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Big Data Architecture is the backbone of modern data-driven decision-making. It transforms raw data into actionable insights through a structured pipeline. Here’s a breakdown of the key components and how they work together:
1. Data Sources
Data comes in three forms:
- Structured (SQL databases, CSV files)
- Semi-structured (JSON, XML)
- Unstructured (social media logs, IoT sensor data)
Linux Command to Check Data Sources:
List active data streams (Kafka example) kafka-topics.sh --list --zookeeper localhost:2181
2. Data Ingestion
Two primary methods:
- Batch Processing (Hadoop, Spark)
- Real-time Streaming (Kafka, Flink)
Python Code for Real-time Ingestion (Kafka Consumer):
from kafka import KafkaConsumer
consumer = KafkaConsumer('data_topic', bootstrap_servers='localhost:9092')
for msg in consumer:
print(msg.value.decode('utf-8'))
3. Data Storage
Options include:
- RDBMS (PostgreSQL, MySQL)
- NoSQL (MongoDB, Cassandra)
- Distributed Storage (HDFS)
HDFS Command to Store Data:
hdfs dfs -put local_data.csv /user/bigdata/input
4. Analytics & Servicing
Techniques:
- Machine Learning (Scikit-learn, TensorFlow)
- Predictive Modeling (PySpark ML)
PySpark Example for Analysis:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("BigDataAnalysis").getOrCreate()
df = spark.read.csv("hdfs://path/to/data.csv", header=True)
df.show()
5. Data Consumption
Visualization tools:
- Dashboards (Tableau, Power BI)
- Real-time Alerts (Elasticsearch, Kibana)
Elasticsearch Query for Alerts:
GET /_search
{ "query": { "match": { "error_level": "critical" } } }
6. Big Data Governance
Ensures compliance (GDPR, HIPAA). Tools:
- Apache Atlas (Metadata Management)
- Apache Ranger (Access Control)
Linux Command for Log Auditing:
sudo auditctl -w /var/log/bigdata -p rwxa -k bigdata_monitor
You Should Know:
- Optimizing Data Ingestion: Use Kafka + Spark Streaming for low-latency processing.
- Securing Storage: Encrypt HDFS with Kerberos:
kinit -kt /etc/security/keytabs/hdfs.keytab hdfs@DOMAIN
- Scaling Analytics: Use Kubernetes for deploying ML models:
kubectl create deployment ml-model --image=tensorflow/serving
What Undercode Say:
Big Data Architecture is not just about tools—it’s about efficient pipelines, security, and real-time decision-making. Mastering these components ensures scalability, compliance, and business agility.
Expected Output:
A well-structured Big Data pipeline that ingests, processes, and serves insights while maintaining security and governance.
Prediction:
Future architectures will integrate AI-driven automation for self-optimizing data flows, reducing manual intervention.
Relevant URLs:
IT/Security Reporter URL:
Reported By: Ashish – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
