The Right Fuel: The Evolution of Table Formats in the Lakehouse
- Databricks
- Databricks
- 5 min reading time
Tobias Vogler
Choosing the right storage and metadata architecture is the most critical—and often the most underestimated—foundation of a modern data platform. Poor decisions in this area can lead to performance bottlenecks, inconsistent data states, and skyrocketing storage costs.
Just as a rocket won’t tolerate the wrong fuel, a data platform won’t tolerate the wrong format choice. This wiki clears up the misconception that file formats like Apache Parquet and table formats like Delta Lake are competing technologies. It tells the story of how they define the modern data lakehouse as symbiotic layers—and why choosing the right fuel determines the success or failure of your data platform.
Table of contents
1. Executive Summary: The Integration of Data Warehouses and Data Lakes
The history of modern data architecture has been marked by a seemingly intractable dilemma. On one side stood the classic data warehouse (DWH). It offered reliable ACID transactions (Atomicity, Consistency, Isolation, Durability ), strict concurrency (parallel access), schema validation, and high-performance data operations using DML (Data Manipulation Language such as UPDATE or DELETE). The price, however, was high: proprietary system limitations, a lack of flexibility with unstructured data such as videos or free text, and a rigid coupling ofcompute power andstorage, which made scaling projects extremely expensive.
On the other hand, the data lake offered the allure of freedom and cost-efficiency: inexpensive cloud storage (object storage), a strict separation of computing power and storage, and the ability to store any amount of data—structured or unstructured. It sounds promising. But without an organizing structure, these data lakes quickly turned into unusable data swamps: transaction security, multi-user guarantees, and reliable schema management were simply lacking.
The modern solution is called the Data Lakehouse— and it really is as elegant as it sounds. Instead of reinventing the wheel, it builds an intelligent metadata layer (known as the Table Format) directly on top of existing, highly efficient file formats. The result is architecturally groundbreaking: for the first time, a platform combines the reliability, governance, and transactional integrity of a traditional data warehouse with the cost-efficiency, openness, and scalability of a data lake. No compromise—just the best of both worlds, on a shared, open infrastructure.
2. The Tanks: Why Parquet Is the Universal Container for Analytical Fuel
Before we can choose the right fuel, we need the right tank. Our journey begins at the file level. Here, Apache Parquet has established itself as the global de facto standard for analytical workloads. Parquet stores relational and tabular data not row by row (like traditional CSV or JSON files), but in highly compressed columnar blocks (columnar storage).
This physical layout allows the query engine to utilize two fundamental optimization mechanisms directly during read operations:
- Column Pruning: If an analytical query requires only 3 of a table’s 150 columns, the engine physically reads only the blocks containing those 3 columns. The remaining read and write operations (I/O) are completely eliminated.
- Predicate Pushdown (Row Filtering): Parquet files contain statistical minimum and maximum values for each data block in theirfooters. This allows the engine to determine, even before reading entire files, whether a sought-after data record (e.g., Customer_ID = 45982) could even exist in that block. Irrelevant file partitions are skipped immediately.
The Design Flaw: Why Unfinished Parquet Floors Fail
Although Parquet is an extremely efficient file format, it reaches a logical limit when used in isolation in enterprise scenarios. In practice, a business table consists of thousands of individual Parquet files. Without a higher-level entity, this leads to massive problems:
If an ETL (Extract, Transform, Load) process writes new files while an analyst is querying the same table, the analyst will see inconsistent partial states because there is no parallel access control. Furthermore, every UPDATE or DELETE operation requires the complete reading, modification, and rewriting of millions of rows into new files—a massive performance bottleneck.
So, anyone who uses Parquet without a metadata layer is doing the same thing as someone who accidentally fills up with diesel: The tank is full, but the engine just won't start.
3. Mission Control: Delta Lake as an Intelligent Storage Layer
This is where Delta Lake comes in. Delta Lake is not a new file format, but an open metadata layer that operates directly on Parquet files. It acts as the “brain” that restores the mathematical reliability of a relational database to the physical files.
At the heart of this technology is the JSON-based transaction protocol, known as the Transaction Log (or Delta Log). Every write, delete, or update command is recorded sequentially in this log as an atomic state entry (commit). Only once the entry has been successfully written to the log is the change considered to exist for downstream query engines.
The core features of the Delta Protocol in detail:
4. The Open Market: Delta Lake vs. Apache Iceberg vs. Apache Hudi
Delta Lake is not alone in a modern multi-cloud architecture. It shares the market for open table formats with two other major open-source initiatives: Apache Iceberg and Apache Hudi. All three use Parquet as their physical foundation, but differ fundamentally in their architectural philosophy.
Technical Comparison of Table Formats:
Course Correction 2026: The Shift to Iceberg and Polaris
As recent market developments—particularly SAP’s acquisition of the Dremio query engine in May 2026—demonstrate, the previous exclusivity of individual formats is breaking down. The SAP BDC (Business Data Cloud) is natively compatible with Apache Iceberg and the open-source Polaris Catalog.
For enterprise architects, this means that platforms must increasingly be designed to be format-agnostic. To prevent the dreaded proliferation of catalogs (Unity Catalog for Databricks, Polaris for SAP/Dremio/Snowflake), it is strongly recommended to establish a higher-level catalog orchestration layer (such as Atlan or Collibra) and to encapsulate AI scenarios via the MCP (Model Context Protocol).
5. Conclusion and Architecture Recommendation
The right combination of physical file structures and intelligent table formats is the fuel that keeps your lakehouse mission on track. It transforms unstructured storage layers into agile, transaction-safe analytics platforms—ready for takeoff.
Based on your architecture roadmap, the s-peers expert team has identified three clear recommendations:
- No Raw Parquet Lakes: Never store business-critical data as plain Parquet without a metadata layer. The administrative overhead involved in manual schema maintenance and concurrency workarounds will inevitably catch up with you.
- Maintain decoupling: Leverage the inherent strengths of the architectures—keep compute and storage strictly separate.
- Multi-format readiness: Design your platform so that it can consume both Delta and Iceberg structures via open REST catalog interfaces. This protects your investment against future strategic shifts by major platform vendors.
Your architecture is unique—let’s talk
Selecting the right metadata layer and orchestrating your catalogs are key to ensuring the future-proofing of your BI and AI initiatives. Together, we’ll analyze your existing SAP and cloud landscape and build a high-performance, resilient data foundation.
Andreas & Yvonne's Databricks-Guide
Would you like all the important information at a glance?
Download the free guide to SAP Databricks now!
Your data strategy is individual - your consulting should be too
Choosing the right format and technology level depends heavily on your specific use cases—whether streaming data, large batch processing, or complex analysis workloads.
Let's talk about which architecture is right for your data and goals, with no obligation. Contact us for a personal consultation.
