What happens when your organization collects more data than it can effectively use? For many enterprises, this is not just a hypothetical question but a daily reality. With data pouring in from IoT devices, customer interactions, internal systems, and third-party sources, the sheer volume, variety, and velocity of information can overwhelm traditional storage and analytics systems.
At first glance, the solution seems obvious: centralize everything in a data lake. However, building a data lake that delivers value is no simple task. It's not just about storing data; it's about ensuring it is accessible, organized, and usable. Without proper architecture and governance, a data lake can quickly become a data swamp-disorganized, overwhelming, and ultimately useless.
In this guide, we'll cover the essentials of building a data lake. From architecture to implementation and best practices, data lake consulting is everything you need to know to turn data into actionable insights.
Key considerations before building a data lake
Creating a data lake requires both strategic foresight and technical precision. Without a structured approach, organizations risk building a system that becomes a costly and underutilized "data swamp" with fragmented governance, inefficient workflows, and limited usability. Here's why getting the foundational steps right is critical for long-term success.
Phased implementation
A phased approach is critical to managing the complexity and scale of a data lake project. Rather than attempting an all-encompassing deployment, this strategy focuses on building the data lake incrementally. Initial phases often include ingesting specific, high-priority datasets or proof-of-concept models for selected use cases. Subsequent phases scale operations, adding more data sources, processing capabilities, and user functionalities.
The technical advantage of a phased approach is the ability to validate architecture decisions and uncover potential bottlenecks early. For instance, initial implementations may focus on batch processing of structured ERP data when designing data ingestion pipelines. Insights from this phase can then inform the design of more complex real-time streaming solutions for IoT or CRM data in later stages.
Change management
The introduction of a data lake impacts workflows across the organization. Resistance to change can manifest in various forms, from reluctance to adopt new tools to hesitancy to share data across departments. It all comes down to technical training for data engineers, data scientists, and analysts. Without such training, even the most advanced data lake can become another unused tool. At the same time, clear documentation of governance policies, lineage tracking, and role-based access control can instill confidence in the system's reliability and security.
Agile development
The journey from a basic landing zone to a fully integrated data lake can be challenging, especially without a clear strategy. At N-iX, we employ an Agile development model to streamline the process, focusing on iterative progress and early wins.
Agile development often begins with small, focused projects, such as piloting an ingestion pipeline for a single data source. Feedback from these pilots informs subsequent iterations to refine data processing workflows, schema designs, and metadata management practices. For example, a pilot project might reveal inefficiencies in handling semi-structured data, prompting framework adjustments.
These key considerations form the strategic backbone of the data lake initiative. But while strategy drives the vision, the technical architecture lays the foundation. Let's delve into the critical elements that underpin the building a data lake.
WHITE PAPER
Explore the biggest data engineering trends in 2025—get the white paper!
Success!
The foundation of building a data lake
Building a data lake from scratch is a complex undertaking that requires a clear understanding of its foundational components and how they interact to meet organizational objectives. The architecture must balance scalability, performance, and security while supporting diverse use cases, from advanced analytics to machine learning. Below is a breakdown of the key foundational elements required to establish a robust and efficient data lake.
Object storage or HDFS
Selecting the right storage system for your data lake is one of the most critical architectural decisions. The choice between Object Storage and Hadoop Distributed File System (HDFS) impacts performance and how your data lake integrates with downstream workflows and analytics.
Cloud-based object storage solutions, such as Amazon S3, Azure Blob Storage, and Google Cloud Storage, have become the standard for modern data lakes. Their scalability and durability are unmatched, offering features like elastic capacity and high data redundancy. Data integrity is virtually guaranteed, with durability rates reaching exceptionally high thresholds.
However, HDFS remains relevant in hybrid architectures, particularly for scenarios requiring consistent and high-performance I/O. While object storage is immutable and optimized for storing inputs and final processed data, HDFS can be used as a high-performance cache for intermediate processing results. Its tightly coupled architecture supports append and truncate operations, which object storage does not natively handle.
By combining object storage and HDFS, organizations can balance scalability and cost efficiency with performance optimization. Object storage can be a persistent layer for long-term data retention, while HDFS can enhance computational performance for workloads that demand frequent read/write operations. Meanwhile, HDFS still presents scalability challenges, as expanding capacity requires adding nodes or upgrading drives, making it less flexible than its cloud-based counterpart for storage growth.
