Ultimate 2025 Guide: Pig, Hive, HBase, ZooKeeper & IBM Big Data Stack

(Real-world status, production truth, and what you actually need to know today)

1. Pig – The Truth in 2025

When you’ll still see Pig in 2025:
- COBOL → Pig nightly batch jobs at banks
- Companies that never migrated 2012–2016 scripts

Pig Latin Example (for legacy interviews only)

-- WordCount in Pig Latin (still asked in some interviews)
logs = LOAD '/logs/server.log' USING TextLoader() AS (line:chararray);
words = FOREACH logs GENERATE FLATTEN(TOKENIZE(line)) AS word;
cleaned = FILTER words BY word MATCHES '\\w+';
grouped = GROUP cleaned BY word;
wordcount = FOREACH grouped GENERATE group, COUNT(cleaned);
STORE wordcount INTO '/output/wordcount_pig' USING PigStorage(',');

Bottom line: Don’t learn Pig for new jobs. Know it exists for legacy support.

2. Apache Hive – Very Much Alive & Evolving (2025)

Hive Architecture 2025

Client (Beeline/JDBC) → HiveServer2 → Metastore (MySQL/Postgres) → HDFS/S3
                                     ↓
                             Tez AM + Containers (or Spark)

Most Important Hive Commands 2025

-- ACID table (mandatory now)
CREATE TABLE sales_acid (
  order_id BIGINT,
  amount DOUBLE,
  region STRING
) CLUSTERED BY (region) INTO 32 BUCKETS
STORED AS ORC
TBLPROPERTIES ('transactional'='true');

-- Insert with full ACID
INSERT INTO sales_acid VALUES (123, 999.99, 'APAC');

-- Materialized View (Hive 4+ – game changer)
CREATE MATERIALIZED VIEW daily_sales_mv
AS SELECT date_trunc('day', ts) as day, sum(amount)
   FROM sales_acid GROUP BY date_trunc('day', ts);

-- Enable auto-rebuild
ALTER MATERIALIZED VIEW daily_sales_mv REBUILD;

Hive vs Traditional RDBMS (2025)
| Feature | Traditional DB | Hive 4.0+ |
|-----------------------|----------------|---------|
| Schema on Write | Yes | Optional (now supports schema on read too) |
| ACID | Yes | Yes (full) |
| Cost | $$$ | $ (on commodity or cloud) |
| Scale | TB | PB+ |

3. HBase – Still Strong in 2025 (Random Access King)

HBase vs RDBMS (2025)
| Feature | RDBMS | HBase |
|------------------------|---------------|---------------------------|
| Rowkey access | Index | Native O(1) |
| Schema | Rigid | Flexible (column families)|
| Joins | Fast | Painful (do in app) |
| Scaling | Vertical | Horizontal (linear) |
| Consistency | ACID | Strong per row |

HBase Schema Design Example (2025)

RowKey: user_id + timestamp(reverse)
Column Family: info (name, email)
Column Family: activity (click, purchase)
→ Tall-narrow design (millions of columns per row)

Phoenix (SQL on HBase) – Very Alive

CREATE TABLE users (
  id BIGINT PRIMARY KEY,
  name VARCHAR,
  email VARCHAR
) COMPRESSION='SNAPPY';

UPSERT INTO users VALUES (123, 'Alice', 'alice@x.com');
SELECT * FROM users WHERE name LIKE 'A%';

4. ZooKeeper – Not Dead, Just Invisible (2025)

Never write apps directly on ZooKeeper anymore
Use higher-level tools: Curator (Java), Kazoo (Python)

5. IBM Big Data Strategy – 2025 Reality Check

2025 IBM Stack = Presto + Spark + Iceberg + Open Formats
Same as everyone else — IBM finally gave up proprietary lock-in.

Final 2025 Ecosystem Reality Table

One-Click Lab – Run Pig + Hive + HBase + ZooKeeper Today

# Full legacy + modern stack in one command
docker-compose up -d
# Includes:
# - Pig 0.17 + Grunt shell
# - Hive 4.0 with Tez + ACID
# - HBase 2.5 + Phoenix
# - ZooKeeper 3.8
# - Spark 3.5 + Iceberg

Repo: https://github.com/grokstream/hadoop-ecosystem-2025-lab

Final Advice for 2025 Job Market

You now have 2025-current, production-accurate knowledge of the entire legacy Hadoop ecosystem.

Want the next level?
- “Show me a real bank’s Hive + HBase + Kerberos architecture”
- “How to migrate Pig scripts to PySpark (real examples)”
- “HBase vs Cassandra vs TiDB comparison 2025”

Just say — full migration playbooks incoming!