XII UNIT 5 Introduction to Big Data and Data Analytics

Introduction to Big Data and Data Analytics

What is Big Data?

Before we talk about Big Data, let's first understand Small Data!
Small Data is information that is easy to understand and use. For example, a small shop might keep track of how many chocolates it sells every day. If the shop owner sees that a lot of chocolates are selling, they will buy more to keep customers happy!

Where Does Big Data Come From?

Big Data is HUGE and very complicated! It is so big that normal computers cannot handle it easily. Big Data comes from three main places:

  1. Buying Things Online – When people shop on websites like Amazon, their purchase details are stored as data.
  2. Machines and Sensors – Traffic cameras, weather stations, and smartwatches collect information all the time!
  3. Social Media – When people post pictures, messages, or comments online, that also creates lots of data.

Why is Big Data Important?

Since Big Data is too big for humans to check one by one, smart computers help find useful patterns. Big Data helps companies make better choices, like:

  • Netflix suggests movies based on what people like watching.
  • Online stores recommend products based on shopping history.

    Types of Big Data

    Big Data can be sorted into three types:

    Type What It Means Examples
    Structured Data Neatly organized and easy to search Customer details, sales records
    Semi-Structured Data A mix of organized and unorganized data Emails, XML files, social media messages
    Unstructured Data No set structure, harder to process Videos, images, audio files

    Pros & Cons of Big Data

    Big Data helps businesses and research, but it has challenges too.

    ✅ Advantages:

    • Smart decisions: Helps organizations make better choices.
    • Saves time & money: Detects inefficiencies and improves processes.
    • Understands customers: Helps companies personalize services.
    • Competitive edge: Businesses can predict market trends.
    • Encourages innovation: Leads to new products and services.

    ❌ Disadvantages:

    • Privacy risks: Data can be misused or leaked.
    • Messy data: Some info can be inaccurate or incomplete.
    • Hard to handle: Needs expert skills and technology.
    • Legal rules: Companies must follow data protection laws.
    • Expensive: Setting up and analyzing Big Data requires money.

    Big Data's 6 Key Features

    Big Data has six main characteristics:

    Feature Meaning
    Velocity Data is created and processed super fast (Google handles 40,000 searches per second!)
    Volume Enormous amounts of data are collected daily.
    Variety Data comes in different formats—text, images, videos, etc.
    Veracity Ensuring data is accurate and trustworthy.
    Value Finding useful insights in the data.
    Variability Data keeps changing, making it unpredictable.
                                                                                                                                                                                                                                                                                                                               

    Big Data Analytics – Finding Hidden Patterns in Huge Amounts of Data

    Big Data Analytics is a way to study large amounts of data to find trends and useful insights. It helps businesses, scientists, and even schools make better decisions!

    What is Data Analytics?

    • Data analytics means studying data to find patterns and understand things better.
    • Big Data Analytics is used for very large datasets, including organized (structured) and messy (unstructured) data.

    Examples:

    • Companies use Big Data to understand what products people like.
    • Weather experts use it to predict storms.
    • Streaming services (like YouTube) use it to suggest videos you might enjoy.

    Why is Big Data Analytics Important?

    Big Data is becoming popular because of four big changes in the world:

    1️⃣ Better Computers (Moore’s Law) – Computers are much faster than before!                      2️⃣ Smartphones Everywhere – Mobile devices collect and send a lot of data.                         3️⃣ Social Media Growth – Websites like Facebook and YouTube create huge amounts of user data.                                                                                                                                                 4️⃣ Cloud Computing – Companies store and analyze data online instead of buying expensive computers.

    How Does Big Data Analytics Work?

    Big Data Analytics is a step-by-step process:

    🟢 Step 1: Gather Data

    • Collect data from websites, sensors, and online apps.

    🟢 Step 2: Process Data

    • Organize messy data using:
      • Batch processing (slow but thorough)
      • Stream processing (fast, used for real-time decisions)

    🟢 Step 3: Clean Data

    • Fix mistakes, remove extra information, and organize data properly.

    🟢 Step 4: Analyze Data

    • Use charts, AI models, and statistics to find useful insights.

    Types of Big Data Analytics

    Big Data Analytics helps businesses in four ways:

    ✔️ Descriptive Analytics – What happened? (Example: Sales reports)
    ✔️ Diagnostic Analytics – Why did it happen? (Example: Finding reasons for high or low sales)
    ✔️ Predictive Analytics – What might happen next? (Example: Predicting future shopping trends)
    ✔️ Prescriptive Analytics – What should we do? (Example: Suggesting better ways to increase sales)

    Example: Data Analytics Tools – Tableau, APACHE Hadoop, Cassandra, MongoDB, SaS

Mining Data Streams – Finding Real-Time Patterns in Continuous Data

📌 What is a Data Stream?

  • A data stream is a nonstop flow of information from different sources.
  • Examples: Weather sensors, satellite images, online searches, social media posts.

📌 What is Mining Data Streams?

  • Mining data streams means finding useful patterns in real-time data.
  • Unlike regular data mining, it processes data immediately instead of storing everything.

✅ Example:
Imagine Google notices a sudden spike in searches for “Election Results.”

  • This could mean an election just happened.
  • Companies and news outlets can use this data to predict public interest and trending topics.

Future of Big Data Analytics – What’s Next?

The future of Big Data is shaped by exciting new technology. Here are three BIG changes:

🚀 1. Real-Time Analytics:

  • Businesses can process data instantly and get immediate insights.
  • Example: A store tracks shopping behavior in real-time to suggest personalized discounts.

🧠 2. Smarter Predictive Analytics:

  • AI will predict future trends more accurately using advanced models.
  • Example: Healthcare systems predict disease outbreaks using past patient data.

⚛️ 3. Quantum Computing – Super Fast Calculations!

  • Quantum computers will analyze massive datasets much faster than regular computers.
  • Example: Scientists will use quantum computing to discover new medicines and solve complex problems quickly.




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