Storage
The storage layer in a data lake supports various use cases across data ingestion, processing, and analysis. A well-architected storage system often follows a tiered approach, commonly referred to as the Medallion Architecture, to organize data based on its quality, validation, and intended use. For example:
- Bronze (Raw Data) zone: Stores unprocessed data exactly as ingested, preserving its original state for auditing and reprocessing.
- Silver (Trusted Data) zone: Contains validated and cleaned datasets ready for integration into downstream systems.
- Gold (Refined Data) zone: Places structured, processed data optimized for analytics and ML applications.
Data engineers subdivide these zones into use-case-specific partitions, such as staging areas for data warehouses or training datasets for AI models. Columnar formats like Parquet are ideal for analytical workloads, offering efficient compression and optimized query performance. Meanwhile, row-based formats, such as Avro, are well-suited for applications requiring frequent write operations and schema evolution support. Human-readable formats (CSV and JSON) remain valuable for debugging and small-scale data exploration.
Computing
Compute resources connect data ingestion and storage with analytics and consumption. Dynamic scalability is a defining characteristic of modern computing solutions. This elasticity is critical for handling fluctuating demands, such as seasonal analytics or experimental data science workflows. Practical compute configurations also incorporate data locality optimization, ensuring processing resources are as close to the data as possible.
Modern data lakes leverage multiple compute paradigms to accommodate different workloads:
- SQL-based transformations allow users to perform transformations using familiar syntax in cloud-native environments.
- Cluster-based frameworks like Apache Spark or Hadoop are ideal for large-scale data processing. These frameworks distribute tasks across multiple nodes, enabling parallel processing of massive datasets.
- Serverless functions offer an event-driven computing model. They are particularly effective for lightweight transformations and real-time data streaming, as they allocate resources dynamically based on demand.
Governance
A robust governance framework is necessary to maintain data quality, secure sensitive information, and comply with regulatory standards. Without governance, even the most sophisticated computing layers can become ineffective due to unreliable or inaccessible data.
Key aspects of governance include:
- Access Control frameworks ensure only authorized users can view or modify specific datasets.
- Metadata catalogs document key details about datasets, including schema, source, and purpose.
- Encryption standards protect data at rest, while protocols secure data in transit using advanced features like tokenization and masking.
- Compliance automated tools embedded in the governance layer streamline processes like data anonymization, retention management, and consent tracking.
Data quality
Data quality involves creating an environment where data is consistently reliable, accurate, and structured to meet diverse use cases. Modern tools such as Apache Griffin can validate incoming data against pre-defined quality rules and identify anomalies or deviations that could compromise downstream analytics or ML models. To comply with stringent privacy regulations, data lake experts implement dynamic masking mechanisms, detect PII during ingestion, and apply masking policies based on roles or usage contexts.
Data security
Learning how to create a data lake requires a comprehensive approach that addresses risks across all architectural layers, including user access, system integration, and real-time monitoring. Data immutability and network isolation further strengthen the security framework. Technologies like Delta Lake enforce immutable storage controls when transaction logs maintained within these systems provide detailed records of all interactions. Complementing this, network isolation via Virtual Private Clouds reduces exposure to external threats by containing data traffic within private, secure channels.
Data catalog
A data catalog is the operational foundation of a data lake. Modern data catalogs automate metadata harvesting and dynamically update as new datasets are ingested. Tools such as AWS Glue and Apache Atlas enrich datasets with detailed metadata, including schema definitions, lineage, and quality metrics.
Machine Learning capabilities further enhance the catalog by profiling datasets to uncover patterns, relationships, and sensitivities. For example, ML models can predict metadata tags or classify sensitive information. Integration with query engines allows users to explore datasets through lightweight SQL queries, validating their relevance before resource-intensive processing.
Data lineage
Data lineage provides a comprehensive view of a dataset's lifecycle, capturing its origins, transformations, and destinations. Technically, lineage is visualized as a directed graph that maps relationships between datasets and workflows. Building a data lake, engineers automate the tracking of every action, from ingestion to analysis, reducing oversight risks and simplifying audits. These systems are particularly valuable for debugging and optimization, enabling errors to be traced to specific input data or transformation logic.
