Data Lake: flexible storage for Big Data
Schema-on-read for data scientists and machine learning
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Schema-on-read
Structure data at the moment of analysis, not at storage.
All data types
Structured, semi-structured and unstructured in one system.
ML-ready
Directly suitable for machine learning en predictive analytics.
Kostenefficient
Up to 80% cheaper than traditional data warehouses for large volumes.
What is a Data Lake?
A data lake is a centralized repository for large amounts of raw data in their original form. Unlike a data warehouse that works with schema-on-write (structuring data before storage), a data lake uses the schema-on-read principle: data is stored as it comes in and only structured at the moment you analyze it.
The power of raw data
Data lakes zijn ontworpen voor organisaties die werken met diverse databronnen: IoT-sensoren, clickstreams, social media, logbestanden, afbeeldingen en video’s. Al deze data kan in zijn oorspronkelijke formaat worden opgeslagen zonder vooraf te bepalen hoe het gebruikt gaat worden.
This makes data lakes ideal for data scientists who want to perform exploratory analyses, machine learning modellen want to train or discover patterns that remain hidden with traditional BI tools.
De flexibiliteit van een data lake opent deuren die met een traditioneel warehouse gesloten blijven. Denk aan een retailer die camerafoto’s analyseert om winkelgedrag te begrijpen, of een fabrikant die sensordata van productiemachines combineert met onderhoudsrapporten om storingen te voorspellen. Deze use cases vereisen ruwe, onbewerkte data in grote volumes.
Moreover, a data lake offers cost advantages when storing large amounts of data. Where a warehouse needs computing power to structure data during loading, a lake stores everything directly at lower storage costs. Processing only takes place when the data is actually analyzed.
This “store now, analyze later” principle gives organizations the freedom to collect data whose value only becomes clear in the future.
Data Lake architectuur
Ingest
Data flowing in from all sources: batch, streaming, real-time.
Store
Ruwe data opslaan in zones: bronze (raw), silver (cleaned), gold (curated).
Process
Transform and enrich data with Spark, Databricks or similar tools.
Analyze
Generate insights via ML, analytics or export to BI tools.
Data Lake vs. Data Warehouse
The choice between a data lake and a data warehouse depends on your use case. Data warehouses are optimized for structured data and fast SQL queries for BI reporting. Data lakes are ideal for storing large volumes of unstructured data for machine learning and advanced analytics.
In practice we see clear patterns. Organizations where Finance and controlling are the primary data users often benefit more from a warehouse. Businesses with strong data science teams or innovative AI ambitions lean more toward a data lake. The question is not which technology is better, but which fits your organization and objectives.
Also consider the required expertise. A data warehouse requires SQL knowledge and data modeling. A data lake often requires programming skills in Python or Scala and experience with big data frameworks. The availability of these skills within your team or the willingness to invest in them factors into your decision.
More and more organizations choose a hybrid approach: the data lakehouse. This combines the flexibility of a data lake with the query performance of a warehouse.
Data Lake technologie stack
☁️ Cloud Platforms
- Azure Data Lake Storage (ADLS)
- Amazon S3 / AWS Lake Formation
- Google Cloud Storage
- MinIO (on-premise)
- Nederlandse cloud (Leaseweb, KPN)
⚡ Processing Engines
- Apache Spark
- Databricks
- Apache Flink (streaming)
- Presto / Trino
- Dremio
🤖 ML en Analytics
- Python (pandas, scikit-learn)
- TensorFlow / PyTorch
- Azure ML / SageMaker
- Jupyter Notebooks
- Power BI / Tableau
Benefits of a Data Lake
All data, one place
Consolidate data from hundreds of sources in a central, searchable repository.
Toekomstbestendig
Store data without knowing how you will use it later. No lock-in on specific structures.
Schaalbaar
From gigabytes to petabytes without architecture changes. Scale with your data.
Experimenteerruimte
Data scientists can freely experiment without burdening production systems.
Nieuwe inzichten
Ontdek verbanden tussen databronnen die in silo’s verborgen bleven.
Governance
Central access control and audit logging for compliance with AVG en andere regelgeving.
Data Lake in de praktijk
🏭 Predictive Maintenance
Verzamel sensordata van machines en voorspel wanneer onderhoud nodig is. Vermijd ongeplande stilstand en optimaliseer onderhoudsschema’s met machine learning modellen.
🛒 Customer 360
Combine data from CRM, webshop, customer service and social media into a complete customer view. Personalize marketing and improve customer experience.
🏥 Healthcare Analytics
Analyze patient data, medical images and research results. Support diagnoses and discover new treatment patterns with AI.
🚗 Connected Vehicles
Process telemetry data from vehicle fleets. Optimize routes, monitor driving behavior and predict maintenance needs.
🔍 Fraud Detection
Combine transaction data with behavioral patterns and external sources to detect fraud in real-time with machine learning.
📡 IoT Analytics
Collect and analyze data from thousands of sensors. From smart buildings to industriele automatisering.
Voorkom de Data Swamp
A poorly managed data lake can turn into a data swamp: a chaos of uncatalogued, unreliable data that nobody can use. Prevent this by investing from day one in:
- Data catalogus: Know what data you have and where it comes from
- Metadata management: Describe and tag all datasets
- Data quality: Monitor en verbeter datakwaliteit continu
- Access control: Determine who has access to which data
Ready to build your data lake?
From strategy to implementation: we guide your organization toward a scalable, future-proof data architecture.
Frequently asked questions about Data Lakes
What is the difference between a data lake and a data warehouse?
The most important difference is the way data is stored. A data warehouse uses schema-on-write: data is structured before it is stored. A data lake uses schema-on-read: data is stored in raw form and only structured when it is analyzed. Data warehouses are optimized for BI and reporting, data lakes for machine learning and advanced analytics.
What data can I store in a data lake?
Virtually all types of data: structured data (databases, spreadsheets), semi-structured data (JSON, XML, logs) and unstructured data (text, images, video, audio). This makes data lakes ideal for organizations that work with diverse data sources.
What does a data lake implementation cost?
Storage costs are relatively low (often 80% cheaper than warehouse storage), but total costs depend on compute, network and tooling. A basic implementation starts around 15,000-30,000 euros, enterprise implementations range from 50,000 to several hundred thousand depending on complexity and scale. View our transparante kostenbenadering.
How do I prevent my data lake from becoming a data swamp?
Invest in governance from the start: implement a data catalog, define metadata standards, monitor data quality and set clear access controls. Organize your lake in zones (bronze/silver/gold) and document all data pipelines. Read more about preventing a data swamp.
Do I need to choose between data lake or data warehouse?
Not necessarily. Many organizations choose a hybrid approach where both systems exist side by side, or a data lakehouse that combines the advantages of both. The choice depends on your specific use cases and the nature of your data.
Is a data lake GDPR-compliant?
A data lake can be fully GDPR-compliant, provided it is correctly implemented. This requires good access control, encryption, audit logging and the ability to identify and delete personal data. We advise to Nederlandse of Europese hosting te kiezen.
Which skills do I need for a data lake?
Typically data engineers are needed for infrastructure and pipelines (knowledge of Spark, Python, cloud platforms), data scientists for analytics and ML, and data stewards for governance. We can support with expertise and training.
How long does a data lake implementation take?
A basic proof of concept can be done in 4-6 weeks. A production-ready implementation with a few data sources takes 2-4 months. Enterprise implementations with many sources and complex governance can take 6-12 months. We work iteratively so you see value quickly.
About the author
Cost savings and technical specifications are indicative and vary per situation. Contact contact op for a customized estimate.