A Coffee Chat Between Data Engineers
Sarah, a curious data engineer, sits down with Alex, a seasoned data architect, at their favorite coffee shop. Sarah has been hearing a lot about open table formats lately and wants to understand what all the fuss is about.
Part 1: Setting the Stage
Sarah: "Alex, I keep hearing about these 'open table formats' - Iceberg, Delta Lake, Hudi. Everyone's talking about them like they're the next big thing. But honestly, I'm confused. We already have Parquet files. Why do we need another layer on top?"
Alex: chuckles "Great question! Let me paint you a picture. Imagine you have a massive library with millions of books, and you're constantly adding new books, updating old ones, and removing outdated ones. Now, how do you keep track of what's where? How do you ensure everyone sees the same version? How do you go back in time to see what the library looked like last week?"
Sarah: "I guess... a catalog system?"
Alex: "Exactly! That's what open table formats are - they're sophisticated catalog and metadata management systems for your data lake. Parquet is like the paper and binding of your books, but these formats are the library management system."
Sarah: "Ah! So they sit on top of file formats like Parquet?"
Alex: "Precisely! Let me break this down properly."
Part 2: Understanding the Problem
Alex: "Before we dive into the solutions, let's talk about what problems these formats solve. Traditional data lakes had several pain points:"
┌─────────────────────────────────────────────────────────┐
│ TRADITIONAL DATA LAKE CHALLENGES │
├─────────────────────────────────────────────────────────┤
│ │
│ 📁 Files Everywhere │
│ → Difficult to track which files belong to a table │
│ → No central metadata management │
│ │
│ 🔄 ACID Nightmares │
│ → No atomicity in operations │
│ → Readers see partial writes │
│ → No rollback capabilities │
│ │
│ 🐌 Slow Metadata Operations │
│ → LIST operations on millions of files │
│ → No partition pruning efficiency │
│ │
│ ⏰ Time Travel? Forget It! │
│ → No version history │
│ → Can't audit changes │
│ │
│ 🔀 Schema Evolution Headaches │
│ → Breaking changes cause failures │
│ → No backward compatibility │
│ │
└─────────────────────────────────────────────────────────┘
Sarah: "Wow, that's a lot of problems! So these table formats solve all of this?"
Alex: "They do! And each has its own approach and strengths. Let me introduce you to the three main contenders."
Part 3: Meet the Contestants
Alex: "Think of this as a competition with three strong contestants, each with unique backgrounds and superpowers."
Contestant Profiles
| Feature | Apache Iceberg 🏔️ | Delta Lake 🔺 | Apache Hudi 🎯 |
|---|---|---|---|
| Born | Netflix (2017) | Databricks (2019) | Uber (2016) |
| Open Source | Yes (Apache) | Yes (Linux Foundation) | Yes (Apache) |
| Primary Focus | Engine independence | Spark optimization | Upserts & incremental processing |
| Best Known For | "Works everywhere" | "Spark's best friend" | "Streaming updates master" |
Sarah: "Interesting! So they came from different companies solving different problems?"
Alex: "Exactly! Netflix needed something that worked across multiple query engines. Databricks wanted to make Spark more powerful. And Uber needed to handle massive streaming updates efficiently."
Part 4: The Core Architecture
Sarah: "Okay, but how do they actually work? What's the magic under the hood?"
Alex: "Great question! They all follow a similar pattern but with different implementations. Let me show you the architectural layers."
General Architecture Pattern
┌─────────────────────────────────────────────────────────┐
│ QUERY ENGINES LAYER │
│ Spark | Flink | Presto | Trino | Athena | etc. │
└────────────────────┬────────────────────────────────────┘
│
┌────────────────────┼────────────────────────────────────┐
│ ▼ │
│ TABLE FORMAT LAYER │
│ (Iceberg / Delta Lake / Hudi) │
│ │
│ ┌─────────────────────────────────────────┐ │
│ │ Metadata Layer │ │
│ │ • Transaction logs │ │
│ │ • Snapshots │ │
│ │ • Schema definitions │ │
│ │ • Partition information │ │
│ └─────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────▼───────────────────────┐ │
│ │ Manifest/File Management │ │
│ │ • File locations │ │
│ │ • Statistics (min/max/count) │ │
│ │ • Data skipping information │ │
│ └─────────────────────────────────────────┘ │
└────────────────────┬────────────────────────────────────┘
│
┌────────────────────▼────────────────────────────────────┐
│ STORAGE LAYER │
│ Parquet | ORC | Avro │
│ (on S3, HDFS, ADLS, GCS) │
└─────────────────────────────────────────────────────────┘
Sarah: "So they're essentially a metadata and management layer between the query engine and the actual data files?"
Alex: "Bingo! Now let's look at how each one implements this differently."
Part 5: Iceberg - The Universal Adapter
Sarah: "Let's start with Iceberg. Why do they call it the 'universal adapter'?"
Alex: "Iceberg was designed from day one to be engine-agnostic. Netflix runs multiple query engines - Spark, Trino, Presto, Flink - and they needed something that worked seamlessly with all of them."
Iceberg Architecture Deep Dive
ICEBERG TABLE STRUCTURE
─────────────────────────
Metadata File (JSON)
│
┌──────────┼──────────┐
│ │ │
Snapshot 1 Snapshot 2 Snapshot 3 (Version History)
│ │ │
└──────────┴──────────┴──────────┐
│
Manifest List (Avro)
│
┌────────────────────┼────────────────────┐
│ │ │
Manifest File 1 Manifest File 2 Manifest File 3
(Avro - List of (Avro - List of (Avro - List of
data files) data files) data files)
│ │ │
└────────────────────┴────────────────────┘
│
Data Files (Parquet/ORC/Avro)
Alex: "What's brilliant about Iceberg is its three-level metadata structure:"
| Level | Component | Purpose | Format |
|---|---|---|---|
| Level 1 | Metadata File | Points to current snapshot, schema, partitioning | JSON |
| Level 2 | Manifest List | Lists all manifest files for a snapshot | Avro |
| Level 3 | Manifest Files | Lists actual data files with statistics | Avro |
Sarah: "Why so many levels? Isn't that complex?"
