Data Engineering and Analysis Overview

Data Engineering and Analysis

• Data engineering is the process of designing, building, and maintaining systems for collecting, storing, and processing data.
• It's a critical part of data science, ensuring efficient, reliable, and scalable data handling.
• Data engineers develop and maintain data architecture and pipelines, creating programs for data generation.

Responsibilities of a Data Engineer

• Data collection: Designing and executing systems to gather data from various sources (social media, databases, sensors, etc.)
• Data storage: Employing data warehouses or lakes to efficiently store large datasets.
• Data processing: Creating distributed systems to clean, aggregate, and transform data for analysis.
• Data integration: Developing data pipelines to combine data from diverse sources.
• Data quality and governance: Ensuring data quality, reliability, and compliance with regulations.
• Data provisioning: Making processed data accessible to end users and applications.

What is a Data Analyst?

• A Data Analyst consolidates data sources to drive insights.
• Their role involves regularly building systems to model data in a clean and clear way so that everyone can use it to answer ongoing questions.
• Responsibilities: Descriptive statistics, exploratory analysis, creating visualizations to communicate findings, using Excel, SQL, and statistical software.

What is a Data Scientist?

• A Data Scientist studies large datasets using advanced statistical analysis and machine learning algorithms to identify patterns for business insights.
• They typically develop machine learning solutions for accurate and efficient insights at scale.
• Responsibilities: Developing machine learning models, analyzing complex datasets, extracting insights, coding in languages like Python or R.

Data Analyst vs. Data Scientist vs. Data Engineer

• Data engineers build and maintain the systems that data scientists and analysts use for data collection, storage, and analysis.
• Data Analysts summarize past data visually.
• Data Scientists identify patterns and make predictions about future data.

Importance of Software Engineering

• Reduced complexity: Breaking down large software problems into smaller, manageable issues.
• Minimized cost: Streamlined processes and resource optimization reduce development costs.
• Increased reliability: Emphasis on testing and maintenance to ensure software stability and reliability.
• Time Optimization: Effective software engineering practices help make the development process quicker.

Data Engineering Learning Path

• Programming: Fundamental skill emphasizing Python for its wide use in various tasks.
• Scripting and Automation: Automating data pipeline creation, maintenance, configuration, and deployment.
• Relational Databases and SQL: Understanding database structure, SQL for querying data, designing schemas, optimizing queries, and normalization.
• NoSQL Databases and MapReduce: Exploring NoSQL databases and MapReduce techniques; data models, querying, job optimization, and troubleshooting.
• Data Analysis: Understanding statistical analysis to better understand, analyze, and visualize large data sets.
• Data Processing Techniques: Employing batch processing, building pipelines (using ETL tools), and debugging data processing systems.
• Big Data: Working skillfully with big data tools (Hadoop, HDFS, MapReduce, Spark, Hive, Pig).
• Data Workflows: Creating efficient data pipelines, including ETL processing.
• Cloud Computing: Utilizing cloud-based services for data storage, processing, and analysis.
• Infrastructure: Designing, building, and maintaining data infrastructure (warehouses, lakes, marts).

What Is Data?

• Data are individual facts like numbers, words, measurements, observations.
• Types of data:
  • Quantitative: numerical data (prices, weights, ages)
  • Qualitative: descriptive, non-numerical data (names, colors).

Characteristics of Data

• Accuracy: Data should be precise.
• Validity: Data should adhere to relevant rules and definitions.
• Reliability: Data's stability and consistency across collection processes.
• Timeliness: Data should be available promptly for intended use.
• Relevance: Data must apply to the intended purposes.
• Completeness: Data must be complete and satisfy information needs.

Types of Digital Data

• Structured data: Fixed format, accessible, and organized (databases).
• Unstructured data: Irregular, no predefined format (images, audio, video).
• Semi-structured data: Combination of structured and unstructured data (XML, JSON).

Data Lifecyle Management

• Data Lifecycle Management (DLM) tracks data from creation to disposal. 
• Stages: Creation, Storage, Usage, Archival, Destruction.

Data Sources

• Relational Databases: Structured data, used for business activities, transactions, projections.
• Flat Files/XML Datasets: Diverse structured data (surveys, weather).
• APIs/Web Services: Retrieving data via network requests (social media, stock data).
• Web Scraping: Extracting unstructured data from the web.
• Data Streams/Feeds: Real-time data from IoT devices, sensors, social media.

Languages for Data Professionals

• Query languages (SQL): Accessing and manipulating data in relational databases.
• Programming languages (Python, R, Java): Developing and controlling data applications.
• Shell scripting (Linux shell): Automating repetitive tasks.

What is a Data Repository?

• A data repository is a large database infrastructure organizing data sets for various purposes (analysis, reporting, distribution).

Types of Data Repositories

• Relational databases
• Data Warehouses
• Data Marts
• Data Lakes
• Operational Data Stores
• Data Cubes
• Metadata repositories