PBig data refers to massive, complex datasets that traditional data processing systems cannot handle, characterized by their Volume, Velocity, Variety, Veracity, and Value

Big data refers to massive, complex datasets that traditional data processing systems cannot handle, characterized by their Volume, Velocity, Variety, Veracity, and Value. The core purpose of big data is to extract meaningful insights and patterns through analysis, enabling organizations to make better, more informed decisions, identify growth opportunities, and improve business models.

What Big Data Is
It's not just about size: While the sheer volume of data is a key aspect, big data also encompasses the variety and speed at which data is generated.
Unstructured and Complex: It includes data from various sources like sensors, social media, images, videos, text, and transactions, which can be structured, semi-structured, or unstructured.
A Transformative Concept: The growth of the internet and connected devices has led to an exponential increase in data volume and complexity, giving rise to the concept of big data

1. Introduction to Big Data Ecosystem Pertemuan 1 Program Studi Teknik Informatika Corporate Minimalist Deck

2. Learning Outcomes Memahami definisi dan karakteristik 5V Big Data Membedakan OLTP vs OLAP dalam konteks data Menjelaskan perbedaan Data Warehouse, Data Lake, dan Lakehouse Mengidentifikasi arsitektur Lambda dan Kappa Menganalisis dampak Big Data pada industri modern

3. Peta Konsep Sesi 1. Big Data & 5V 2. OLTP vs OLAP 3. Data Warehouse vs Data Lake vs Lakehouse 4. Arsitektur Lambda & Kappa 5. Studi Kasus Industri 6. Tren & Update 2025

4. Big Data & The 5V Volume Velocity Variety Veracity Value

5. OLTP vs OLAP OLTP: Online Transaction Processing (operasional, real-time) OLAP: Online Analytical Processing (analitik, historical, agregasi) Perbedaan utama: tujuan, jenis query, volume data

6. DW vs DL vs Lakehouse Data Warehouse: terstruktur, untuk BI/OLAP Data Lake: menyimpan semua jenis data (structured, semi-structured, unstructured) Lakehouse: menggabungkan fleksibilitas DL dengan keandalan DW

7. Arsitektur Big Data Lambda Architecture: batch + streaming layer Kappa Architecture: hanya streaming layer (lebih sederhana)

8. Dampak Big Data pada Industri Netflix: personalisasi rekomendasi film Tokopedia: analitik perilaku belanja real-time Gojek: optimasi order dan pricing secara real-time

9. Update Tren 2025 AI-augmented Data Engineering Adopsi Lakehouse (Delta, Iceberg, Hudi) semakin dominan Integrasi Big Data dengan LLM dan RAG (Retrieval-Augmented Generation) Rise of DuckDB & Polars untuk analitik cepat

10. Ringkasan & Tugas Ringkasan: Big Data = 5V, DW vs DL vs Lakehouse, Lambda/Kappa, dampak industri. Tugas: Cari studi kasus Big Data terbaru (2024–2025) dan buat analisis singkat (max 2 halaman).