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https://github.com/joekakone/db-analytics-tools
Databases Analytics Tools - Data Integration - Data Visualization - Machine Learning
https://github.com/joekakone/db-analytics-tools
data-engineering data-integration data-visualization etl machine-learning pipeline sql
Last synced: 24 days ago
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Databases Analytics Tools - Data Integration - Data Visualization - Machine Learning
- Host: GitHub
- URL: https://github.com/joekakone/db-analytics-tools
- Owner: joekakone
- License: mit
- Created: 2023-04-23T15:45:57.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-04-08T12:35:13.000Z (7 months ago)
- Last Synced: 2024-10-02T03:04:44.313Z (about 1 month ago)
- Topics: data-engineering, data-integration, data-visualization, etl, machine-learning, pipeline, sql
- Language: Python
- Homepage: https://pypi.org/project/db-analytics-tools/
- Size: 116 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 db_analytics_tools.analytics 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
### Dependencies
DB Analytics Tools requires
* Python
* Psycopg2
* Pyodbc
* Pandas
* SQLAlchemy
* Streamlit
DB Analytics Tools can easily installed using pip
```sh
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.
```python
# 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
```python
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
);
"""
client.execute(query=query)
```
### Data Manipulation Language
```python
query = """
----- POPULATE TABLE -----
insert into public.transactions (transaction_id, client_id, product_name, product_category, quantity, unitary_price, amount)
values
(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);
"""
client.execute(query=query)
```
### Data Query Language
```python
query = """
----- GET DATA -----
select *
from public.transactions
order by transaction_id;
"""
dataframe = client.read_sql(query=query)
print(dataframe.head())
```
```txt
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.
```python
# 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 :
```python
query = """
----- CREATE FUNCTION ON DB -----
create or replace function public.fn_test(rundt date) returns integer
language plpgsql
as
$$
begin
--- DEBUG MESSAGE ---
raise notice 'rundt : %', rundt;
--- EXTRACT ---
--- TRANSFORM ---
--- LOAD ---
return 0;
end;
$$;
"""
client.execute(query=query)
```
### Run a function
Then ETL function can easily be run using the ETL class via the method `ETL.run()`
```python
# ETL Function
FUNCTION = "public.fn_test"
## Dates to run
START = "2023-08-01"
STOP = "2023-08-05"
# Run ETL
etl.run(function=FUNCTION, 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.
```python
## ETL Functions
FUNCTIONS = [
"public.fn_test",
"public.fn_test_long",
"public.fn_test_very_long"
]
## 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
Documentation available on [https://joekakone.github.io/db-analytics-tools](https://joekakone.github.io/db-analytics-tools).
## Help and Support
If you need help on DB Analytics Tools, please send me an message on [Whatsapp](https://wa.me/+22891518923) or send me a [mail](mailto:[email protected]).
## Contributing
[Please see the contributing docs.](CONTRIBUTING.md)
## Maintainer
DB Analytics Tools is maintained by [Joseph Konka](https://www.linkedin.com/in/joseph-koami-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 [www.josephkonka.com](https://josephkonka.com).
## Let's get in touch
[](https://github.com/joekakone) [](https://www.linkedin.com/in/joseph-koami-konka/) [](https://www.twitter.com/joekakone) [](mailto:[email protected])