An Introduction to Data Lakes: Wolk’s Practical Guide to Taking a Dip in the Water

When working in the data-space you probably at the very least have heard about the term Data Lake, or maybe you just came by the term Data Lakehouse. Maybe your manager asked you to evaluate different types of storage solutions and you happened to come across data lakes. Whatever the reason is you are interested, we will take you through the origins, basics and future of data lakes.

In this blog we will guide you in the decision whether a data lake is a good solution for you, and if so: which approach to choose.

What Exactly Is a Data Lake? The Foundation of Modern AI Infrastructure

At its core, a data lake is a very simple concept: it's a centralized repository that allows you to store all your structured and unstructured data at any scale - making it the ideal foundation for AI and machine learning initiatives.

A data lake is essentially a directory on a remote server where you can store all sorts of files in their raw, native formats - from structured database tables to unstructured text, images, videos, and sensor data.

Unlike traditional data warehouses that require data to be transformed and structured before storage, data lakes accept data in its original form - whether that's JSON files, images, videos, log files, or any other format.

This "store now, structure later" approach offers a lot of flexibility, especially when you're not yet sure how all your data might be used in the future. It also introduces complexity and the risk of becoming unmanageable.

More on that later, let’s first have a look at its origins to understand whether these data lakes are still relevant today.

Data Lake Evolution: From Hadoop clusters to AI Enablers

The Hadoop Era

The concept of data lakes emerged around 2010 with the rise of Apache Hadoop, an open-source framework designed for distributed storage and processing of large datasets. Early data lakes were built using Hadoop Distributed File System (HDFS), which allowed organizations to store massive amounts of data across clusters of hardware.

However, these early implementations came with significant challenges:

• They required specialized skills to set up and maintain

• The infrastructure was complex and expensive

• Querying the data was often slow and cumbersome

• Data governance and metadata management were afterthoughts

Many early data lake projects unfortunately turned into "data swamps": vast repositories of data with little structure, and little usage.

The Cloud Revolution

Around 2015, cloud storage solutions like Amazon S3, Azure Blob Storage, and Google Cloud Storage began to change the game. These object storage services offered:

• Virtually unlimited storage

• Pay-as-you-go pricing

• High durability and availability

• Simple APIs for data access

This shift reduced the barrier to entry for creating data lakes. Organizations no longer needed specialized infrastructure or Hadoop expertise. With just a few clicks and a credit-card, you could create cloud storage buckets and start uploading data.

The Rise of Modern Processing Engines

While cloud storage solved the data storage problem, organizations still needed efficient ways to process and analyze the data stored in them. This led to the development of powerful processing engines like:

• Apache Spark: A unified analytics engine for large-scale data processing

• Apache Trino: Distributed SQL query engines for big data

• Apache Flink: A stream processing framework for high-throughput, low-latency applications

These tools allowed data engineers, analysts and scientists to directly query and process data stored in cloud data lakes without first moving it to a data warehouse.

Enter the Data Lakehouse: Bridging Two Worlds

Despite these advancements, organizations still faced data management challenges. Years of pumping data into a data lake, in some cases resulted in so-called data swamps. Data lakes excelled at storing raw, diverse data but lacked the performance, structure and reliability of data warehouses for business intelligence. Data warehouses provided structured, and optimized access but were expensive and inflexible.

The data lakehouse paradigm emerged around 2019 to combine the best of both worlds:

• The flexibility and cost-efficiency of data lakes

• The performance, governance, and reliability of data warehouses

This architectural pattern is enabled by open table formats like Delta Lake and Iceberg.

Delta Lake: Bringing Reliability to Data Lakes

Delta Lake is an open-source storage layer that brings ACID transactions to your data lake. Created by Databricks, Delta Lake addresses many of the reliability challenges of traditional data lakes by providing:

• ACID Transactions: Ensures data consistency even with concurrent reads and writes

• Schema Enforcement: Prevents data corruption by validating that new data adheres to the table's schema

• Time Travel: Allows access to previous versions of data for audits or rollbacks

• Optimization: Supports file compaction and indexing for improved query performance

Apache Iceberg: High-Performance Table Format for Analytics

Apache Iceberg is an open table format originally developed at Netflix to address the limitations of Hive tables in large-scale data environments. Since becoming a top-level Apache project in 2020, Iceberg has gained significant adoption and offers:

• ACID Transactions: Provides atomic operations and serializable isolation for concurrent writers

• Schema Evolution: Supports adding, dropping, renaming, and reordering columns without table rewrites

• Hidden Partitioning: Automatically handles partition values, eliminating user errors and allowing partition evolution

• Time Travel: Enables point-in-time queries using either timestamps or snapshot IDs

• High Performance: Improves query speed through metadata-based file pruning and statistics

Delta Lake vs. Apache Iceberg: Key Considerations

While both Delta Lake and Apache Iceberg solve similar problems, there are important differences to consider:

When to choose Delta Lake:

• If Spark is your primary processing engine

• If you're using Databricks as your platform

• If you want a battle-tested solution with strong commercial support

When to choose Apache Iceberg:

• If you need to work across multiple processing engines (Spark, Flink, Trino, etc.)

