Choosing data storage and management approaches is a strategically important task for any modern business. Constantly growing volumes of information and the implementation of data-driven technologies require companies to carefully study and compare various options for such solutions. In this article, we present a comparative analysis of the most popular systems today: data lake vs. data warehouse. Learn about their architecture, key functions, and application scenarios.
Core Concept and Purpose
Modern companies work daily with growing volumes of heterogeneous assets — from structured records to multimedia and streaming sources. Choosing the right storage architecture directly impacts the effectiveness of analytics and decision-making. In this context, it's important to understand the differences between data lakes and data warehouses — two key approaches, each focused on its own tasks and use cases.
Data Lakes
Data lakes (DL) are systems for storing large volumes of raw assets in their native format. They contain various types and formats of data in one place, including both structured (customer records, order records, etc.) and unstructured (videos, records from IoT devices, etc.) information.
The concept was first described by James Dixon, CTO of Pentaho, in his blog in October 2010. Amid the boom in cloud services and mobile apps, such systems have become widely popular in data management. They help businesses quickly and reliably store large volumes of diverse data, primarily in unstructured or semi-structured formats.
Data lakes are storage solutions that accommodate assets without predefined structure or schema. They are ideal for companies that need to store multiple types of data in large volumes, using it for machine learning, analytics, or other scenarios.
Data Warehouses
Data warehouses (DW) don't simply store information; they format, classify, tag, and distribute it to optimize data processing. They are a higher-level solution that performs a comprehensive set of operations, including aggregating assets from various sources, cleaning, preparing, storing, searching, and transferring them to third-party systems.
Data warehouses often provide a range of additional functions related to processing and exploration. They help businesses improve the speed and accuracy of analytical tasks, including analyzing financial transactions and trends, forecasting, performance monitoring, and more.
Previously, on-premises DWs were considered a key data management tool, sought after by large and medium-sized businesses. Today, data lakehouses have become a fully fledged third architecture, actively adopted by large companies — they combine the flexibility of data lakes with the structure and manageability of data warehouses.
Data Structure and Storage Approach
The efficiency of working with information depends largely on how it is organized, stored, and processed within the system. Architectural approaches define the logic of asset interaction at all stages — from loading to analysis. Data lakes and warehouses use different storage and computation models, which directly impacts their flexibility, performance, and application areas.
Data Lakes
Modern data lake architecture is multi-layered. It consists of several layers:
- Ingestion layer. In this layer, the system receives information from various sources in batch (CSV, daily dumps) or streaming (real-time) format. The main connectors are DBs, APIs, SaaS, IoT devices, etc.
- Storage layer. Modern services physically store assets in cloud object storage in formats such as JSON, CSV, images, binary files, and so on. Data partitioning is also implemented here for improved traceability and data integrity protection. Meanwhile, open table formats such as Apache Iceberg and Delta Lake have become industry storage standards. They provide ACID transactions, versioning, and interoperability across different computing engines.
- Management and metadata layer. The third layer contains options for information quality assurance, security and access control, cataloging, metadata management, lineage tracking, and retention/lifecycle policies.
- Processing and computing layer. This layer manages computational processes separate from storage — one of the key advantages of data lake architecture. Machine learning model training, batch and streaming assets processing, ELT/ETL processing, and SQL analytics occur here.
- Consumption layer. At the final level, information stored in the data lake is transferred to external systems. Most often, they are integrated with BI dashboards (PowerBI, Tableau, QuickSight), AI/ML platforms (SageMaker, Vertex AI, Databricks), APIs, and other consumers.
Data lake architecture is best suited for storing unstructured or semi-structured information in its raw form. Its key feature is the "schema-on-read" approach, where assets are structured as they're read. This allows for loading different types of data into the system and provides the necessary flexibility for rapid searching, exploration, and other operations.
Data Warehouses
DW architecture, like a data lake, is characterized by a multi-layered structure. It has several levels:
- Ingestion layer. This layer receives information from various sources, including operational DBs (OLTP), CRM, ERP, financial systems, marketing platforms, APIs, and so on.
- Processing layer. The ETL process is key to the next layer. First, raw data is loaded into a staging environment and then transformed using SQL. Orchestration, quality assessment, normalization, security checks, and so on are also performed here.
- Storage and modeling layer. This layer stores integrated and consistent data. Various analytical engines (OLAP, SQL) are used here, allowing users to query datasets and analyze them directly in the DW.
- Consumption and visualization layer. The top-level storage layer contains a set of tools for detailed analytics, aggregation, metric management, and KPI monitoring. These include BI dashboards, reporting options, OLAP cubes, APIs, and more.
Unlike data lakes, DWs use a "schema-on-write" approach. This requires all assets loaded into the system to conform to a specific schema. This improves information quality and consistency, optimizing it for analytical processes (OLAP).
Data warehouses are most effective for storing and handling structured data, as they are based on relational DBs and strict schemas. However, some are adapted for unstructured and semi-structured data.
