Data Architecture Template: Building Scalable, Intelligent Systems

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A well-designed data architecture is essential for modern businesses leveraging AI, cloud, and data-driven intelligence. Below is a detailed breakdown of the key components:

1. Data Stores

  • Internal Databases: MySQL, PostgreSQL, MongoDB
  • Data Lakes: AWS S3, Azure Data Lake, Hadoop HDFS
  • External Data Sources: APIs (REST, GraphQL), Cloud Storage (Google Cloud Storage, AWS S3)

2. Data Acquisition (ETL)

  • Extraction: Web scraping (BeautifulSoup, Scrapy), API calls (Python requests)
  • Transformation: Pandas, PySpark, SQL queries
  • Loading: AWS Glue, Apache NiFi, Talend

3. Data Platform

  • Data Warehousing: Snowflake, Google BigQuery, Amazon Redshift
  • Data Modeling: Star Schema, Kimball Methodology, NoSQL designs

4. Data Propagation

  • Replication: Kafka, AWS DMS, Debezium
  • Data Transfer: SFTP, Rsync, AWS DataSync

5. Data Access and Providers

  • Access Methods: SQL (PostgreSQL, MySQL), REST APIs (FastAPI, Flask)
  • Security: OAuth2, JWT, Role-Based Access Control (RBAC)

6. Data Analytics

  • BI Tools: Tableau, Power BI, Looker
  • Advanced Analytics: Python (NumPy, SciPy), R, TensorFlow

7. Data Governance

  • Data Quality: Great Expectations, Deequ
  • Compliance: GDPR, CCPA, HIPAA

You Should Know:

Essential Linux Commands for Data Management


<h1>Monitor disk usage</h1>

df -h

<h1>Search for files</h1>

find / -name "*.csv"

<h1>Transfer files securely</h1>

scp file.txt user@remote:/path

<h1>Process logs in real-time</h1>

tail -f /var/log/syslog | grep "error" 

Windows Commands for Data Operations


<h1>List running services</h1>

Get-Service

<h1>Check disk health</h1>

chkdsk /f

<h1>Export data to CSV</h1>

Get-Process | Export-Csv processes.csv 

Python Snippet for ETL

import pandas as pd 
df = pd.read_csv("data.csv") 
df_clean = df.dropna() 
df_clean.to_parquet("clean_data.parquet") 

What Undercode Say:

A robust data architecture ensures scalability, security, and efficiency. Leveraging cloud platforms, automation, and governance frameworks is critical for future-proofing data systems.

Expected Output:

A structured data pipeline with automated ETL, secure access controls, and real-time analytics capabilities.

Relevant URL: Join My Tech Community

References:

Reported By: Ashish – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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