Data is everywhere. It’s growing fast. Every business wants to manage, analyze, and use it smartly. That’s where modern data architectures like lakehouses come in.
If you’re looking to build something powerful, flexible, and scalable—this is it. A lakehouse architecture combines the best of data lakes and data warehouses.
So, how do you stand up a lakehouse using Delta or Iceberg? Let’s break it down in a fun, simple way.
📦 What’s a Lakehouse Anyway?
Imagine taking the raw power of a data lake and mixing it with the structure and speed of a data warehouse.
That’s a lakehouse.
It keeps your data in open formats but adds the things you need to run enterprise workloads—like ACID transactions, schema enforcement, and better performance.
Two of the most popular formats for powering lakehouses are:
- Delta Lake (from Databricks)
- Apache Iceberg (open-source, rising fast!)
⚙️ Step 1: Choose Your Format (Delta or Iceberg)
You’ll want to decide which format works best for you:
- Delta Lake is great if you’re using Spark and Databricks.
- Iceberg works well in many engines like Flink, Trino, Presto, Dremio, and Spark.
Both are open formats, so you’re not locked in. That’s the beauty!
Need help choosing? Think about:
- What tools you already use
- Community support
- Longevity and compatibility
🏗️ Step 2: Set Up Your Storage
Pick a cloud storage service or on-prem system. Lakehouses thrive on scalable object storage.
Popular options include:
- Amazon S3
- Azure Data Lake Storage (ADLS)
- Google Cloud Storage
- HDFS (if you’re on-prem)
This is where your raw and transformed data will live.
🛠️ Step 3: Pick Your Compute Engine
Next, choose how you’ll process and query the data in your lakehouse.
Here are some friendly favorites:
- Spark – Works with both Delta and Iceberg
- Trino – Great SQL engine, runs fast queries on Iceberg
- Databricks – Best if you’re all-in on Delta
- Dremio – Awesome GUI and supports Iceberg
- Snowflake (beta) – Starting to support Iceberg tables
Pick what matches your engineering stack and team skills.
🧱 Step 4: Build Your Table(s)
Create Delta or Iceberg tables so you can start organizing and querying data.
For Delta Lake with Spark:
spark.sql("CREATE TABLE my_table (id INT, name STRING) USING DELTA LOCATION 's3://your-bucket/my_table'")
For Iceberg with Spark:
spark.sql("CREATE TABLE my_table (id INT, name STRING) USING iceberg LOCATION 's3://your-bucket/my_table'")
Want SQL instead of code? You got it! Many tools (like Trino) let you use SQL to create and manage Iceberg tables.
🔁 Step 5: Load Some Data!
What’s a lakehouse without some juicy data?
Ingestion tools you can use:
- Apache Spark for batch loads
- Apache NiFi for streaming and automation
- Kafka + Iceberg for stream-to-lake magic
- Airbyte or Fivetran for SaaS connectors
Start small. Load logs, CSVs, or JSONs and build from there.
🔍 Step 6: Query Like a Boss
Now your data’s in. Let’s analyze it!
Use SQL tools, notebooks, or BI dashboards to run queries on your lakehouse.
Here’s a basic query in Spark SQL over a Delta table:
SELECT name, COUNT(*) FROM my_table GROUP BY name
Or with Trino and Iceberg:
SELECT category, AVG(price) FROM sales_table GROUP BY category
Fast, flexible, and fun.
🛡️ Step 7: Govern, Optimize, Repeat
A real lakehouse isn’t just about storing and querying data. You need good governance too.
Things to think about:
- Schema evolution (adding columns as your data grows)
- Time Travel (view past versions of your data)
- Data Lineage (know where your data came from)
- Access Control (who can see what?)
Both Delta and Iceberg support these, but implementation varies.
Optimize as you go:
- Compact small files.
- Repartition your data.
- Use Z-Ordering or clustering logic (Delta only).
🎯 Bonus Tip: Make It a Team Sport
Don’t do this alone. Build a team to manage, enhance, and scale your lakehouse.
Include folks across:
- Data Engineering
- Analytics
- Security
- Business units
The more teamwork, the more value your lakehouse creates!
🚀 Summary: Checklist for Standing Up a Lakehouse
- ✔️ Choose a format: Delta or Iceberg
- ✔️ Pick storage: S3, ADLS, etc.
- ✔️ Add compute: Spark, Trino, Databricks
- ✔️ Set up tables: Using code or SQL
- ✔️ Ingest data: Batch or streaming
- ✔️ Query data: Fast, interactive analytics
- ✔️ Govern and optimize: Balance power and control
🎉 Final Words
Standing up a lakehouse with Delta or Iceberg is easier than you think. Once it’s running, it becomes a powerful foundation for data analytics, AI/ML, and more.
It’s open. It’s flexible. It grows with you.
Now go ahead and build your lakehouse. Just don’t forget to bring your floaties—you’re about to dive deep into data!