Alex: "It seems complex, but it's genius! This structure enables incredible performance optimizations:"
Iceberg's Key Innovations
╔══════════════════════════════════════════════════════════╗
║ ICEBERG SUPERPOWERS ║
╠══════════════════════════════════════════════════════════╣
║ ║
║ 1. 🎯 Hidden Partitioning ║
║ Users don't need to know about partitioning! ║
║ ───────────────────────────────────────────── ║
║ SELECT * FROM events WHERE date = '2024-01-01' ║
║ ║
║ Iceberg automatically uses partition pruning, ║
║ even if users don't specify partition columns! ║
║ ║
║ 2. 🔄 Partition Evolution ║
║ Change partitioning without rewriting data ║
║ ───────────────────────────────────────────── ║
║ Day 1: Partition by day ║
║ Day 100: Change to partition by hour ║
║ → Old data stays as-is, new schema applies forward ║
║ ║
║ 3. 📊 Column-level Statistics ║
║ Min/Max/Null count for every column in manifest ║
║ → Aggressive data skipping ║
║ ║
║ 4. ⚡ Metadata-only Operations ║
║ Many operations don't touch data files ║
║ → Rolling back = changing metadata pointer ║
║ → Fast snapshot creation ║
║ ║
║ 5. 🔧 Schema Evolution ║
║ Add, drop, rename columns safely ║
║ → Uses unique column IDs (not names) ║
║ ║
╚══════════════════════════════════════════════════════════╝
Sarah: "Wait, hidden partitioning? How does that work?"
Alex: "In traditional systems, you'd write: SELECT * FROM events WHERE date_partition = '2024-01-01'. You need to know the partition column. With Iceberg, you just write: SELECT * FROM events WHERE event_date = '2024-01-01', and Iceberg figures out the partitioning behind the scenes. It maps your filter column to the partition specification automatically!"
Sarah: "That's actually really cool! What about the snapshot mechanism?"
Alex: "Snapshots are Iceberg's way of implementing time travel and ACID. Each write operation creates a new snapshot. Let me show you:"
Iceberg Snapshot Example
Timeline of Operations:
────────────────────────
T1: Initial Load (1000 records)
Snapshot-1 → Manifest-List-A → Data-File-001.parquet
T2: Insert 500 new records
Snapshot-2 → Manifest-List-B → Data-File-001.parquet (existing)
→ Data-File-002.parquet (new)
T3: Delete 100 records
Snapshot-3 → Manifest-List-C → Data-File-001.parquet (partial)
→ Data-File-002.parquet (partial)
→ Data-File-003.parquet (new, with deletes)
T4: Schema change: Add column "user_agent"
Snapshot-4 → Manifest-List-D → All previous files
→ Data-File-004.parquet (with new schema)
Time Travel Query:
──────────────────
SELECT * FROM events.snapshots
┌─────────────┬────────────────────┬──────────┬───────────┐
│ Snapshot ID │ Timestamp │ Operation│ Files │
├─────────────┼────────────────────┼──────────┼───────────┤
│ 1 │ 2024-01-01 10:00 │ append │ 1 │
│ 2 │ 2024-01-01 11:00 │ append │ 2 │
│ 3 │ 2024-01-01 12:00 │ delete │ 3 │
│ 4 │ 2024-01-01 13:00 │ append │ 4 │
└─────────────┴────────────────────┴──────────┴───────────┘
Query as of timestamp:
SELECT * FROM events TIMESTAMP AS OF '2024-01-01 11:30'
→ Returns data from Snapshot-2
Part 6: Delta Lake - The Spark Powerhouse
Sarah: "Okay, Iceberg sounds impressive. What about Delta Lake? Why would I choose it?"
Alex: "Delta Lake is the darling of the Spark ecosystem. If you're heavily invested in Spark and especially Databricks, Delta Lake offers some compelling advantages."
Delta Lake Architecture
DELTA LAKE STRUCTURE
────────────────────
Delta Table
│
┌────────────┴────────────┐
│ │
Transaction Log Data Files
(_delta_log/) (Parquet)
│
├─── 00000000000.json ← Initial commit
├─── 00000000001.json ← Second commit
├─── 00000000002.json ← Third commit
├─── ...
└─── 00000000010.checkpoint.parquet ← Checkpoint
Each JSON entry contains:
{
"add": {
"path": "part-00000.parquet",
"partitionValues": {"date": "2024-01-01"},
"size": 12345,
"stats": "{min: ..., max: ...}"
}
}
Sarah: "It looks simpler than Iceberg. Just JSON log files?"
Alex: "Yes! Delta Lake uses a transaction log that's essentially an append-only sequence of JSON files. It's beautifully simple and effective."
Delta Lake's Key Features
| Feature | Description | Advantage |
|---|---|---|
| Transaction Log | Ordered, append-only log of operations | Simple, reliable ACID |
| Optimistic Concurrency | Multiple writers can work simultaneously | Better for high-write scenarios |
| Checkpoints | Periodic snapshots of table state | Fast startup and metadata reads |
| Z-Ordering | Multi-dimensional clustering | Excellent query performance |
| Dynamic File Pruning | Runtime file skipping in joins | Spark-optimized performance |
Sarah: "What's this Z-Ordering thing?"
Alex: "Ah, one of Delta Lake's coolest features! Let me explain with a visual:"
Understanding Z-Ordering
WITHOUT Z-ORDERING:
───────────────────
Data organized by just one column (e.g., date):
File 1: date=2024-01-01, country=[US, UK, FR, DE, JP]
File 2: date=2024-01-02, country=[US, UK, FR, DE, JP]
File 3: date=2024-01-03, country=[US, UK, FR, DE, JP]
Query: SELECT * FROM events WHERE date='2024-01-02' AND country='US'
→ Must scan entire File 2 (even though only 20% is US data)
WITH Z-ORDERING:
────────────────
Data co-located by multiple columns (date + country):
File 1: date=2024-01-01, country=[US, US, US, US, US]
File 2: date=2024-01-01, country=[UK, UK, UK, UK, UK]
File 3: date=2024-01-02, country=[US, US, US, US, US]
File 4: date=2024-01-02, country=[UK, UK, UK, UK, UK]
Same Query: SELECT * FROM events WHERE date='2024-01-02' AND country='US'
→ Only scans File 3 (precisely targeted!)
Command:
OPTIMIZE events ZORDER BY (date, country)
Sarah: "So it's like multi-dimensional partitioning without creating actual partitions?"
Alex: "Exactly! It clusters related data together. Perfect for queries that filter on multiple columns."
