Introduction to Data Engineering

Introduction to Data Engineering

• Data Engineering involves designing, building, and maintaining data systems
• Data Engineering enables organizations to aggregate, store, and analyze massive amounts of data
• Data Engineering ensures data accessibility, consistency, and security for analytics and ML applications
• Data Engineering focuses on data pipelines, ETL processes, data storage solutions, and big data technologies to enable data-driven decision-making

Data Engineering Lifecycle

• Data Engineering Lifecycle consists of five primary stages to ensure efficient data handling, processing, and utilization
• Generation: Data originates from applications, IoT devices, logs, and external APIs for example user interactions, sensor data, and system logs
• Storage: Data is stored in databases, data lakes, or cloud storage with scalability, reliability, and security
• Examples of storage are relational databases (SQL), NoSQL databases, and object storage
• Ingestion: Involves moving data from various sources into a centralized storage system via Batch ingestion (ETL) or real-time ingestion (streaming) and tools such as Apache Kafka, AWS Kinesis, and Airflow
• Transformation: Converts raw data into structured and usable formats for analytics and machine learning via Data cleaning, deduplication, and aggregation
• Apache Spark, dbt, and SQL-based transformations are technologies used
• Serving Data: Involves providing processed data to end-users, applications, and analytical tools such as Dashboards, machine learning models, APIs
• BI tools (Tableau, Power BI), OLAP systems, and Data APIs can be used

Data Sources

• Structured Data: Relational Databases CSV
• Semi-structured Data: API responses, XML, Web Scraping, JSON
• Unstructured Data: Images, Videos, IoT sensor data, Logs

Data Storage

• Databases
• Data Lakes
• Data Warehouses

Types of Storage Systems

• Relational Databases (SQL): MySQL, PostgreSQL, SQL Server
• NoSQL Databases: MongoDB (Document), Cassandra, Redis (Key-Value), (Column)
• Data Lakes: AWS S3, Azure Data Lake, Google Cloud Storage
• Data Warehouses: Snowflake, Google BigQuery, Amazon Redshift

Key Concepts: ETL (Extract, Transform, Load)

• Extract: Gather data from various sources like APIs, databases, and logs
• Transform: Clean, format, and structure data
• Load: Store processed data into Data Warehouses or Data Lakes
• Modern ELT (Extract, Load, Transform) shifts transformations to the target system

Batch Processing vs. Real-Time Processing

• Batch processing involves collecting data over a period of time and processing it in chunks (batches), this is suitable for large-scale data transformation where real-time processing is unnecessary
• Processes large volumes of data at once, works on a scheduled basis, has higher latency, and is for historical data analysis and reports, requires less computational power compared to real-time
• Real-time processes continuous data streams as they arrive, used in applications that require instant response and minimal latency
• Processes data instantly as events happen, handles low-latency (milliseconds to seconds), used for time-sensitive applications, requires higher computational resources and works with event-driven architectures
• Both Batch & Real-Time Processing are essential in Data Engineering, modern systems often combine both (Hybrid Approach), choosing the right method depends on the business use case

Data Engineering vs Data Science

• Data Engineering focuses on building and maintaining data pipelines and storage systems
• Data Engineering ensures data is clean, organized, and available for analysis using Tools like SQL, Hadoop, Spark, Airflow, and Kafka to move and process data
• Data Science analyzes data to find patterns, make predictions, and solve problems using machine learning, statistics, and visualization techniques with tools like Python, Pandas, TensorFlow, and Jupyter Notebooks to create models and insights

Importance of Data Engineering

• Organizations rely on data pipelines to provide accurate and real-time insights for decision-making via Data-Driven Decision Making
• AI models require clean, structured, and high-quality data managed by data engineers, Supporting Al and Machine Learning
• Data engineers handle vast amounts of structured and unstructured data with Big Data technologies like Hadoop & Spark from Managing Large-Scale Data Efficiently

Key Concepts: Data Pipelines

• A data pipeline is a system that automatically moves and processes data from one place to another
• Data pipelines ensure data flows smoothly between sources like databases, APIs, or files and destinations like data warehouses, data lakes, or analytics dashboards

Key Features of a Data Pipeline

• Automated Workflow: Data is processed without manual effort
• Data Movement: Transfers data between different systems
• Transformation: Cleans, formats, and structures data for better use
• Reliability: Ensures data is delivered accurately and on time

Types of Data Pipelines

• Batch Pipelines: Process large amounts of data at scheduled intervals like nightly reports
• Real-time Pipelines: Continuously process and update data instantly like fraud detection systems

Example Data Pipeline on e-commerce website

• Step 1: A customer places an order
• Step 2: The order data is collected from the website database
• Step 3: The data is cleaned and processed like checking for missing values
• Step 4: The processed data is stored in a data warehouse
• Step 5: Analysts and Al models use the data for insights like sales reports and recommendations

Tools Used in Data Pipelines

• Apache Airflow Manages workflow automation
• Apache Kafka Handles real-time data streaming
• Apache Spark Processes large datasets efficiently
• ETL/ELT Tools Extract, transform, and load data

ETL (Extract, Transform, Load) in Data Engineering

• ETL stands for Extract, Transform, Load which is a key process in data engineering that moves data from different sources, processes it, and loads it into a target system like a data warehouse or data lake
• Extract (E) - Collecting Data: Data is extracted from multiple sources such as databases, APIs, logs, or spreadsheets that can be structured (SQL databases) or unstructured (Images, sound, text logs)
• Tools used: SQL queries, APIs, Web Scrapers, Apache Kafka
• Transform (T) - Cleaning & Processing: Raw data is cleaned, reformatted, and enriched as Common transformations by removing duplicates & handling missing values, standardizing data formats and aggregating or joining data from multiple sources
• Tools: Apache Spark, Pandas, dbt, AWS Glue
• Load (L) - Storing Data: Processed data is loaded into a Data Warehouse (e.g., Snowflake, BigQuery, Redshift) or Data Lake (e.g., Amazon S3, Azure Data Lake) and optimized for fast queries and analytics
• Tools are ETL pipelines (Airflow, Talend, Informatica), SQL-based inserts

Data Warehouse vs. Data Lake

• A Data Warehouse stores cleaned, structured, and processed data for reporting and analysis
• Key features include Storing structured data in tables, data is organized and optimized for fast queries, business intelligence & reporting and follows Schema-on-Write
• A Data Lake is a large storage system that keeps raw, unstructured, and structured data in its original form
• Key Features: Data Lake can store any type of data whether structured, semi-structured or unstructured, its used for data science, Al, and big data analytics
• Data Lakes follow Schema-on-Read and can store data cheaply for future use

Technologies Used in Data Engineering

• SQL Databases: PostgreSQL, MySQL, Oracle for structured data
• NoSQL Databases: MongoDB, Cassandra for flexible, high-scale data
• Big Data Tools: Apache Hadoop, Spark for large-scale distributed processing
• Streaming Platforms: Apache Kafka for real-time data ingestion
• Orchestration: Apache Airflow for scheduling and monitoring workflows

Big Data Processing

• Apache Hadoop: Distributed file storage and batch processing (MapReduce, HDFS)
• Apache Spark: In-memory, high-speed data processing engine with batch & streaming support

Real-World Applications of Data Engineering

• Healthcare: Real-time patient monitoring, electronic health records processing
• Finance: Fraud detection, risk management, customer analytics
• E-Commerce: Recommendation engines, inventory optimization, user behavior analysis