How to build a data lake: Architecture and best practices for modern data

A company can collect years of transaction records, application logs, customer events, sensor feeds, documents, images, and operational data without becoming any better at using them. The problem is often not the amount of data. It is where the data lives and how difficult it is to access.

A data lake addresses this problem by creating a shared environment for storing large volumes of structured and unstructured data without forcing every data source into the same schema before storage. A data lake is a centralized repository designed to store raw data at scale. The lake is a centralized repository in the architectural sense, even when the physical data sits across several storage services or cloud accounts.

That flexibility makes data lakes useful for data analytics, data science, machine learning, AI, archival workloads, and exploration of new data sources. It also creates a risk. Without ownership, metadata, access rules, and data quality controls, a data lake can become a data swamp that contains vast amounts of data nobody trusts or understands. Avenga’s data services cover data architecture, platforms, engineering, governance, analytics, and AI systems built around enterprise data.

Key takeaways

• A data lake stores diverse data with minimal transformation at ingestion. Raw data can remain available for future processing and new use cases.
• Architecture matters more than storage capacity. Ingestion, metadata, security, processing, governance, and consumption need to work together.
• Cloud object storage is usually the storage foundation. Compute and storage can scale separately as the volume of data changes.
• Data quality still matters. A lake should preserve raw data while also creating trusted layers for analytics and machine learning.
• A data catalog is not optional at scale. Teams need to know what data exists, where it came from, who owns it, and how it changed.
• A lakehouse adds stronger data management controls. Data lakehouse designs combine lake storage with features historically associated with a data warehouse.

What is a data lake?

A data lake is a storage and processing architecture for keeping large amounts of data in its native format or with minimal initial transformation. A company can ingest data from multiple sources, including:

• Relational databases
• Business applications
• SaaS tools
• Files
• APIs
• Internet of Things devices
• Application logs
• Streaming data
• Images and media
• JSON and other semi-structured formats

The main difference from a traditional data warehouse is when the data structure is imposed. A data warehouse usually stores structured data prepared for defined reporting or business intelligence workloads. A data lake can retain raw data before teams know every future question they may want to ask.

Current data lake architecture guidance recommends data lakes for exploratory analytics, advanced data science, and machine learning where teams need access to diverse data types and schema-on-read flexibility. This does not make the data warehouse obsolete. Data lakes and data warehouses frequently coexist in a modern data architecture.

Data lake architecture: Core components

A useful data lake architecture includes more than storage. At minimum, it needs six connected areas.

Architecture component	Purpose
Data sources	Business applications, databases, devices, files, APIs, and external systems
Data ingestion	Batch loads, CDC, events, and streaming pipelines
Data lake storage	Raw, processed, and curated data
Catalog and metadata	Discovery, ownership, schemas, lineage, and classification
Processing	Data engineering, transformation, enrichment, and quality controls
Consumption	BI, analytics, AI, machine learning, and data applications

Modern cloud data lake architecture usually separates compute from data storage. That allows organizations to store data at relatively low cost while assigning compute resources only when they need to process or analyze data. Current data lake architecture guidance also recommends designs that separate data producers and consumers, with a centralized catalog helping teams share and access data without forcing everything through a single pipeline.

Build a data lake using a layered architecture

Building a modern data lake usually means creating several data layers rather than placing everything into one folder or object-storage bucket. A common pattern is the bronze, silver, and gold architecture.

Bronze: Raw data

The bronze layer stores raw data as it arrives from the data source. Transformation should remain minimal. This layer gives teams the ability to:

• Rebuild downstream datasets
• Investigate errors
• Reprocess historical data
• Retain source-level evidence

Raw data in its native format is useful because future requirements may differ from today’s.

Silver: Cleansed data

The silver layer applies validation, standardization, deduplication, transformation, and data quality rules. This is where teams begin turning diverse data into reliable datasets that analysts and downstream applications can use.

Gold: Business-ready data

The gold layer organizes data for specific business or analytical purposes. Examples include:

• Customer metrics
• Revenue reporting
• Operational dashboards
• ML features
• Financial aggregates

Current architecture guidance recommends this layered approach, with data quality increasing as data moves from raw to curated layers. The same pattern works outside Databricks. The principle is more important than the product name: preserve source data, create trusted intermediate datasets, and expose business-ready outputs separately.

