🧭 Big Data Ecosystem Overview

This document explains how the main components of the Hadoop-based Big Data ecosystem connect and work together: Hue, Hive, Impala, HDFS, Spark (PySpark), HBase, Iceberg, Parquet, ORC, Oozie, and Teradata.

🧱 Core Components and Their Roles

Component	Type	Purpose
HDFS	Storage	Distributed file system for storing large datasets across cluster nodes.
Hive	SQL Engine	Batch SQL engine for data warehousing and ETL.
Impala	SQL Engine	Low‐latency, interactive SQL engine for analytics.
Hue	Web UI	Web-based interface to interact with Hadoop ecosystem tools.
Spark / PySpark	Processing Engine	Distributed computing engine for data processing, ETL, ML, analytics.
Iceberg	Table Format	Modern table layer adding ACID transactions, schema evolution, time travel.
HBase	NoSQL Database	Real-time read/write access to data stored on HDFS.
Parquet / ORC	File Formats	Columnar storage formats optimized for big data analytics.
Oozie	Workflow Scheduler	Orchestrates and schedules Hadoop ecosystem jobs (Hive, Spark, etc.).
Teradata	Enterprise Data Warehouse	MPP SQL data warehouse for large-scale structured analytics.

🔗 Ecosystem Architecture Flow

                ┌────────────────────┐
                │        Hue         │
                │ (Web UI for Hadoop)│
                │ - Run Hive/Impala  │
                │ - Browse HDFS/HBase│
                └─────────┬──────────┘
                          │
          ┌───────────────┼───────────────────┐
          │                                   │
  ┌───────▼─────────┐                 ┌───────▼─────────┐
  │      Hive        │                │     Impala       │
  │ (Batch SQL Engine)│               │ (Fast SQL Engine)│
  │ - ETL / Warehousing│              │ - Interactive BI │
  │ - Works with Oozie │              │                  │
  └────────┬─────────┘                 └────────┬─────────┘
           │                                     │
           │ (Both use the same Hive Metastore)  │
           └────────────────┬────────────────────┘
                            │
                     ┌──────▼───────┐
                     │   Iceberg    │
                     │ Modern Table │
                     │   Format     │
                     │ (ACID, schema│
                     │ evolution)   │
                     └──────┬───────┘
                            │
                    ┌───────▼────────┐
                    │     HDFS       │
                    │ (Data Storage) │
                    └───────┬────────┘
                            │
        ┌───────────────────┼────────────────────────────┐
        │                                           │
 ┌──────▼───────┐                             ┌────────▼─────────┐
 │   PySpark     │                             │     HBase        │
 │ (Compute / ETL)│                            │ (NoSQL, Real-time)│
 │ - Reads/Writes │                            │ - Random R/W      │
 │   to HDFS/Iceberg/Hive                     │ - Built on HDFS   │
 └───────────────┘                             └────────┬─────────┘
                                                       │
                                       ┌───────────────▼─────────────┐
                                       │        Teradata             │
                                       │ Enterprise Data Warehouse   │
                                       │ (Structured large-scale SQL)│
                                       └─────────────────────────────┘

📂 Data Storage Layer — HDFS

HDFS (Hadoop Distributed File System) is the foundation where all raw and processed data is stored.

Stores data as blocks across cluster nodes.
Provides fault tolerance and high throughput.
Other components (Hive, Impala, Spark, HBase) read/write from it.

🧮 Data Processing and Query Engines

🐝 Hive

SQL interface for data stored in HDFS.
Converts queries into MapReduce / Tez / Spark jobs for batch processing.
Ideal for ETL and large-scale aggregations.
Defines table metadata in the Hive Metastore.

⚡ Impala

Real-time SQL query engine.
Reads the same tables defined in Hive Metastore.
Executes queries directly in memory for low-latency analytics.
Best for BI dashboards and interactive analysis.

🔥 Spark / PySpark

Distributed computation engine for batch, streaming, and ML.
PySpark provides a Python API for Spark.
Can read/write from:
- HDFS
- Hive tables
- Iceberg tables
- Parquet/ORC files
Often used for ETL, data transformation, or feature engineering.

