023-08-05
Iterations  : 5
[Runing Date: 2023-08-01] [Function: public.fn_test] Execution time: 0:00:00.122600
[Runing Date: 2023-08-02] [Function: public.fn_test] Execution time: 0:00:00.049324
[Runing Date: 2023-08-03] [Function: public.fn_test] Execution time: 0:00:00.049409
[Runing Date: 2023-08-04] [Function: public.fn_test] Execution time: 0:00:00.050019
[Runing Date: 2023-08-05] [Function: public.fn_test] Execution time: 0:00:00.108267

Run several functions

Most of time, several ETL must be run and DB Analytics Tools supports running functions as pipelines.

## ETL Functions

## Dates to run
START = "2023-08-01"
STOP = "2023-08-05"

# Run ETLs
etl.run_multiple(functions=FUNCTIONS, start_date=START, stop_date=STOP, freq="d", reverse=False)
Functions   : ['public.fn_test', 'public.fn_test_long', 'public.fn_test_very_long']
Date Range  : From 2023-08-01 to 2023-08-05
Iterations  : 5
[Runing Date: 2023-08-01] [Function: public.fn_test..........] Execution time: 0:00:00.110408
[Runing Date: 2023-08-01] [Function: public.fn_test_long.....] Execution time: 0:00:00.112078
[Runing Date: 2023-08-01] [Function: public.fn_test_very_long] Execution time: 0:00:00.092423
[Runing Date: 2023-08-02] [Function: public.fn_test..........] Execution time: 0:00:00.111153
[Runing Date: 2023-08-02] [Function: public.fn_test_long.....] Execution time: 0:00:00.111395
[Runing Date: 2023-08-02] [Function: public.fn_test_very_long] Execution time: 0:00:00.110814
[Runing Date: 2023-08-03] [Function: public.fn_test..........] Execution time: 0:00:00.111044
[Runing Date: 2023-08-03] [Function: public.fn_test_long.....] Execution time: 0:00:00.123229
[Runing Date: 2023-08-03] [Function: public.fn_test_very_long] Execution time: 0:00:00.078432
[Runing Date: 2023-08-04] [Function: public.fn_test..........] Execution time: 0:00:00.127839
[Runing Date: 2023-08-04] [Function: public.fn_test_long.....] Execution time: 0:00:00.111339
[Runing Date: 2023-08-04] [Function: public.fn_test_very_long] Execution time: 0:00:00.140669
[Runing Date: 2023-08-05] [Function: public.fn_test..........] Execution time: 0:00:00.138380
[Runing Date: 2023-08-05] [Function: public.fn_test_long.....] Execution time: 0:00:00.111157
[Runing Date: 2023-08-05] [Function: public.fn_test_very_long] Execution time: 0:00:00.077731


Documentation available on

Help and Support

If you need help on DB Analytics Tools, please send me an message on Whatsapp or send me a mail.


Please see the contributing docs.


DB Analytics Tools is maintained by Joseph Konka. Joseph is a Data Science Professional with a focus on Python based tools. He developed the base code while working at Togocom to automate his daily tasks. He packages the code into a Python package called SQL ETL Runner which becomes Databases Analytics Tools. For more about Joseph Konka, please visit

Let's get in touch

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