Understanding the evolution of data lakes
Tracing the journey from Hadoop to Snowflake and Databricks and how lakehouse architecture is reshaping data platforms.
Over the past two decades, the evolution of data lakes has transformed data platform architecture, from early Hadoop-based lakes to the modern, unified lakehouse paradigm. With nearly two decades in data engineering, including over a decade of architecting and building modern data platforms for financial services, I’ve seen this architectural evolution unfold at scale. It’s more than a shift in tooling; it’s a fundamental rethinking of how we design for scale, governance and agility.
This article captures that evolution and shares key insights to help teams think more strategically about choosing the right architecture, whether you’re focused on near-real-time analytics, AI/ML, regulatory reporting or all of the above.
1. The Hadoop era – origins of modern data lakes
In the early 2000s, the explosion of unstructured data (e.g., logs, clickstreams, IoT signals) demanded storage solutions that traditional relational databases couldn’t handle. Apache Hadoop emerged as a scalable, distributed storage and batch processing framework, enabling data lake creation for diverse datasets.
| Key benefits | Challenges |
| Hadoop introduced a schema-on-read model, enabling organizations to ingest and store raw, semi-structured and unstructured data without rigid up-front schema design. | Querying data typically required writing complex MapReduce jobs or using early abstraction layers, like Hive and Pig, resulting in low developer productivity. |
| It provided cost-effective scalability by utilizing distributed storage and processing across clusters of commodity hardware. | Hadoop architectures were inherently batch-oriented and lacked native support for near-real-time or low-latency analytics. |
| Hadoop’s batch processing frameworks, particularly MapReduce, allowed enterprises to analyze petabyte-scale datasets that were previously infeasible with traditional relational systems. | Data governance, metadata management and security were immature, often leading to “data swamp” conditions where data quality and lineage could not be trusted. |
2. Cloud data warehouses: high-performance structured analytics
By the early to mid-2010s, cloud-native data warehouses like Snowflake, BigQuery and Redshift brought scale and simplicity together by redefining structured data analytics through modern cloud technology. These platforms were designed for structured analytics and interactive business intelligence.
| Key benefits | Challenges |
| Cloud data warehouses introduced fully managed, elastic compute environments that abstracted infrastructure complexities and enabled rapid scalability. | Cloud warehouses were optimized for structured data and often required separate systems or complex architectures to integrate unstructured or streaming data sources. |
| They provided strong native SQL support, democratizing access to big data analytics for business users and analysts. | Near-real-time ingestion and event-driven architectures were not natively supported, limiting their use for operational analytics. |
| By separating storage and compute layers, platforms like Snowflake optimized cloud cost and performance independently, making large-scale structured analytics more viable. | Without careful workload optimization, compute costs could escalate rapidly with large concurrent user bases or unpredictable query patterns. |
3. Spark & Databricks: powering unified analytics platforms
By the mid 2010s, Apache Spark emerged as a faster, more flexible alternative for large-scale distributed data processing. Databricks added performance, flexibility and programmability to big data engineering pipelines with a cloud-native experience for distributed analytics, ML and near-real-time processing.
| Key benefits | Challenges |
| Apache Spark revolutionized big data processing with distributed in-memory computation, offering major performance gains over traditional batch models like MapReduce. | Early Spark and Databricks implementations lacked robust transactional guarantees and strong schema governance, complicating enterprise data management. |
| Databricks provided a unified cloud-native platform supporting batch analytics, streaming data and machine learning workflows from a single environment. | Operating large Spark clusters required specialized tuning and expertise, with cost and performance management becoming a critical operational burden at scale. |
| Broad language support, including Python, Scala and SQL, enabled cross-functional teams (e.g., data engineers, data scientists and data analysts) to collaborate effectively. | SQL query performance over large Spark datasets often lagged behind specialized warehouses without extensive optimization efforts. |
4. The lakehouse revolution: benefits of data lakehouse architecture
Emerging in the late 2010s and early 2020s, lakehouse architecture began to unify data lakes and cloud data warehouses into a single platform. Lakehouse architecture blends the scale and flexibility of data lakes with the performance, structure and reliability of warehouses. This is achieved through open table formats like Delta Lake and Apache Iceberg, which enable ACID transactions, schema evolution and time travel by managing metadata and transaction logs directly on cloud object stores.