Analytics, Performance, and Use in Decision-Making
The practical value of any storage system is determined by how effectively it supports analytics and helps make informed business decisions. Query processing speed, data quality, and scalability directly impact the accuracy of insights and the speed of response to changes. Data lakes and warehouses address these challenges differently, offering their own approaches to analytics, big data, and machine learning model implementation.
Data Lakes
Data lakes are highly effective for solving a wide range of analytical and data-driven tasks. They quickly load and process any asset format, easily scale to accommodate varying workloads, and allow for long-term storage of large volumes of content. Let's look at the most popular data lake use cases in the business environment.
Business Intelligence (BI) and reporting
The systems load raw info from external sources, structure it by category, and create models in tables. After processing user queries through SQL engines (Presto/Trino, Athena, Databricks SQL, Spark SQL), they find the required records and send them for visualization to BI tools (Tableau, PowerBI, Looker).
Data lakes support parallel storage of processed and raw data, store data over long periods of time for trend analysis, and track.
Machine learning
Data lakes significantly improve the speed and performance of ML models, as they can store large sets of info in various formats (text, images, logs, IoT records, etc.). They also help optimize the training/retraining of ML algorithms and the monitoring of their performance.
The most common use cases:
- Recommender systems (clickstreams + product information).
- Customer churn prediction (CRM + behavioral signals).
- Maintenance prediction (sensor data).
- Financial monitoring (real-time processing + pattern analysis).
Big data
Data lakes provide storage and processing services for massive datasets, enabling loading and retrieval of information in specific big data formats, such as Parquet, ORC, Delta Lake, Iceberg, and Hudi. They also support various processing patterns, including batch and streaming ETL, log analysis, time series processing, and large-scale joins/aggregations.
Data Warehouses
DWs are useful for optimizing BI, machine learning, and big data processing. Key features include asset cleansing and organization, accelerated processing of complex queries, and built-in analytics and reporting capabilities.
Let's look at key DW use cases that offer the greatest business value. These use cases highlight their strengths: structured design, high query speed, and readiness for analytics.
Business analytics and reporting
Modern cloud storage systems are optimized for handling and managing structured data. This allows them to efficiently and quickly analyze large volumes of information, generating data-driven reports and insights with visualization.
Data warehouses provide businesses with a number of important BI tools. These include comprehensive monitoring dashboards, KPI tracking (MRR, churn, CAC, LTV, ARPU), financial statement analysis, forecasting and trend analysis, performance benchmarking, and more.
Machine learning
DWs are widely used in several ML-related scenarios. First and foremost, they are used as a repository for characteristics used to train/retrain ML models. For example, they store historical labels, behavioral metrics, aggregated data, etc.
A common scenario is training ML models in-house. This eliminates the need for developers to transfer information to external systems, while version history and autoscaling support improve the performance of such processes.
Big data
Cloud services are capable of storing petabyte-scale datasets and scaling computing resources elastically to match the current workload. They support structured and semi-structured data in a range of formats (JSON, Avro, Parquet, ORC). This enables the loading and processing of large-scale datasets from a variety of sources, including clickstreams, web tracking, IoT sensor data, event logs, and more.
Role in the Modern Data Stack
Data lakes and data warehouses are key components of modern data architecture. Together with a data lakehouse, they form the foundation of a modern tech stack. Both systems enable the storage, use, and transfer of vast amounts of information of various types and formats to third-party services and applications, both with and without preprocessing.
The roles of data lakes and warehouses in a modern data stack are represented by a number of processes and integrations:
- Loading and storage. These systems act as a centralized location for collecting information from various sources (OLTP, SaaS, logs and telemetry, files, IoT devices). Cloud object storage, orchestrators, streaming/batch data loading, and other tools are used for this purpose.
- Transformation and cleansing. Assets loaded into data lakes and warehouses are transformed and cleansed using either the Extract, Load, Transform (ELT) or Extract, Transform, Load (ETL) approach. These processes are performed through integrations with SQL engines (Presto/Trino, Athena, BigQuery, Snowflake), Apache Spark, and other frameworks.
- Analytics. Native integrations with SQL engines and business intelligence data storage allow you to perform relevant tasks directly within their interface without transferring info to third-party services or storage.
- AI and ML training and tuning. The ecosystems under consideration may contain large volumes of data with high granularity, making them suitable for training/retraining models, streaming inference, batch evaluation, and other AI and ML operations.
- Data management and evaluation. A key component of a modern data stack is the integration of systems with asset management and control tools. This enables options such as cataloging and quality control, provenance tracking, access control, and more.
Final Thoughts
We have considered popular information storage models. They are widely used to build and manage modern tech infrastructure. However, they follow different data management strategies, suited to different purposes and use cases. Data lakes are designed for quickly loading and transferring large volumes of raw information. Data warehouses are ideal for sorting and cleaning assets, as well as performing calculations and other processes.