INTRODUCTION TO DATA ENGINEERING

Data engineering entails the designing,building and maintaining of scalable data infrastructure which enables efficient :-

• data processing
• data storage
• data retrival

KEY CONCEPTS OF DATA ENGINEERING

DATA PIPELINES -automates the flow of data from source(s) to destination(s), often passing through multiple stages like cleaning, transformation, and enrichment.

Core Components of a Data Pipeline

1. Source(s): Where the data comes from

Databases (e.g., MySQL, PostgreSQL)

APIs (e.g., Twitter API)

Files (e.g., CSV, JSON, Parquet)

Streaming services (e.g., Kafka)

2.Ingestion: Collecting the data

Tools: Apache NiFi, Apache Flume, or custom scripts

3.Processing/Transformation: Cleaning and preparing data

Batch processing: Apache Spark, Pandas

Stream processing: Apache Kafka, Apache Flink

4.Storage: Where the processed data is stored

Data Lakes (e.g., S3, HDFS)

Data Warehouses (e.g., Snowflake, BigQuery, Redshift)

5.Orchestration: Managing dependencies and scheduling

Tools: Apache Airflow, Prefect, Luigi

6.Monitoring & Logging: Making sure everything works as expected

Logging tools (e.g., ELK Stack, Datadog)

Alerting systems

ETL - ETL stands for Extract, Transform, Load — it's a core concept in data engineering used to move and process data from source systems into a destination system like a data warehouse.

ETL Example
Let’s say you're analyzing sales data:

Extract: Pull sales data from a MySQL database and product info from a CSV.

Transform:

Join sales with product names

Format dates

Remove duplicates or missing values

Load: Save the clean, combined data to a Snowflake table for analytics.

DATABASES AND DATA WAREHOUSES

What is a Database?
A database is designed to store current, real-time data for everyday operations of applications.

✅ Used For:

• CRUD operations (Create, Read, Update, Delete)
• Running websites, apps, or transactional systems
• Real-time access

🔧 Examples:
Relational: MySQL, PostgreSQL, Oracle, SQL Server

NoSQL: MongoDB, Cassandra, DynamoDB

What is a Data Warehouse?
A data warehouse is designed for analytics and reporting. It stores historical, aggregated, and structured data from multiple sources.

✅ Used For:

• Running analytics and reports
• Business Intelligence (BI)
• Long-term storage of historical data

🔧 Examples:

• Snowflake
• Amazon Redshift
• Google BigQuery
• Azure Synapse

CLOUD COMPUTING
Cloud computing entails the provision of on-demand access to computing resources.
these resources include-

• Servers
• Databases
• Storage

Importance of cloud computing

1. 🚀 Scalability Need to process 1 GB or 10 TB of data? Cloud services like AWS, GCP, and Azure scale automatically.

Easily handle spikes in data volume without buying new hardware.

Example: Auto-scaling a Spark cluster on AWS EMR for large data processing.

1. 💰 Cost-Efficiency (Pay-as-you-go) Only pay for what you use — no need for expensive on-prem hardware.

Great for startups and enterprises alike.

Example: Storing terabytes in Amazon S3 vs buying physical servers.

1. 🔧 Managed Services You don’t need to set up or maintain infrastructure.

Tools like BigQuery, Snowflake, AWS Glue, Databricks, and Azure Data Factory handle the heavy lifting.

Example: Load data into BigQuery and run SQL instantly — no server setup required.

BENEFITS OF CLOUD COMPUTING

• Scalabilitiy - scaling of compute and storage resources
• Cost effective- Pay as you go
• Security- provide compliance and encryption
• collaboration- access services within the internet

CLOUD SERVICE MODELS

• Infrastructure as a Service(IaaS)- provides virtualized computing resources over the internet.
  Examples:

• AWS EC2

• Google Compute Engine

• Azure Virtual Machines

• Platform as a service(PaaS)- allows management of of runtime environment
  Examples:

• Google App Engine

• AWS Elastic Beanstalk

• Azure App Service

• Software as a Service(SaaS)- allows fully managed software applications.
  Examples:

• Google Workspace (Docs, Sheets)

• Salesforce

• Microsoft 365

• Dropbox

CLOUD DEPLOYMENT MODELS

1. Public cloud The cloud infrastructure is owned and operated by a third-party provider (like AWS, Azure, GCP), and services are delivered over the internet.

Key Features:

• Shared infrastructure (multi-tenant)
• Scalable and cost-effective
• Pay-as-you-go pricing

Examples:

• AWS (Amazon Web Services)
• Microsoft Azure
• Google Cloud Platform (GCP)
• Private cloud Cloud infrastructure is exclusively used by one organization. It can be hosted on-premises or in a third-party data center.

Key Features:

• Greater control and security
• Customization for business needs
• Often more expensive to maintain

Examples:

• VMware vSphere
• OpenStack
• Private Azure Stack

1. Hybrid cloud A combination of public and private clouds, allowing data and applications to move between them.

Key Features:

• Flexibility to run workloads where they fit best
• Cost optimization and scalability
• Secure handling of sensitive data

Examples:

• AWS Outposts (AWS + on-prem)
• Azure Arc
• Google Anthos

DATA GOVERNANCE & SECURITY
Data governance is the set of policies, processes, and standards that ensure data is accurate, consistent, and properly managed across an organization.

Goals of Data Governance:

• Ensure data quality (no duplicates, missing values, or inconsistencies)
• Enable data ownership (who owns/controls different data assets)
• Promote data cataloging and discoverability
• Enforce data access rules and compliance (GDPR, HIPAA, etc.)

Data Security
Data security protects data from unauthorized access, breaches, leaks, or corruption.

🔑 Key Areas:
a. Access Control

• Role-Based Access Control (RBAC)

• Identity and Access Management (IAM)
  b. Data Encryption

• At rest: Encrypt data stored in disks/databases (e.g., S3 encryption)

• In transit: Use HTTPS/TLS to encrypt data during transfer

c. Auditing & Monitoring

• Log who accessed or changed what and when
• Detect suspicious activity d. Data Masking / Tokenization Hide or scramble sensitive fields (e.g., credit card numbers)