• If vendor neutrality is important to your organization

• If you need more sophisticated partitioning and schema evolution features

When to Choose a Data Lakehouse Over a Traditional Database

While data lakehouses offer powerful capabilities, they're not the right solution for every scenario. Here's a detailed breakdown of when to invest in a Data Lakehouse architecture versus sticking with a traditional databases, like PostgreSQL:

Choose a Data Lakehouse When:

1. Your Data Volume Exceeds Traditional Database Capacity

Data lakehouses truly shine when you're dealing with massive data volumes with diverse data formats, where traditional databases become impractical:

• Multi-Terabyte-scale data: When your data exceeds 10 terabytes, traditional databases often struggle with both storage and query performance.

• High-velocity data streams: When ingesting hundreds of thousands or millions of events from IoT devices, user interactions, or other high-volume sources.

• Data that grows fast: When historical data retention requirements mean your storage needs double or triple annually.

2. You Need to Support Diverse Data Types and Formats

Traditional databases excel at structured data but struggle with:

• Unstructured data: Text documents, emails, social media posts, audio, video, and images

• Semi-structured data: JSON, XML, logs, and other formats with nested or variable structure

• Multiple data formats: When you need to work with Parquet, Avro, ORC, CSV, and proprietary formats in the same system

3. Your Analytics Require Advanced AI/ML Capabilities

When your organization's analytics goals include:

• Training machine learning models: Models requiring extensive historical data and compute resources

• Supporting data science experimentation: Providing a flexible environment where data scientists can explore without impacting production systems

• Implementing real-time model scoring: Needing to score models against streaming data at scale

• Building computer vision or NLP solutions: Working with image, video, or text data that requires specialized processing

4. You Have a Multi-Engine Environment

When you need to support:

• Multiple processing frameworks: Spark, Flink, Presto/Trino, and others running against the same data

• Different query languages: SQL, Python, R, and other languages accessing the same datasets

• Various analytical tools: Connecting business intelligence, data science notebooks, and custom applications to a unified data source

Why Would My Organisation Need a Data Lakehouse?

If you're embarking on your data and AI journey, here are some reasons to consider implementing a data lake or lakehouse architecture:

1. Scalability and Cost Efficiency

Modern data lakes built on cloud storage offer virtually unlimited scalability at a fraction of the cost of traditional data warehouses:

• Pay only for what you use: Unlike data warehouses with predefined capacity, you pay only for the storage you actually consume

• No upfront commitment: Start small and grow as your needs evolve

• Separation of storage and compute: Scale storage and processing independently based on your workloads

2. Supporting Diverse Workloads

Data lakes and lakehouses can support a wide range of use cases:

• Business intelligence and reporting

• Data science experimentation

• Machine learning model training

• Real-time analytics

• Batch processing

This versatility means you don't need separate systems for different types of analysis.

3. Future-Proofing Your Data Strategy for AI Success

Perhaps the most compelling reason to implement a data lake is to future-proof your organization's AI journey:

• Store raw data now that becomes AI training data later: Often the true value of historical data isn't apparent until you're ready to train AI models that need extensive historical examples

• Create the foundation for GenAI and LLM initiatives: Large language models and generative AI require access to vast, diverse datasets that data lakes are designed to provide

• Support the full AI/ML lifecycle: From exploratory data analysis to feature engineering, model training, and ongoing model improvement

• Enable real-time AI applications: Modern data lakes support both batch and streaming data for real-time AI inferencing

• Flexibility to adopt emerging AI tools and frameworks: With open formats, you can easily leverage new AI technologies as they emerge

Getting Started: A Practical Guide to Building Your AI-Ready Data Foundation

Ready to begin your data lake journey and lay the groundwork for AI success? Here's a step-by-step approach to building your first data lake or lakehouse that will support your current analytics needs while positioning you for future AI initiatives:

1. Start with a Clearly Defined AI or Analytics Use Case

As highlighted in our previous blog posts, the key to success with data and AI initiatives is to start with clearly scoped, high-value projects. Look for use cases that:

• Solve a pressing business problem with significant ROI potential

• Are feasible with your current data quality and quantity

• Use a maximum of two data sources initially to limit complexity

• Can deliver measurable value quickly to build momentum

Some AI-ready use cases to consider:

• Predictive analytics: Forecasting demand, anticipating equipment failures, or predicting customer churn

• Natural language processing: Analyzing customer feedback, support tickets, or product reviews

• Computer vision: Quality inspection in manufacturing or visual analytics for retail

• Recommendation engines: Personalized product recommendations or content suggestions

• Simpler starting points: Automating manual reporting processes or creating a unified customer data view

2. Choose Your Storage Foundation and Table Format

For most organizations, cloud storage is the simplest and most cost-effective option:

• Azure Data Lake Storage Gen2: Combines the scalability of Azure Blob Storage with a hierarchical file system

• Amazon S3: The most widely used object storage service with broad tool support

• Google Cloud Storage: Seamlessly integrates with Google's data processing services

If you have specific compliance requirements or existing on-premises infrastructure, you might also consider hybrid or on-prem options with tools like MinIO.

Once you've selected your storage layer, choose an open table format that will provide the structure and performance benefits of a data warehouse:

• Delta Lake: Offers ACID transactions, schema enforcement, and time travel capabilities with strong Spark integration

• Apache Iceberg: Provides schema evolution, hidden partitioning, and multi-engine support for a more vendor-neutral approach

Consider setting up a metastore (e.g. Unity Catalog) to track your table schemas and statistics. This will make your data discoverable and queryable across different tools and teams.

Many organizations also implement a medallion architecture with three layers:

• Bronze: Raw data stored exactly as it was ingested

• Silver: Cleansed, validated, and enriched data

• Gold: Business-level aggregates optimized for specific use cases

This architecture helps balance the flexibility of raw data storage with the performance and usability required for analytics and AI workloads.

3. Define Your Data Ingestion Strategy

Start simple with batch ingestion processes:

• Extract data periodically from source systems

• Apply basic validations

• Organize data using a simple folder structure

As you mature, you can implement more sophisticated approaches:

• Streaming ingestion for real-time data

• Change data capture from operational databases

• Event-driven architectures

Tools like Airflow and Dagster are your friends for orchestrating those pipelines! If you are moving into event-driven territory, or require low latency updates, have a look at Kafka on Confluent.

4. Define Your Data Organization Strategy

How you organize your data in the lake will significantly impact usability, performance, and governance. Consider implementing:

• Logical partitioning: Organize your data by date, region, category, or other dimensions that align with common query patterns

• Optimized file formats: Use columnar formats like Parquet for structured data to improve query performance

• Right-sized files: Aim for files of optimal size (typically 100MB-1GB) to balance processing efficiency and parallelism

• Semantic layers: Create views or models that translate raw data into business-friendly terms (this will also help with AI integration!)

• Retention policies: Establish clear rules for how long different types of data should be retained

5. Choose Your Processing Layer and AI Development Environment

Based on your use case and team skills, select appropriate tools for processing, analyzing data, and developing AI:

• For SQL and analytics users: SaaS options like Amazon Athena, Google BigQuery, Snowflake or our favourite Databricks. If you are looking at an open-source stack we recommend Trino as a query engine.

• For real-time analytics and AI inferencing: Apache Kafka, Flink, Spark Streaming, or Azure Stream Analytics

• For large language models (LLMs) and generative AI: Azure OpenAI Service, Amazon Bedrock, or open-source LLM frameworks such as Pydantic AI.

6. Implement a Data Catalog and Governance Tools

To prevent your data lake from becoming a data swamp, invest in proper cataloging and governance tools from the beginning:

• Data catalog: Implement tools like Azure Purview, AWS Glue Data Catalog, or open-source options like Unity Catalog to provide a searchable inventory of your data assets

• Data lineage tracking: Document the origin and transformations of your data to build trust and enable impact analysis

• Automated data quality checks: Set up regular validation of your data against defined rules and expectations

• Access management: Implement fine-grained access controls based on roles and responsibilities

• Privacy controls: Ensure sensitive data is properly identified, protected, and managed according to regulations

Even a simple approach to governance will pay dividends as your data lake grows in both size and strategic importance. Remember that governance is not just about control—it's about enabling broader, safer use of your data assets.

Start Small, Think Big with Your Data & AI Foundation

Building a data lake or lakehouse doesn't require massive upfront investment or specialized expertise. The key is to start with a clear, high-value use case and expand incrementally as you demonstrate success and build organizational AI capabilities.

Remember these principles for data lake and AI success:

• Focus on business value first, technology second

• Start with a small, well-defined AI or analytics use case

• Implement basic data governance and quality controls from day one

• Build your data architecture with AI workloads in mind

• Learn and adapt as you go, gradually increasing complexity

By taking this measured approach, you'll avoid the pitfalls that have plagued data lake projects in the past while positioning your organization to leverage the full potential of AI and advanced analytics.