| Key benefits | Challenges |
| Lakehouse architectures unify storage for both structured and unstructured data, enabling consistent access across SQL analytics, machine learning and near-real-time applications. | Achieving optimal query performance across diverse workloads requires sophisticated metadata optimization and data/file layout strategies. |
| Open table formats like Delta Lake and Apache Iceberg introduced ACID transactions, schema enforcement and time travel on cloud object stores—addressing critical governance gaps in traditional data lakes. | Near-real-time and ad-hoc workloads can still face latency challenges compared with purpose-built warehouses unless architectures are carefully tuned. |
| Enterprises can now build analytics and AI platforms without duplicating data across lakes and warehouses, reducing architectural complexity, operational overhead and simplifying data lake creation. | Managing open interoperability across evolving standards (Delta, Iceberg, Hudi) and ensuring seamless multicloud governance remain complex enterprise engineering tasks. |
Choosing the right data architecture for analytics and AI
| Dimension | Data lake | Cloud data warehouse | Lakehouse |
| Storage layer efficiency | Scalable object storage (e.g., AWS S3, Azure Blob Storage/ADLS) | Structured storage optimized for SQL workloads (e.g., proprietary hybrid columnar storage format used by Snowflake internal tables) | Scalable storage with open table formats (e.g., Delta Lake, Iceberg) |
| Data types supported | Structured, semi-structured, unstructured | Historically supported structured and semi-structured data (with evolving capabilities for unstructured data) | Structured, semi-structured, unstructured |
| Analytics support | Requires external compute engines (e.g., Hive, Presto, Spark) | Native SQL compute engines (e.g., Snowflake SQL, BigQuery) | Native SQL + ML Compute (e.g., DB-SQL, Photon) |
| ML & streaming | Batch ML support; need external streaming engines (e.g., Flink) | Limited ML support (e.g., via Snowpark); limited streaming support | Native ML capabilities (e.g., MLflow, Feature Store) and Spark Structured Streaming |
| Governance | Externalized catalogs (e.g., Hive Metastore, AWS Lakeformation Governance) | Strong integrated governance via catalog (e.g., Snowflake Horizon) | Strong governance on managed tables with open catalogs (e.g., Unity Catalog) |
| Operational complexity | High (requires tuning of Hadoop/Spark clusters) | Low (fully managed SaaS platforms with native/internal tables) | Medium (depends on whether using managed tables, external tables, tuning table configs or serverless compute) |
Quick architecture fit: selecting the best approach for your needs
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If you primarily need raw, cheaper, scalable storage for diverse datasets, choose data lake.
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If your focus is structured SQL analytics with faster time-to-insight, choose cloud data warehouse.
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If you aim for unifying analytics, AI/ML and near-real-time workloads (e.g., decisioning capabilities), choose lakehouse.
Data lakehouse vs. cloud data warehouse: key differences
While cloud data warehouses and lakehouse architectures are increasingly converging in high-level capabilities, they remain fundamentally different in their architectural foundations and operational strengths.
| Category | Cloud data warehouse (e.g., Snowflake, BigQuery) | Lakehouse (e.g., Databricks/Delta Lake) |
| Storage format | Proprietary storage formats for native/internal tables (e.g., Snowflake micro-partitions) | Open table formats (e.g., Delta Lake, Apache Iceberg) |
| Metadata management | Fully managed internal catalogs for native/internal tables | Externalized metadata with open catalog systems (e.g., Unity Catalog, Hive Metastore) |
| Compute/query engine | Optimized SQL compute for structured datasets | Flexible compute for structured + unstructured data (e.g., Photon Engine) |
| Streaming readiness | Micro-batching via ingestion connectors | Native structured streaming support (e.g., Spark Structured Streaming) |
| ML/AI integration | Historically needed external ML services, with evolving native ML capabilities (e.g., Snowflake ML) | Native ML and MLOps integration (e.g., MLflow, Feature Store) |
| Vendor lock-in | High (proprietary storage format and compute for internal tables) | Low (open data storage formats, open compute engines) |
Choosing between them depends on whether your future workloads prioritize curated analytics simplicity and ease of use OR flexible, scalable, near-real-time data platforms built on open standards.
Final take: What’s next for modern data platforms?
Lakehouse architectures are redefining modern data platforms. However, in practice, we rarely replace one paradigm with another. Architectures evolve and converge. Data lakes are gaining governance capabilities. Cloud warehouses are adopting open formats and expanding AI/ML integration. Top vendors reflect this shift as well: Snowflake is embracing Iceberg Open Table Format and machine learning capabilities; Databricks is strengthening SQL performance and governance frameworks along with open sourcing Unity Catalog.
Rather than choosing a single model, enterprises are blending strategies—modernizing data lakes, enhancing enterprise data warehouses and selectively adopting lakehouse patterns. Architectures will continue to converge, but key lakehouse principles, such as openness, unification and near-real-time capabilities, are increasingly shaping the future direction of modern data platforms.