How to build a data lake step by step

A data lake implementation should begin with business and data requirements rather than cloud storage configuration.

1. Define the use cases

Decide why the organization needs a data lake. Typical use cases include:

• Big data analytics
• Data science
• Machine learning
• AI
• Customer analytics
• IoT analysis
• Fraud detection
• Historical analysis
• Log analytics
• Research

Do not start by trying to collect data from every system. A defined use case tells the team which data to ingest first.

2. Inventory the data sources

Document where the data comes from. For each data source, record:

• Owner
• Data type
• Volume
• Update frequency
• Sensitivity
• Retention requirements
• Expected consumers

This prevents the architecture from becoming a collection of undocumented data pipelines.

3. Choose cloud data storage

Cloud object storage is often the foundation for a cloud data lake. Common options include Amazon S3, Azure Data Lake Storage, and Google Cloud Storage. Azure Data Lake, for example, builds data lake capabilities on Azure Storage and supports hierarchical organization, large-scale analytics, and access controls. The important decision is not simply which cloud provider to select. The architecture should support the expected data volume, security model, processing engines, cost profile, and future consumers.

4. Design data ingestion

A data lake may need several ingestion patterns. Batch ingestion works for periodic database exports and files. Change data capture can move database changes incrementally. Data streaming supports real-time data from applications, devices, or event systems. A single data lake can use all three. The data pipeline should also capture metadata about when data arrived, where it came from, and whether ingestion succeeded.

5. Add a data catalog

A data catalog helps users discover and understand what is available. It should record information such as:

• Dataset name
• Owner
• Schema
• Business definition
• Sensitivity
• Data lineage
• Update frequency
• Access rules

On AWS, the AWS Glue Data Catalog can provide centralized metadata for data stored across analytics services. A catalog helps data scientists and analysts find existing datasets instead of creating more data silos.

6. Add governance and security

A data lake requires defined rules for access, classification, retention, encryption, and audit. The governance model should answer:

• Who can ingest data?
• Who can access data?
• Who owns each dataset?
• Which information is sensitive?
• What must be encrypted?
• How long should data remain?
• Which changes require approval?

Avenga’s cybersecurity services can support access controls, security architecture, monitoring, and cloud security around data platforms.

7. Build consumption paths

Do not make every user query raw storage directly. Provide suitable access methods for different groups. Data scientists may need notebooks and ML tools. Analysts may use SQL. Applications may consume APIs or curated datasets. BI tools may access prepared tables. Good architecture keeps storage flexible while making consumption controlled.

Build a governed data platform that connects ingestion, trusted datasets, analytics, and AI.

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Data lake best practices

The technology used to build your data lake matters less than several operating practices.

Keep raw data immutable

Preserve an original copy where practical. If a processing mistake occurs, teams can rebuild downstream datasets instead of returning to every source system.

Separate raw and trusted data

Do not let analysts guess whether a dataset is validated. Clear zones or layers make data quality visible.

Track data lineage

Data lineage records how a dataset moved from its original data source through transformation and consumption. This matters for debugging, audit, compliance, and AI.

Automate data quality checks

Data quality should cover completeness, validity, consistency, freshness, and expected values. Current data governance guidance recommends profiling, cleansing, validating, and monitoring datasets rather than treating quality as a one-time activity.

Design for failure

Data pipelines break. Sources change schemas. APIs time out. Files arrive late. Streaming systems produce duplicates. Make ingestion idempotent where possible, log failures, and define how the system recovers.

Monitor cost

Cloud data storage is relatively inexpensive, but data processing can become costly. Track:

• Storage growth
• Compute usage
• Data transfer
• Duplicate data
• Idle clusters
• Expensive queries

A data lake solution should scale technically and economically.

Data lake vs data warehouse vs data lakehouseData lake vs data warehouse vs data lakehouse

These terms describe different approaches rather than strict competitors.