🕒 Oozie

Orchestrates complex workflows and schedules recurring jobs in the Hadoop ecosystem.
Coordinates jobs such as Hive scripts, Spark jobs, shell commands, HDFS actions.
Enables dependency management, error-handling, and scheduling (time or data triggers).

🧊 Iceberg — Modern Table Format

Apache Iceberg improves upon Hive’s traditional table format.

✅ Features:

ACID transactions
Schema evolution (add/remove columns safely)
Hidden partitioning
Time travel (query older snapshots)

Integration:

Stored on HDFS or cloud storage.
Accessible via Hive, Spark, Impala, Presto, etc.

🗂 File Formats — Parquet vs ORC

Feature	Parquet	ORC
Type	Columnar	Columnar
Developed By	Twitter & Cloudera	Hortonworks
Optimized For	Spark, Impala, Iceberg	Hive, Spark
Compression	Snappy, GZIP, LZO, ZSTD	ZLIB, Snappy
Read Performance	Excellent for Spark & Impala	Excellent for Hive
Write Performance	Faster	Slower, but better compression
Metadata	Stored per column chunk	Rich stats per stripe (min/max, bloom filters)
File Extension	`.parquet`	`.orc`

Example:

# PySpark Example
df = spark.read.csv("data.csv", header=True)
df.write.parquet("data_parquet/")
df.write.orc("data_orc/")

🧰 HBase — NoSQL Real-time Store

Built on HDFS but optimized for random read/write.
Schema-flexible: stores data as key-value pairs.
Suitable for:
- IoT sensor data
- Time-series logs
- Real-time lookup tables
Accessible via:
- APIs (Java, Python, REST)
- Apache Phoenix for SQL queries
- Hue HBase Browser

🏢 Teradata — Enterprise Data Warehouse

Highly scalable MPP (Massively Parallel Processing) SQL data warehouse for large-scale structured analytics.
Often used as the enterprise “single source of truth” for curated, cleaned data.
Works alongside Hadoop ecosystems:
- Raw data processed in Hadoop → summaries/load to Teradata
- BI tools connect to Teradata for stable reporting.
Architecture highlights:
- Shared-nothing architecture
- Parsing Engine (PE) for SQL queries
- AMP (Access Module Processor) nodes for parallel data processing
- BYNET network for high-speed inter-node communication

🖥️ Hue — The User Interface Layer

Hue (Hadoop User Experience) provides a web-based UI for:

Writing and running Hive or Impala SQL queries.
Browsing and managing HDFS files.
Viewing HBase tables.
Managing Spark jobs and workflows (including Oozie workflows).

⚙️ Example End-to-End Workflow

Data lands in HDFS from logs or ingestion.
Oozie triggers jobs (e.g., nightly at 02:00).
PySpark cleans and transforms the data, writes output to Iceberg/Hive tables.
Results are stored in Parquet or ORC format.
Hive runs scheduled ETL aggregations.
Impala enables interactive analytics on that data.
Teradata receives curated, structured data for enterprise reporting.
HBase serves real-time data lookups or time-series.
Hue is used by analysts/engineers to run queries, monitor jobs, browse data, and visualize results.

🚀 Summary

Layer	Technology	Role
UI	Hue	Web-based UI for querying and data exploration
Schedule	Oozie	Orchestrates and schedules workflows in Hadoop
SQL Engines	Hive / Impala	Batch and interactive SQL processing
Processing	Spark / PySpark	Distributed data processing & ETL
Storage Format	Iceberg	Table management with ACID and schema evolution
File Formats	Parquet / ORC	Compressed columnar storage for analytics
Storage	HDFS	Distributed, fault-tolerant storage layer
NoSQL	HBase	Real-time read/write data store
DW	Teradata	Enterprise data warehouse for large-scale SQL analytics

🧠 Quick Summary in One Line

HDFS stores → Iceberg/Hive organize → Impala/Hive analyze → PySpark processes → HBase serves real‐time → Teradata warehouses curated data → Oozie orchestrates the jobs → Hue visualizes and manages it all.

sany2k8/Big Data Ecosystem Overview.md