Delta Lake Exclusive Features
╔══════════════════════════════════════════════════════════╗
║ DELTA LAKE SUPERPOWERS ║
╠══════════════════════════════════════════════════════════╣
║ ║
║ 1. 🚀 MERGE (Upsert) Operations ║
║ Highly optimized for Spark ║
║ ───────────────────────────────────────────── ║
║ MERGE INTO target ║
║ USING source ║
║ ON target.id = source.id ║
║ WHEN MATCHED THEN UPDATE SET * ║
║ WHEN NOT MATCHED THEN INSERT * ║
║ ║
║ 2. 🔄 Stream-Static Joins ║
║ Streaming data joins with Delta tables ║
║ → Auto file tracking ║
║ → Incremental processing ║
║ ║
║ 3. 🎯 Delta Live Tables (DLT) ║
║ Declarative ETL framework ║
║ → Auto data quality ║
║ → Pipeline observability ║
║ ║
║ 4. 📝 Change Data Feed (CDF) ║
║ Track row-level changes ║
║ → Know what changed, when, and how ║
║ ║
║ 5. ⚡ Liquid Clustering (Preview) ║
║ Automatic data clustering ║
║ → No manual OPTIMIZE needed ║
║ ║
╚══════════════════════════════════════════════════════════╝
Sarah: "What's this Change Data Feed? Sounds useful!"
Alex: "It's fantastic for CDC scenarios! Let me show you:"
Change Data Feed Example
-- Enable CDF on table
CREATE TABLE users (
id INT,
name STRING,
email STRING
) USING DELTA
TBLPROPERTIES (delta.enableChangeDataFeed = true)
-- Make changes
INSERT INTO users VALUES (1, 'Alice', 'alice@email.com')
UPDATE users SET email = 'alice_new@email.com' WHERE id = 1
DELETE FROM users WHERE id = 1
-- Query changes
SELECT * FROM table_changes('users', 0, 10)
Result:
┌────┬───────┬────────────────────┬─────────────┬────────────┐
│ id │ name │ email │ _change_type│ _commit_ver│
├────┼───────┼────────────────────┼─────────────┼────────────┤
│ 1 │ Alice │ alice@email.com │ insert │ 1 │
│ 1 │ Alice │ alice@email.com │ update_pre │ 2 │
│ 1 │ Alice │ alice_new@email.com│ update_post │ 2 │
│ 1 │ Alice │ alice_new@email.com│ delete │ 3 │
└────┴───────┴────────────────────┴─────────────┴────────────┘
Part 7: Apache Hudi - The Streaming Specialist
Sarah: "And what about Hudi? What's its claim to fame?"
Alex: "Hudi came from Uber's need to handle massive real-time data updates. Think about it - Uber has millions of trips updating constantly. Hudi was built for exactly this: incremental processing and fast upserts."
Hudi Architecture
HUDI TABLE STRUCTURE
────────────────────
Hudi Table
│
┌──────────────┼──────────────┐
│ │ │
Timeline Metadata Files Data Files
(.hoodie/) │ │
│ │ │
├─ Commits │ ┌───┴────┐
├─ Cleans │ │ │
├─ Rollbacks │ Base Files Log Files
└─ Restores │ (Parquet) (Avro)
│ │ │
│ COW Table MOR Table
│ (Copy on (Merge on
│ Write) Read)
Sarah: "What's COW and MOR?"
Alex: "Great catch! That's Hudi's secret sauce. Hudi offers two table types, each optimized for different use cases:"
Copy-on-Write (COW) vs. Merge-on-Read (MOR)
┌─────────────────────────────────────────────────────────────┐
│ COPY-ON-WRITE (COW) │
├─────────────────────────────────────────────────────────────┤
│ │
│ Write Process: │
│ ───────────── │
│ 1. Read existing file │
│ 2. Merge with updates │
│ 3. Write new file (entire file rewritten) │
│ 4. Mark old file for cleanup │
│ │
│ Timeline: │
│ [File-A-v1.parquet] → UPDATE → [File-A-v2.parquet] │
│ │
│ ✅ Advantages: │
│ • Faster reads (just scan parquet) │
│ • Simpler query logic │
│ • Better for read-heavy workloads │
│ │
│ ❌ Disadvantages: │
│ • Slower writes (rewrite entire file) │
│ • Higher write amplification │
│ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ MERGE-ON-READ (MOR) │
├─────────────────────────────────────────────────────────────┤
│ │
│ Write Process: │
│ ───────────── │
│ 1. Append updates to log file (delta file) │
│ 2. Keep base file unchanged │
│ 3. Periodically compact logs into base │
│ │
│ Timeline: │
│ [Base.parquet] + [delta-1.log] + [delta-2.log] │
│ ↓ │
│ (After Compaction) │
│ ↓ │
│ [New-Base.parquet] │
│ │
│ ✅ Advantages: │
│ • Faster writes (append-only) │
│ • Lower write amplification │
│ • Better for write-heavy workloads │
│ • Near real-time data availability │
│ │
│ ❌ Disadvantages: │
│ • Slower reads (merge base + logs) │
│ • More complex query logic │
│ • Requires compaction management │
│ │
└─────────────────────────────────────────────────────────────┘
Sarah: "So if I'm doing real-time analytics with lots of updates, I'd use MOR?"
