Can AI in data analytics handle more than ETL automation, like data quality,and storage ?

[kodexolabs]

It's a frequent misperception that AI in data engineering is just used for ETL process automation. AI, however, can also optimize data storage, control massive data pipelines, and guarantee data quality. Data integrity can be preserved by using sophisticated techniques like anomaly detection to find data inconsistencies. An Isolation Forest model, for example, can be used to find abnormalities in datasets.

import numpy as np
from sklearn.ensemble import IsolationForest

data = np.array([[10, 20], [15, 25], [30, 35], [500, 600]])
model = IsolationForest(contamination=0.1)
model.fit(data)
anomalies = model.predict(data)
print(anomalies)

As proven with Apache Airflow, AI can manage data pipelines by automating data integration and transformation operations, assuring effective processing and storage.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def process_data():
    print("Processing data...")

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
}
dag = DAG('data_pipeline', default_args=default_args, schedule_interval='@daily')

process_task = PythonOperator(
    task_id='process_data',
    python_callable=process_data,
    dag=dag
)

Using usage patterns to forecast future requirements, AI can improve data storage. The amount of storage needed can be predicted using a linear regression model.

import numpy as np
from sklearn.linear_model import LinearRegression

usage_data = np.array([[1, 100], [2, 150], [3, 200], [4, 250]])
X = usage_data[:, 0].reshape(-1, 1)
y = usage_data[:, 1]

model = LinearRegression()
model.fit(X, y)

future_usage = np.array([[5], [6], [7]])
predicted_usage = model.predict(future_usage)
print(predicted_usage)

In order to guarantee traceability and transparency in data transformations, AI may also automate data lineage tracking.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df['C'] = df['A'] + df['B']

lineage = {
    'original_data': df[['A', 'B']].to_dict(),
    'transformed_data': df.to_dict()
}

print(lineage)

In conclusion, AI in data engineering goes beyond ETL process automation. It improves data lineage tracing, pipeline management, storage optimization, and data quality. By using these methods, data engineering procedures can be greatly enhanced, becoming more dependable and efficient. Together, let's investigate and exchange ideas on these cutting-edge possibilities!