Next Steps on Your Data and AI Journey

Ready to start your data lake implementation and build your AI foundation? Here are your next steps:

1. Assess your current data assets and identify potential high-value use cases

2. Evaluate cloud providers based on your specific requirements and existing technology stack

3. Start a small proof-of-concept project to demonstrate value

4. Develop a data governance framework that will scale with your initiatives

5. Build internal skills in data engineering, data science, and AI/ML

The organizations that succeed with AI in the coming years will be those that have built robust, flexible data foundations today. Your data lake or lakehouse architecture is not just another IT project—it's the cornerstone of your organization's AI-powered future.

Looking for expert guidance on your data and AI journey? Whether you're just getting started with data lakes or looking to optimize your existing data platform for AI workloads, we're here to help. Connect with us to discuss how we can help you implement effective data strategies tailored to your specific business needs and AI ambitions.

FAQ: Data Lakes, Lakehouses, and Modern Data Architecture

Q: What's the difference between a data lake and a data lakehouse?

A: A data lake is a storage repository that holds vast amounts of raw data in its native format, while a data lakehouse combines the flexibility and cost-efficiency of data lakes with the performance, governance, and reliability features of data warehouses. Lakehouses add structure, ACID transactions, and performance optimizations while maintaining the ability to store diverse data types.

Q: Which open table format should I choose - Delta Lake or Iceberg?

A: Your choice depends on your specific requirements: Choose Delta Lake if you primarily use Spark and the Databricks ecosystem; choose Apache Iceberg if you need multi-engine support and vendor neutrality. Both formats offer ACID transactions and time travel, but they differ in their ecosystem integration and technical implementation. Delta Lake works exceptionally well within the Databricks environment, while Iceberg provides more flexibility across different processing engines.

Q: Can I implement a data lake on-premises, or do I need to use the cloud?

A: Data lakes can be implemented both on-premises and in the cloud. While cloud implementations (using services like S3, Azure Data Lake Storage, or Google Cloud Storage) are more common due to their elasticity and cost-efficiency, on-premises solutions using tools like MinIO or Hadoop are viable options for organizations with specific compliance requirements or existing infrastructure investments.

Q: How much data do I need before a data lake makes sense?

A: While there's no strict threshold, data lakes typically become more valuable when your data volume exceeds 10TB, when you're working with diverse data types (structured, semi-structured, and unstructured), or when you need to support multiple processing engines and analytical workloads. For smaller datasets with well-defined schemas, traditional databases like PostgreSQL might be more appropriate.

Q: What's the "medallion architecture" in data lakes?

A: The medallion architecture is a data organization pattern that defines three processing layers: Bronze (raw data as ingested), Silver (cleansed, validated, and conformed data), and Gold (business-level aggregates and derived datasets). This approach balances the need to preserve raw data with providing optimized datasets for specific use cases, improving both governance and performance.

Q: How do I prevent my data lake from becoming a "data swamp"?

A: Implement robust governance practices from day one: use a data catalog to track your assets, document data lineage, implement data quality checks, establish clear access controls, and organize your data logically. Using structured formats like Delta Lake or Iceberg also helps maintain order by providing schema enforcement and metadata management.

Q: What skills does my team need to implement and maintain a data lake?

A: A successful data lake team typically requires skills in cloud infrastructure, data engineering (ETL/ELT processes), SQL, Python or other programming languages, data modeling, and knowledge of distributed processing frameworks like Spark. As you advance, expertise in ML frameworks and data governance becomes increasingly important.

Q: How do data lakes support AI and machine learning workloads?

A: Data lakes provide the foundation for AI by storing diverse, raw data that can be used to train models, supporting the scale needed for large training datasets, enabling feature engineering at scale, and providing the flexibility to work with various data types. The ability to maintain historical data through features like time travel also helps with model versioning and reproducible ML workflows.

Q: How do data lakehouses compare to traditional data warehouses in terms of cost?

A: Data lakehouses typically offer better cost efficiency than traditional data warehouses through separation of storage and compute (pay only for what you use), more affordable storage costs (especially for historical data), reduced data duplication, and the ability to scale resources based on workload. However, costs can vary significantly based on implementation details and usage patterns.

Q: Can I migrate from a traditional data warehouse to a data lakehouse incrementally?

A: Yes, most organizations adopt an incremental approach to migration. Common strategies include maintaining your existing warehouse while building new use cases in the lakehouse, implementing a hybrid architecture where the lakehouse feeds into the warehouse, or gradually moving specific workloads to the lakehouse based on use case suitability. This allows you to manage risks while demonstrating value.