Alex: "Exactly! MOR is perfect for streaming scenarios. Let me show you a real example:"
Hudi Streaming Use Case
UBER RIDE TRACKING EXAMPLE
──────────────────────────
Scenario: Track rides in real-time
Updates every few seconds: ride status, location, fare
With Hudi MOR:
──────────────
T1 (10:00:00): Ride starts
Base: [ride_123 | status=started | fare=0.00]
T2 (10:00:05): Location update
Base: [ride_123 | status=started | fare=0.00]
Log: [ride_123 | location=update_1] ← Fast append
T3 (10:00:10): Fare update
Base: [ride_123 | status=started | fare=0.00]
Log: [ride_123 | location=update_1]
[ride_123 | fare=5.50] ← Fast append
T4 (10:00:15): Status update
Base: [ride_123 | status=started | fare=0.00]
Log: [ride_123 | location=update_1]
[ride_123 | fare=5.50]
[ride_123 | status=in_progress] ← Fast append
T5 (10:05:00): Compaction runs
New Base: [ride_123 | status=in_progress | fare=5.50 | location=update_1]
Log: (cleared)
Real-time query at T3:
→ Reads base + merges log entries
→ Returns: ride_123 | status=started | fare=5.50 | location=update_1
Hudi's Unique Features
╔══════════════════════════════════════════════════════════╗
║ APACHE HUDI SUPERPOWERS ║
╠══════════════════════════════════════════════════════════╣
║ ║
║ 1. 📊 Incremental Queries ║
║ Process only changed data ║
║ ───────────────────────────────────────────── ║
║ SELECT * FROM trips ║
║ WHERE _hoodie_commit_time > 'last_processed' ║
║ → Perfect for incremental ETL ║
║ ║
║ 2. 🎯 Indexing Options ║
║ Multiple index types for upserts ║
║ ───────────────────────────────────────────── ║
║ • Bloom Filter Index (default) ║
║ • Simple Index ║
║ • HBase Index ║
║ • Bucket Index ║
║ → Find update target files quickly ║
║ ║
║ 3. 🔄 Multi-Modal Indexes ║
║ Different read/write optimization ║
║ ───────────────────────────────────────────── ║
║ • Read-optimized view (base files only) ║
║ • Real-time view (base + logs) ║
║ → Choose speed vs. freshness ║
║ ║
║ 4. 🧹 Automated Cleanup ║
║ Automatic file management ║
║ ───────────────────────────────────────────── ║
║ • Cleaner removes old file versions ║
║ • Archive timeline to reduce metadata ║
║ → Configurable retention ║
║ ║
║ 5. 📐 Clustering ║
║ Optimize data layout automatically ║
║ ───────────────────────────────────────────── ║
║ • Z-curve clustering ║
║ • Hilbert curve clustering ║
║ → Better query performance ║
║ ║
╚══════════════════════════════════════════════════════════╝
Sarah: "This incremental query thing sounds powerful. Can you show me more?"
Alex: "Sure! This is one of Hudi's killer features for data pipelines:"
Incremental Processing Example
-- Setup
CREATE TABLE raw_events USING HUDI
OPTIONS (
type = 'mor',
primaryKey = 'event_id',
preCombineField = 'ts'
)
-- Batch 1: Load initial data (1000 events)
INSERT INTO raw_events SELECT * FROM source WHERE date = '2024-01-01'
-- Commit Time: 20240101100000
-- Batch 2: Load new data (500 events)
INSERT INTO raw_events SELECT * FROM source WHERE date = '2024-01-02'
-- Commit Time: 20240102100000
-- Batch 3: Load and update data (300 new, 200 updates)
MERGE INTO raw_events...
-- Commit Time: 20240103100000
-- INCREMENTAL QUERY (Read only changes since last run)
SELECT * FROM hudi_table
WHERE _hoodie_commit_time > '20240101100000'
Results:
┌──────────┬─────────────────┬──────────────┬─────────────────┐
│ event_id │ _hoodie_commit │ operation │ rows │
├──────────┼─────────────────┼──────────────┼─────────────────┤
│ ... │ 20240102100000 │ insert │ 500 │
│ ... │ 20240103100000 │ insert │ 300 │
│ ... │ 20240103100000 │ update │ 200 │
└──────────┴─────────────────┴──────────────┴─────────────────┘
Total: Only 1000 rows processed (not all 1800!)
Part 8: The Grand Comparison
Sarah: "Okay, my head is spinning with all these features! Can you give me a side-by-side comparison?"
Alex: "Absolutely! Let me break it down across different dimensions."
Comprehensive Feature Comparison
┌────────────────────────────────────────────────────────────────────────────┐
│ FEATURE COMPARISON MATRIX │
├──────────────────────┬───────────────┬──────────────┬─────────────────────┤
│ Feature │ Iceberg 🏔️ │ Delta Lake 🔺│ Hudi 🎯 │
├──────────────────────┼───────────────┼──────────────┼─────────────────────┤
│ ACID Transactions │ ✅ Full │ ✅ Full │ ✅ Full │
│ Time Travel │ ✅ Excellent │ ✅ Excellent │ ✅ Good │
│ Schema Evolution │ ✅ Excellent │ ✅ Good │ ✅ Good │
│ Partition Evolution │ ✅ Yes │ ❌ No │ ❌ No │
│ Hidden Partitioning │ ✅ Yes │ ❌ No │ ❌ No │
│ Streaming Upserts │ ⚠️ Good │ ✅ Excellent │ ✅ Excellent │
│ Incremental Queries │ ⚠️ Possible │ ⚠️ Via CDF │ ✅ Native │
│ Multi-Engine Support │ ✅ Excellent │ ⚠️ Growing │ ⚠️ Growing │
│ Write Performance │ ⚡ Good │ ⚡⚡ Better │ ⚡⚡⚡ Best │
│ Read Performance │ ⚡⚡ Better │ ⚡⚡ Better │ ⚡ Good (MOR) │
│ │ │ │ ⚡⚡ Better (COW) │
│ Small File Handling │ ⚠️ Manual │ ✅ OPTIMIZE │ ✅ Clustering │
│ Compaction │ ⚠️ Manual │ ✅ OPTIMIZE │ ✅ Automatic │
│ Row-level Updates │ ✅ Yes │ ✅ Yes │ ✅ Yes │
│ Row-level Deletes │ ✅ Yes │ ✅ Yes │ ✅ Yes │
│ Data Skipping │ ✅ Excellent │ ✅ Excellent │ ✅ Good │
│ Metadata Overhead │ 📦 Medium │ 📦 Low │ 📦📦 Higher │
│ Community Size │ 👥 Large │ 👥👥 Larger │ 👥 Medium │
│ Learning Curve │ 📚 Moderate │ 📚 Easy │ 📚📚 Steeper │
└──────────────────────┴───────────────┴──────────────┴─────────────────────┘
Engine Compatibility Matrix
| Query Engine | Iceberg | Delta Lake | Hudi |
|---|---|---|---|
| Apache Spark | ✅ Full | ✅ Full (optimized) | ✅ Full |
| Apache Flink | ✅ Full | ⚠️ Limited | ✅ Full |
| Trino/Presto | ✅ Full | ✅ Growing | ⚠️ Limited |
| AWS Athena | ✅ Native | ⚠️ Via manifest | ✅ Native |
| Snowflake | ✅ External tables | ⚠️ Via UniForm | ❌ No |
| BigQuery | ✅ External tables | ❌ No | ❌ No |
| Dremio | ✅ Native | ✅ Via reflection | ⚠️ Limited |
| StarRocks | ✅ Full | ⚠️ Limited | ⚠️ Limited |
Sarah: "This is super helpful! But which one should I choose for my project?"
Part 9: Decision Framework
Alex: "Great question! Let me give you a decision tree based on your use case."