	Data lake	Data warehouse	Data lakehouse
Data	Structured and unstructured data	Mainly structured data	Structured and unstructured data
Storage	Large-scale object storage	Managed analytical storage	Lake storage with table management
Schema	Often schema-on-read	Usually schema-on-write	Supports both patterns
Main users	Engineers, data scientists, analysts	Analysts and BI users	Analysts, engineers, and data scientists
Workloads	AI, ML, exploration, big data	Reporting and BI	AI, ML, BI, and data engineering

A data lakehouse attempts to combine the flexibility of data lakes with the management and performance features historically associated with warehouses. A 2025 market guide for data lakehouse platforms describes the lakehouse as a converged architecture intended to support a wider range of analytical workloads through one foundational data store. The right approach depends on workload and existing data infrastructure. A company does not need to choose one label for every use case.

Challenges of data lakes

The flexibility of data lakes creates most of their risks.

The data swamp problem

If teams collect data without metadata, ownership, or quality controls, users eventually stop trusting the platform. A large data lake nobody understands has little business value.

Weak data quality

Raw data is useful, but raw data should not automatically become reporting data. Curated layers need validation.

Difficult discovery

Without a catalog, employees may not know that a dataset already exists. They collect the same information again and create more duplication.

Uncontrolled access

Centralized data can become dangerous when permissions are broad. Sensitive datasets need classification and access rules.

Growing complexity

As new data sources, data pipelines, analytics tools, and AI workloads arrive, architecture can become difficult to manage. This is why data management needs operating ownership rather than one implementation project.

A data lake is useful when teams can find data, understand where it came from, and know whether they can trust it. Storage is the easy part. Architecture, ownership, quality, and governance are what turn large amounts of raw data into something people can actually use.

Petyo Dimitrov, Director of Data and AI at Avenga

Building a modern data lake for AI and machine learning

AI makes the quality of data infrastructure more visible. Data science and machine learning teams often need large volumes of diverse data, including structured and unstructured data. A modern data lake can give them access to:

• Historical transactions
• Documents
• Images
• Logs
• Behavioral events
• Sensor data
• External datasets

But collecting data is not enough. Teams need reliable metadata, permissions, lineage, reproducible transformations, and clear versions of training datasets. Avenga’s AI services can connect data platforms with AI systems where models depend on governed enterprise information. Data lakes enable experimentation because data scientists can use data without forcing every dataset into a predefined analytical schema first. The same flexibility needs controls to ensure your data remains understandable as AI use grows.

FAQ

How do you create a data lake?

To create a data lake, define the first business use cases, identify data sources, choose cloud or on-premises storage, design ingestion, organize storage layers, add a data catalog, establish governance, and provide access methods for analytics and AI. Start with a bounded dataset rather than attempting to ingest the entire organization at once.

Can you build your own data lake?

Yes. Organizations can build a data lake using cloud object storage, open-source software, processing engines, catalogs, security tools, and orchestration platforms. The harder part is not the storage technology but operating the architecture, governance, data quality, and pipelines over time.

What are the disadvantages of a data lake?

The main challenges of data lakes include poor data quality, weak discovery, duplicated datasets, uncontrolled access, growing processing costs, and the risk of creating a data swamp. Metadata, governance, ownership, monitoring, and layered architecture reduce these risks.

Is a data lake an ETL tool?

No. A data lake stores and organizes data, while ETL and ELT tools move and transform data. Data ingestion and processing tools feed data into the lake and prepare it for downstream analytics, AI, applications, or a data warehouse.

Conclusion: A useful data lake is more than cheap storage

It is technically easy to store massive volumes of data. It is much harder to make data usable six months later. A successful data lake needs a clear architecture for ingestion, storage, cataloging, processing, governance, and consumption. It should preserve raw data while creating trusted layers for people and systems that need reliable information.

Start with a business use case. Build the smallest useful data platform around it. Make ownership and lineage visible. Automate quality checks. Expand as new data sources and consumers appear. That is how a cloud data lake becomes part of a modern data platform instead of another place where information disappears. If your organization is planning a new data lake, data lakehouse, or broader cloud data platform, contact Avenga to discuss the engineering work.