Understanding Data Lake Architecture: Components and How They Work Together

A data lake is often described as a centralized repository for storing massive amounts of structured and unstructured data. However, building a functional data lake requires understanding how multiple components work together. In this guide, we'll break down the essential building blocks of modern data lake architecture.

What is a Data Lake?

A data lake is fundamentally different from traditional data warehouses. While data warehouses store pre-processed, organized data optimized for specific queries, data lakes store raw data in its native format, allowing flexibility in how data is processed and analyzed later.

The challenge is managing this flexibility without creating a disorganized collection of data that becomes impossible to work with. Modern data lake architecture solves this through carefully designed components.

The Essential Components

1. Object Storage

Object storage is the foundation of any modern data lake. It's where all your actual data lives.

Why Object Storage?

Scalability: Store petabytes of data without infrastructure concerns
Cost-Effective: Cheap compared to other storage solutions.
Durability: Built-in replication and disaster recovery
Flexibility: Works with any data format

Popular choices include Amazon S3, Google Cloud Storage, and MinIO for on-premise deployments.

2. Table Format

A table format defines how data is organized within object storage. It's the layer that transforms raw files into structured, queryable tables.

Think of it as a contract that defines:

How files are organized
What metadata is tracked
How transactions work
How schema evolution happens

Apache Iceberg is the industry standard table format for modern data lakes because it:

Enables ACID Transactions: Ensures data consistency even with concurrent writes.
Supports Schema Evolution: Add, drop, or rename columns without rewriting data.
Provides Time Travel: Query data as it existed at any point in time.
Handles Hidden Partitioning: Partitions are managed transparently, reducing complexity.

Other table formats like Delta Lake and Apache Hudi exist, but Iceberg has become the standard for new data lake projects.

3. Query Engine

The query engine is what processes your data. It reads from object storage and performs computations.

Apache Spark dominates for several reasons:

Distributed Processing: Scales across multiple machines
Multiple Languages: Python, Scala, SQL, R
Broad Ecosystem: Works with hundreds of libraries and tools
Table Format Support: Native support for Iceberg and other formats
Batch and Streaming: Can handle both batch and real-time processing

Other query engines like Presto, Dask, or DuckDB exist, but Spark's maturity and ecosystem make it the primary choice for production data lakes.

4. Metadata Catalog

The metadata catalog tracks information about tables: their schemas, file locations, and versions.

Two main approaches:

Session Catalog (REST Catalog)

Simpler to set up
Each query engine maintains its own metadata
Good for basic use cases

Version Control Catalog (Nessie)

Advanced version control for data
Multiple branches and merges, like Git for data
Supports zero-copy clones
Better for organizations with complex data workflows

How the Components Work Together

Here's a typical data flow:

Ingest: Raw data lands in object storage (e.g., S3)
Catalog Registration: The catalog tracks the location and schema
Query: A user writes a SQL query through Spark
Execution: Spark consults the catalog for schema and location information
Read: Spark reads data from object storage using the table format instructions
Process: Data is transformed and aggregated
Result: Results are returned to the user or written back to storage

Choosing Your Components

For Small Teams / On-Premise:

Object Storage: MinIO
Table Format: Apache Iceberg
Query Engine: Apache Spark
Catalog: REST Catalog or Nessie

For Cloud-Native (AWS):

Object Storage: Amazon S3
Table Format: Apache Iceberg
Query Engine: Apache Spark or AWS Athena
Catalog: AWS Glue or Nessie

For Experimentation:

Object Storage: Any (S3, MinIO, GCS)
Table Format: Apache Iceberg
Query Engine: Spark or DuckDB
Catalog: REST Catalog

Conclusion

Modern data lake architecture is built on four interconnected components: object storage for raw data, table formats for structure and ACID properties, query engines for processing, and metadata catalogs for organization. Understanding how these components work together is key to building scalable, maintainable data lakes that can grow with your organization.

The combination of Apache Iceberg, Apache Spark, and Nessie has emerged as the gold standard for production data lakes, offering flexibility, scalability, and the advanced features needed for enterprise analytics.