Decision Tree
START: Choose Your Table Format
│
┌─────────────┴─────────────┐
│ │
Are you all-in on Spark/ Need multi-engine
Databricks? support?
│ │
┌───────┴───────┐ │
YES NO │
│ │ │
┌───────▼──────┐ │ │
│ DELTA LAKE 🔺│ │ │
│ │ │ ┌─────▼──────┐
│ Reasons: │ │ │ Check Hudi │
│ • Best Spark │ │ │ use case? │
│ integration│ │ └─────┬──────┘
│ • DLT support│ │ │
│ • Unity Catalog │ ┌────────────┴────────────┐
│ • Z-Ordering │ │ Lots of upserts/ General-purpose
│ • Liquid │ │ streaming updates? analytics?
│ Clustering │ │ │ │
└──────────────┘ │ YES NO
│ │ │
│ ┌─────▼──────┐ ┌─────▼──────┐
│ │ HUDI 🎯 │ │ ICEBERG 🏔️│
│ │ │ │ │
│ │ Reasons: │ │ Reasons: │
│ │ • MOR mode │ │ • Hidden │
│ │ • Incremental │ • Partition │
│ │ queries │ │ Evolution│
│ │ • CDC/streaming │ • Multi- │
│ │ • Indexing │ │ engine │
│ │ options │ │ • Netflix │
│ └────────────┘ │ battle- │
│ │ tested │
└─────► Consider └────────────┘
your team's
expertise
Use Case Recommendations
╔══════════════════════════════════════════════════════════════╗
║ WHEN TO USE WHAT ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ 🏔️ CHOOSE ICEBERG WHEN: ║
║ ────────────────────────── ║
║ ✓ You use multiple query engines (Spark, Flink, Trino) ║
║ ✓ You need true vendor independence ║
║ ✓ Partition evolution is important ║
║ ✓ You want hidden partitioning for user simplicity ║
║ ✓ Your team values clean architecture ║
║ ✓ You're on AWS and use Athena heavily ║
║ ✓ You need future-proof format (most open) ║
║ ║
║ Example: Multi-cloud analytics, data mesh architecture ║
║ ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ 🔺 CHOOSE DELTA LAKE WHEN: ║
║ ──────────────────────── ║
║ ✓ You're primarily using Spark (90%+ workload) ║
║ ✓ You're on Databricks platform ║
║ ✓ You want Delta Live Tables for ETL ║
║ ✓ You need Unity Catalog integration ║
║ ✓ Z-Ordering benefits your query patterns ║
║ ✓ You value extensive Spark optimizations ║
║ ✓ You want strong streaming + batch integration ║
║ ║
║ Example: Databricks-centric lakehouse, medallion arch ║
║ ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ 🎯 CHOOSE HUDI WHEN: ║
║ ────────────────── ║
║ ✓ You have high-frequency updates (CDC, streaming) ║
║ ✓ You need near real-time analytics (minute latency) ║
║ ✓ You want efficient incremental processing ║
║ ✓ Your data has clear primary keys ║
║ ✓ Write performance is critical ║
║ ✓ You need both streaming and batch on same table ║
║ ✓ You can manage compaction workflows ║
║ ║
║ Example: IoT, ride-sharing, real-time recommendations ║
║ ║
╚══════════════════════════════════════════════════════════════╝
Sarah: "This is crystal clear! Can you give me some real-world examples?"
Part 10: Real-World Scenarios
Alex: "Absolutely! Let me walk you through some real scenarios I've seen in the field."
Scenario 1: E-commerce Company
🛒 COMPANY: Global E-commerce Platform
📊 DATA VOLUME: 100TB+, 50 million orders/day
🎯 REQUIREMENTS:
• Real-time inventory updates
• Customer analytics
• Order tracking with updates
• Multi-region queries
SOLUTION: Apache Hudi (MOR)
ARCHITECTURE:
─────────────
Kafka Stream → Hudi Upserts (Order Updates)
↓
Orders Table (MOR)
├─ Real-time view: Latest order status
└─ Read-optimized: Analytical queries
Example Flow:
1. Order placed → Insert (instant)
2. Payment confirmed → Update status (instant)
3. Shipped → Update status (instant)
4. Delivered → Update status + add rating (instant)
Query Patterns:
• Real-time: Customer order status (sub-second)
• Analytics: Daily sales reports (minutes)
• Incremental: Process only today's changes for ML
WHY HUDI?
─────────
✓ Fast upserts for order status changes
✓ MOR mode for instant visibility
✓ Incremental queries for efficient ETL
✓ Separate read-optimized view for analytics
Scenario 2: Financial Services
🏦 COMPANY: Investment Bank
📊 DATA VOLUME: 500TB+, regulatory compliance
🎯 REQUIREMENTS:
• Time travel for auditing (7 years)
• Multi-team access (different engines)
• Schema must evolve safely
• Quarterly partition → daily partition change
SOLUTION: Apache Iceberg
ARCHITECTURE:
─────────────
Various Sources → Iceberg Tables
├─ Spark for ETL
├─ Trino for Analytics
├─ Flink for Streaming
└─ Athena for Ad-hoc queries
Example:
Create transaction_data table:
• 2020-2023: Partitioned by quarter
• 2024+: Partitioned by day (without rewrite!)
Time Travel Query:
SELECT * FROM transactions
FOR SYSTEM_TIME AS OF '2023-12-31'
WHERE account_id = '12345'
Audit Trail:
SELECT * FROM transactions.snapshots
WHERE committed_at > '2023-01-01'
WHY ICEBERG?
────────────
✓ Partition evolution (quarter → day)
✓ Multi-engine support (4+ engines)
✓ Strong time travel for compliance
✓ Hidden partitioning (users don't need to know)
✓ Robust snapshot isolation
Scenario 3: SaaS Analytics Platform
☁️ COMPANY: SaaS Analytics Provider
📊 DATA VOLUME: 200TB, multi-tenant
🎯 REQUIREMENTS:
• Databricks-native development
• Delta Live Tables for ETL
• Z-Order for multi-column filters
• Unity Catalog for governance
SOLUTION: Delta Lake
ARCHITECTURE:
─────────────
Bronze Layer (Raw Data)
↓
Silver Layer (Cleaned + DLT)
↓
Gold Layer (Aggregated + Z-Ordered)
↓
Dashboards (Query by tenant + date + feature)
Delta Live Tables Pipeline:
@dlt.table
def bronze_events():
return spark.readStream.format("kafka")...
