Ordered Guide to Big Data, Data Lakes, Data Warehouses & Lakehouses

1  The Modern Data Landscape — Bird’s‑Eye View

Data Sources → Ingestion → Storage → Processing → Analytics / ML → Decisions

Every storage paradigm slots into this flow at the Storage layer, but each optimises different trade‑offs for the rest of the pipeline.

2  Foundations: What Is Big Data?

Typical Sources: social media streams, IoT sensors, click‑streams, transactions, logs, images, videos.

3  Storage Paradigms in Order

3.1 Traditional Databases

• Relational (RDBMS) — schema‑on‑write, ACID (MySQL, PostgreSQL, Oracle)
• NoSQL families — key‑value, document, column‑family, graph (Redis, MongoDB, Cassandra, Neo4j)
  Pros: strong consistency (RDBMS) or horizontal scale (NoSQL)
  Cons: limited for petabyte‑scale raw data or multi‑format analytics

3.2 Data Lake

Object storage that accepts raw data as‑is (schema‑on‑read).
Layers

1. Storage: S3, ADLS, GCS
2. Ingestion: Kafka, Flink, AWS Glue
3. Catalog: AWS Glue Data Catalog, Unity Catalog
4. Processing: Spark, Presto, Athena
5. Governance: IAM, encryption, audit logs
   Strengths: low‑cost, flexible, ML‑friendly
   Challenges: governance, slow ad‑hoc SQL without optimisation

3.3 Big Data Lake (Enterprise‑Grade)

An evolved lake built for multi‑cloud scale, strict governance, and real‑time workloads.

• Adds ACID & versioning with Delta Lake, Apache Hudi, Iceberg
• Data lineage, column‑level access control, and time‑travel queries

3.4 Data Warehouse

Schema‑on‑write repository optimised for OLAP analytics.
Architecture

Sources → ETL → Staging → Warehouse (Star / Snowflake) → BI & Marts

• Cloud DWs: Snowflake, BigQuery, Amazon Redshift, Azure Synapse
• On‑prem DWs: Teradata, Oracle Exadata
  Pros: blazing‑fast SQL, consistent “single source of truth”
  Cons: higher storage cost, rigid schema, mostly structured data

3.5 Data Lakehouse

Unifies lake flexibility with warehouse performance via open table formats.

• Delta Lake / Iceberg / Hudi underpin ACID, indexing, time‑travel
• Enables BI & ML on the same copy of data

4  Quick Comparison

5  Architectural Lineage Diagrams

5.1 Data Lake Lineage

┌────────┐   ┌───────────────┐   ┌──────────┐   ┌────────────┐
| Source │→│ Raw Object Stg │→│ Catalog │→│ Spark / Presto │→ BI / ML
└────────┘   └───────────────┘   └──────────┘   └────────────┘

5.2 Data Warehouse Lineage

┌────────┐   ┌───────┐   ┌──────────┐   ┌──────────┐   ┌──────┐
| Source │→│  ETL  │→│ Staging │→│ Fact & Dim │→ BI │
└────────┘   └───────┘   └──────────┘   └──────────┘   └──────┘

5.3 Lakehouse Lineage

┌────────┐   ┌───────────────┐   ┌────────────────┐   ┌──────────┐
| Source │→│ Bronze (T1)   │→│ Silver (optimised) │→│ Gold (BI) │
└────────┘   └───────────────┘   └────────────────┘   └──────────┘

6  Technology Cheat‑Sheet

7  Choosing the Right Approach

• Need historical KPIs, CFO‑grade accuracy? → Warehouse
• Exploratory data science on raw logs & images? → Data Lake
• Desire one platform for BI + ML without duplication? → Lakehouse
• Regulated enterprise, petabytes, multi‑cloud? → Big Data Lake + Lakehouse overlay

8  Challenges & Best Practices

1. Governance — implement catalog + column‑level ACLs early.
2. Cost Control — tiered storage & auto‑vacuum unused data.
3. Performance — partition, Z‑order, and cache hot data.
4. Data Quality — enforce contracts (Great Expectations, Deequ).

9  Key Takeaways

Big Data (the raw ingredients) needs the right pantry:

• Lakes deliver flexibility; warehouses deliver speed; lakehouses strive for both.
• The storage choice dictates downstream tooling, governance, and cost—but they are complementary pieces of a single analytics continuum.

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