@dlt.table
def silver_events():
return dlt.read_stream("bronze_events")
.filter("quality_score > 0.8")
@dlt.table
def gold_metrics():
return dlt.read("silver_events")
.groupBy("tenant", "date", "metric")
Optimization:
OPTIMIZE gold_metrics
ZORDER BY (tenant_id, date, metric_type)
WHY DELTA LAKE?
───────────────
✓ Deep Databricks integration
✓ Delta Live Tables declarative ETL
✓ Z-Ordering for multi-tenant queries
✓ Unity Catalog for data governance
✓ Photon optimization for Spark
✓ Liquid Clustering (upcoming)
Part 11: Performance Deep Dive
Sarah: "These examples are great! But what about actual performance numbers? How do they compare?"
Alex: "Good question! Performance depends heavily on workload, but let me show you some benchmark patterns."
Performance Comparison by Workload
╔══════════════════════════════════════════════════════════════╗
║ WRITE PERFORMANCE (Normalized) ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ Bulk Insert (1M rows, first write): ║
║ ────────────────────────────────────── ║
║ Iceberg: ████████████░░ 100 sec (Baseline) ║
║ Delta: ██████████░░░░ 85 sec (15% faster) ║
║ Hudi (COW): ████████████████ 120 sec (20% slower) ║
║ Hudi (MOR): ████████░░░░░░ 70 sec (30% faster) ║
║ ║
║ Update 10K rows (out of 1M): ║
║ ──────────────────────────── ║
║ Iceberg: ████████████████ 45 sec ║
║ Delta: ████████████░░ 35 sec ║
║ Hudi (COW): ████████████████ 48 sec ║
║ Hudi (MOR): ████░░░░░░░░░░ 8 sec ⚡ (Winner) ║
║ ║
║ Streaming Upserts (1K rows/sec sustained): ║
║ ────────────────────────────────────────── ║
║ Iceberg: ██████░░░░░░░░ Possible, not optimized ║
║ Delta: ████████████░░ Good with Streaming API ║
║ Hudi (MOR): ████████████████ Excellent ⚡ (Winner) ║
║ ║
╚══════════════════════════════════════════════════════════════╝
╔══════════════════════════════════════════════════════════════╗
║ READ PERFORMANCE (Normalized) ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ Full Table Scan (1M rows): ║
║ ────────────────────────── ║
║ Iceberg: ████████████░░ 20 sec ║
║ Delta: ████████████░░ 20 sec ║
║ Hudi (COW): ████████████░░ 21 sec ║
║ Hudi (MOR): ████████████████████ 35 sec (merge overhead) ║
║ ║
║ Selective Query (WHERE date = 'X' AND country = 'Y'): ║
║ ────────────────────────────────────────────────────── ║
║ Iceberg: ████░░░░░░░░░░ 2 sec (partition pruning) ║
║ Delta: ██░░░░░░░░░░░░ 1 sec (Z-Order) ⚡ (Winner) ║
║ Hudi (COW): ████░░░░░░░░░░ 2.5 sec ║
║ Hudi (MOR): ██████░░░░░░░░ 3.5 sec (merge overhead) ║
║ ║
║ Time Travel (Query old snapshot): ║
║ ────────────────────────────────── ║
║ Iceberg: ██░░░░░░░░░░░░ 0.5 sec ⚡ (metadata only) ║
║ Delta: ██░░░░░░░░░░░░ 0.6 sec (log replay) ║
║ Hudi: ████░░░░░░░░░░ 2 sec (reconstruct state) ║
║ ║
║ Incremental Query (changes since last run): ║
║ ────────────────────────────────────────── ║
║ Iceberg: ████████░░░░░░ 5 sec (scan snapshots) ║
║ Delta: ████████░░░░░░ 5 sec (CDF query) ║
║ Hudi: ██░░░░░░░░░░░░ 1 sec ⚡ (native support) ║
║ ║
╚══════════════════════════════════════════════════════════════╝
Sarah: "So it really depends on the workload! Hudi MOR wins for writes, Delta wins for reads with Z-Order, and Iceberg is balanced?"
Alex: "Exactly! There's no universal 'best'. It's about matching the format to your needs."
Part 12: Migration and Interoperability
Sarah: "What if we pick the wrong one? Can we switch later?"
Alex: "Great forward thinking! Yes, but it requires planning. Let me show you the migration paths."
Migration Paths
┌─────────────────────────────────────────────────────────────┐
│ TABLE FORMAT MIGRATIONS │
├─────────────────────────────────────────────────────────────┤
│ │
│ ICEBERG ←→ DELTA LAKE │
│ ───────────────────── │
│ Tool: Databricks UniForm │
│ Status: Production-ready │
│ │
│ Write in Delta → Read as Iceberg │
│ ✓ Automatic metadata sync │
│ ✓ No data duplication │
│ ✓ Time travel works │
│ ⚠ One-way initially (Delta is source of truth) │
│ │
│ Example: │
│ CREATE TABLE events (...) │
│ USING DELTA │
│ TBLPROPERTIES ( │
│ 'delta.universalFormat.enabledFormats' = 'iceberg' │
│ ) │
│ │
├─────────────────────────────────────────────────────────────┤
│ │
│ HUDI → ICEBERG/DELTA │
│ ─────────────────── │
│ Tool: Manual migration or XTable │
│ Status: Requires custom process │
│ │
│ Approach: │
│ 1. Export Hudi data to Parquet │
│ 2. Create new Iceberg/Delta table │
│ 3. Load data with schema │
│ 4. Cutover reads/writes │
│ │
│ Note: Loses commit history │
│ │
├─────────────────────────────────────────────────────────────┤
│ │
│ PARQUET → ANY FORMAT │
│ ─────────────────── │
│ Tool: Native format tools │
│ Status: Straightforward │
│ │
│ Iceberg: CALL catalog.system.migrate('table_name') │
│ Delta: CONVERT TO DELTA parquet.`/path/to/table` │
│ Hudi: deltastreamer --source-class ParquetSource │
│ │
└─────────────────────────────────────────────────────────────┘
Universal Table Formats (Future)
Alex: "There's also an emerging project called XTable (formerly OneTable) that aims to support multi-format writes."
XTable Architecture:
────────────────────
Single Source Table
│
┌──────┼──────┐
│ │ │
Iceberg Delta Hudi
Metadata Metadata Metadata
│ │ │
└──────┴──────┘
│
Shared Data Files
(Parquet/ORC)
Benefits:
• Write once, read in any format
• No data duplication
• Format flexibility
• Gradual migration path
Status: Incubating project
Part 13: Best Practices
Sarah: "Alright, I'm convinced each has its place. What are the best practices for actually using these in production?"
Alex: "Excellent question! Let me share some battle-tested practices for each format."
Universal Best Practices
╔══════════════════════════════════════════════════════════════╗
║ BEST PRACTICES (ALL FORMATS) ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ 1. 📏 FILE SIZING ║
║ ───────────── ║
║ ✓ Target: 128MB - 512MB per file ║
║ ✓ Avoid: Millions of small files ║
║ ✓ Avoid: Few huge files (>1GB) ║
║ ║
║ 2. 🗂️ PARTITIONING STRATEGY ║
║ ────────────────────── ║
║ ✓ Partition on high-cardinality filter columns ║
║ ✓ Aim for 10-1000 partitions (not millions) ║
║ ✓ Consider query patterns first ║
║ ✗ Don't partition on high-cardinality columns ║
║ (e.g., user_id with millions of users) ║
║ ║
║ 3. 🔧 MAINTENANCE ║
║ ──────────── ║
║ ✓ Schedule regular compaction ║
║ ✓ Monitor metadata size ║
║ ✓ Set retention policies ║
║ ✓ Clean up old snapshots/commits ║
║ ║
║ 4. 📊 MONITORING ║
║ ─────────── ║
║ ✓ Track table size ║
║ ✓ Monitor snapshot/commit growth ║
║ ✓ Watch query performance trends ║
║ ✓ Alert on metadata bloat ║
║ ║
║ 5. 🔒 GOVERNANCE ║
║ ─────────── ║
║ ✓ Document table ownership ║
║ ✓ Define schema change process ║
║ ✓ Implement access controls ║
║ ✓ Maintain data dictionaries ║
║ ║
╚══════════════════════════════════════════════════════════════╝
Format-Specific Best Practices
Iceberg Best Practices:
-- 1. Use appropriate file formats
CREATE TABLE events (
event_id BIGINT,
timestamp TIMESTAMP,
data STRING
)
USING iceberg
PARTITIONED BY (days(timestamp)) -- Hidden partitioning!
TBLPROPERTIES (
'write.format.default' = 'parquet',
'write.parquet.compression-codec' = 'zstd',
'write.target-file-size-bytes' = '134217728' -- 128MB
);
-- 2. Expire old snapshots regularly
CALL catalog.system.expire_snapshots(
table => 'events',
older_than => TIMESTAMP '2024-01-01 00:00:00',
retain_last => 100
);
-- 3. Remove orphan files
CALL catalog.system.remove_orphan_files(
table => 'events',
older_than => TIMESTAMP '2024-01-01 00:00:00'
);
-- 4. Rewrite manifests for better performance
CALL catalog.system.rewrite_manifests('events');
-- 5. Use partition evolution when needed
ALTER TABLE events
SET PARTITION SPEC (hours(timestamp)); -- Change from days to hours
Delta Lake Best Practices:
-- 1. Enable important features
CREATE TABLE events (
event_id BIGINT,
timestamp TIMESTAMP,
data STRING
)
USING DELTA
PARTITIONED BY (date)
TBLPROPERTIES (
'delta.enableChangeDataFeed' = 'true',
'delta.autoOptimize.optimizeWrite' = 'true',
'delta.autoOptimize.autoCompact' = 'true',
'delta.deletedFileRetentionDuration' = 'interval 7 days'
);
-- 2. Regular OPTIMIZE with Z-ORDER
OPTIMIZE events
ZORDER BY (user_id, event_type);
-- 3. Vacuum old files
VACUUM events RETAIN 168 HOURS; -- 7 days
-- 4. Analyze table for statistics
ANALYZE TABLE events COMPUTE STATISTICS FOR ALL COLUMNS;
-- 5. Use OPTIMIZE WRITE for streaming
spark.conf.set("spark.databricks.delta.optimizeWrite.enabled", "true")
Hudi Best Practices:
-- 1. Configure appropriate table type and options
val hudiOptions = Map(
"hoodie.table.name" -> "events",
"hoodie.datasource.write.table.type" -> "MERGE_ON_READ",
"hoodie.datasource.write.recordkey.field" -> "event_id",
"hoodie.datasource.write.partitionpath.field" -> "date",
"hoodie.datasource.write.precombine.field" -> "timestamp",
// Compaction settings
"hoodie.compact.inline" -> "true",
"hoodie.compact.inline.max.delta.commits" -> "5",
// Index settings
"hoodie.index.type" -> "BLOOM",
"hoodie.bloom.index.parallelism" -> "200",
// File sizing
"hoodie.parquet.max.file.size" -> "134217728", -- 128MB
"hoodie.parquet.small.file.limit" -> "104857600" -- 100MB
)
-- 2. Schedule compaction for MOR tables
val compactionInstant = hudiClient.scheduleCompaction()
hudiClient.compact(compactionInstant)
-- 3. Run clustering for better layout
val clusteringInstant = hudiClient.scheduleClustering()
hudiClient.cluster(clusteringInstant)
-- 4. Archive timeline to reduce metadata
spark.sql("call run_archive('events', '10')") -- Keep last 10 commits
-- 5. Clean up old file versions
spark.sql("call run_clean('events', '10')") -- Retain 10 commits
Part 14: Future Trends
Sarah: "This is incredibly comprehensive! What's next for these formats? Where are they heading?"
Alex: "Great question! The table format space is evolving rapidly. Let me share what's on the horizon."
Emerging Trends
┌─────────────────────────────────────────────────────────────┐
│ FUTURE DEVELOPMENTS │
├─────────────────────────────────────────────────────────────┤
│ │
│ 🎯 Iceberg Roadmap │
│ ──────────────── │
│ • Puffin statistics format (advanced data skipping) │
│ • Secondary indexes │
│ • Branching and tagging (Git-like operations) │
│ • Write distribution modes │
│ • Deletion vectors (faster deletes) │
│ │
│ 🎯 Delta Lake Roadmap │
│ ────────────────── │
│ • Liquid Clustering (auto-optimization) │
│ • Deletion vectors (production) │
│ • UniForm 2.0 (bidirectional sync) │
│ • Delta Kernel (engine-agnostic library) │
│ • Photon performance improvements │
│ │
│ 🎯 Hudi Roadmap │
│ ────────────── │
│ • Consistent hashing index │
│ • Record-level index │
│ • Multi-modal index │
│ • Better Flink integration │
│ • Timeline server scalability │
│ │
│ 🎯 Industry Convergence │
│ ────────────────── │
│ • Multi-format support becoming standard │
│ • Interoperability improving (XTable/UniForm) │
│ • Cloud vendor adoption expanding │
│ • Standard query engine interfaces │
│ • Cross-format tooling emerging │
│ │
└─────────────────────────────────────────────────────────────┘
Alex: "I think we'll see more convergence over time. Each format is learning from the others. Delta is adding engine-agnostic support, Iceberg is improving write performance, and Hudi is simplifying its architecture."
Part 15: Quick Reference Guide
Sarah: "This has been amazing! Can you give me a quick reference I can keep handy?"
Alex: "Of course! Here's a cheat sheet."
Quick Decision Matrix
┌─────────────────────────────────────────────────────────────┐
│ QUICK DECISION GUIDE │
├─────────────────────────────────────────────────────────────┤
│ │
│ Your Situation → Recommended Format │
│ ───────────────────────────────────── │
│ │
│ ✓ "We use Databricks exclusively" │
│ → Delta Lake 🔺 │
│ │
│ ✓ "We need to support Spark, Flink, Trino, Athena" │
│ → Iceberg 🏔️ │
│ │
│ ✓ "We have heavy real-time upserts" │
│ → Hudi (MOR) 🎯 │
│ │
│ ✓ "We're on AWS, multi-engine analytics" │
│ → Iceberg 🏔️ │
│ │
│ ✓ "We need to change partitioning later" │
│ → Iceberg 🏔️ │
│ │
│ ✓ "Spark only, want best Spark performance" │
│ → Delta Lake 🔺 │
│ │
│ ✓ "CDC from databases, streaming updates" │
│ → Hudi (MOR) 🎯 │
│ │
│ ✓ "Multi-cloud, vendor independence critical" │
│ → Iceberg 🏔️ │
│ │
│ ✓ "Need incremental ETL patterns" │
│ → Hudi 🎯 │
│ │
│ ✓ "Large team, need stable/mature solution" │
│ → Delta Lake 🔺 or Iceberg 🏔️ │
│ │
└─────────────────────────────────────────────────────────────┘
Feature Checklist
┌──────────────────┬──────────┬────────┬──────────────────────┐
│ Need This? │ Iceberg │ Delta │ Hudi │
├──────────────────┼──────────┼────────┼──────────────────────┤
│ Spark only │ ⚪ OK │ 🟢 Best│ ⚪ OK │
│ Multi-engine │ 🟢 Best │ 🟡 Growing│ 🟡 Limited │
│ Time travel │ 🟢 Yes │ 🟢 Yes │ 🟡 Basic │
│ Fast upserts │ ⚪ OK │ 🟢 Good│ 🟢 Best │
│ Streaming writes │ ⚪ OK │ 🟢 Good│ 🟢 Best │
│ Partition evolve │ 🟢 Yes │ ❌ No │ ❌ No │
│ Hidden partition │ 🟢 Yes │ ❌ No │ ❌ No │
│ Z-Order/Cluster │ ❌ No │ 🟢 Yes │ 🟢 Yes │
│ Incremental ETL │ 🟡 Via snapshot│ 🟡 Via CDF│ 🟢 Native│
│ CDC optimized │ ⚪ OK │ 🟢 Good│ 🟢 Best │
│ Read performance │ 🟢 Great │ 🟢 Great│ 🟡 Good (MOR) │
│ Write performance│ ⚪ OK │ 🟢 Good│ 🟢 Best (MOR) │
│ Maturity │ 🟢 High │ 🟢 High│ ⚪ Medium │
│ Community │ 🟢 Large │ 🟢 Large│ ⚪ Growing │
│ Docs quality │ 🟢 Great │ 🟢 Great│ 🟡 Improving │
└──────────────────┴──────────┴────────┴──────────────────────┘
Legend: 🟢 Excellent | ⚪ Good | 🟡 Adequate | ❌ Not supported
Conclusion
Sarah: "Alex, this has been incredibly helpful! I feel like I finally understand the differences and can make an informed decision."
Alex: "I'm glad! The key takeaway is that there's no 'best' format - only the best format for YOUR use case. Let me summarize:"
Final Thoughts
╔══════════════════════════════════════════════════════════════╗
║ THE BOTTOM LINE ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ All three formats solved the data lake ACID problem. ║
║ All three are production-ready and battle-tested. ║
║ All three will continue to evolve and improve. ║
║ ║
║ Choose based on: ║
║ 1. Your primary query engine(s) ║
║ 2. Your workload characteristics ║
║ 3. Your team's expertise ║
║ 4. Your org's existing investments ║
║ 5. Your future flexibility needs ║
║ ║
║ 🏔️ Iceberg = Future-proof, engine-agnostic champion ║
║ 🔺 Delta = Spark-optimized, enterprise-ready ║
║ 🎯 Hudi = Real-time upsert specialist ║
║ ║
║ You can't go wrong with any of them - ║
║ they're all excellent solutions! ║
║ ║
╚══════════════════════════════════════════════════════════════╝
Sarah: "Perfect! One last question - can I start with one and add another later?"
Alex: "Absolutely! Many organizations run multiple formats side-by-side. Use Iceberg for cross-engine analytics, Delta for Spark-heavy workloads, and Hudi for CDC pipelines. They can coexist peacefully."
Sarah: "Amazing. Thanks for the coffee chat, Alex! I'm ready to build our data lakehouse now."
Alex: "Anytime! Remember - the best architecture is the one that solves YOUR problems. Happy building!"
Additional Resources
Official Documentation
- Apache Iceberg: https://iceberg.apache.org/docs/latest/
- Delta Lake: https://docs.delta.io/latest/index.html
- Apache Hudi: https://hudi.apache.org/docs/overview
Community & Support
- Iceberg Slack: Apache Iceberg community workspace
- Delta Lake Forum: delta-users Google group
- Hudi Slack: Apache Hudi community workspace
Tutorials & Examples
- Iceberg hands-on demos on GitHub
- Delta Lake quickstart notebooks
- Hudi tutorials and use cases
Author Note: This conversation represents real-world experiences from implementing open table formats across various industries. Each format has passionate communities and continues to evolve rapidly. Stay updated with the official channels for the latest features and best practices!
Top comments (0)