Orchestration
Data orchestration seamlessly coordinates workflows across ingestion, transformation, and processing stages. For example, it checks raw data ingestion is completed before transformation and analysis tasks begin. Modern orchestration tools improve efficiency through dynamic scaling, adjusting compute resources based on workload demands. A surge in data ingestion during peak hours automatically allocates additional processing capacity, while scaling down during off-peak hours reduces operational costs.
Building a robust enterprise data lake relies on these foundational components working seamlessly together. Each element, from storage and computing to governance and orchestration, is critical in ensuring scalability, security, and performance for diverse workloads.
Data lake schemas and architecture designs
In this section, we'll get down to data lake schemas and architecture types, exploring how they structure and manage data to support diverse business needs. A well-designed schema and architecture are vital for optimizing data storage, accessibility, and governance.
Data lake schemas
Data lakes are designed to handle diverse raw data formats, from structured to unstructured, without imposing a rigid schema at ingestion. This flexibility comes at a cost-without proper schema planning. For this reason, schema designs play a crucial role in maintaining structure and usability within the data lake.
Flat schema
Flat schemas are the most straightforward approach to data organization in data lakes. Data is ingested without predefined relationships, leaving datasets in their raw, unstructured form. For example, a flat schema might store IoT sensor data in large JSON files without further segmentation. While this method speeds up ingestion and preserves the data's original state, querying the data often requires additional transformations, which can be resource-intensive.
Pros:
- Simplifies ingestion by storing data as-is.
- Preserves the original format for flexibility in reprocessing.
- Supports diverse data types without predefined constraints.
Cons:
- Querying raw data can lead to inefficiencies in analytics workflows.
- Lacks structure, making data discovery and governance more challenging.
- Increased storage and compute costs for processing unoptimized formats.
Hierarchical schema
The hierarchical schema introduces structure to data lakes by segmenting data into zones based on its lifecycle and processing requirements. A healthcare organization, for instance, might store raw patient records in the raw zone, clean and validate them in the trusted zone, and produce aggregated reports for analysis in the refined zone.
Pros:
- Facilitates governance by segmenting data based on processing stages.
- Optimizes performance for analytics through refined, use-case-specific zones.
- Enhances security with zone-specific access controls.
Cons:
- Requires careful planning and management of zones.
- Introduces additional complexity in defining workflows and transitions between zones.
Schema-on-read
Schema-on-read allows flexibility by deferring schema application until data is queried. This approach enables data lakes to ingest diverse data types without modification, applying structure dynamically when needed. For instance, data scientists analyzing semi-structured JSON files might define schema attributes on the fly, depending on the analytical use case.
Pros:
- Provides maximum flexibility for analytics and exploration.
- Avoids constraints during ingestion, supporting diverse data formats.
- Allows dynamic adaptation to new use cases and analytical demands.
Cons:
- Requires robust query engines to manage dynamic schema applications.
- Potentially results in slower query performance if metadata is incomplete or outdated.
Read more: Data lakehouse architecture: How to build your data foundations for AI
Data lake architecture design types
Zone architecture
Zone architecture provides a structured approach to organizing data within a data lake. At its core, zone architecture is designed to support a clear data lifecycle. Each zone is configured with specific metadata management tools, storage mechanisms, and user interfaces to accommodate its unique role.
- The Raw Zone is the initial repository for ingested data, storing it in its native format. This zone is particularly valuable for compliance and reproducibility, allowing organizations to reprocess data as new techniques or methods emerge.
- The Refined Zone is where preprocessed data transitions for cleaning, enrichment, and transformation. This intermediate zone is a staging ground to prepare data for advanced use cases.
- The Trusted Zone applies to data that requires additional oversight, such as sensitive or regulatory-compliant datasets. This zone enforces strict governance measures, ensures policy adherence, and mitigates data breaches or misuse risks.
- The Exploration Zone supports advanced analytics and data science experiments. Here, users can manipulate data without impacting production workflows.
Lambda architecture
Lambda architecture enhances a data lake's capability to handle batch and real-time data processing, addressing the growing demand for near-instantaneous insights. Modern implementations of the Lambda architecture leverage cloud-native services to optimize resource allocation and minimize latency. This architecture ensures robust data management while supporting diverse use cases for building a data lake.
- The Batch Layer ingests raw data into the data lake and processes it using pre-defined workflows. This layer emphasizes data consistency, allowing organizations to generate comprehensive historical views for deep analysis.
- The Speed Layer processes data streams in near real-time, enabling applications such as fraud detection, anomaly monitoring, and real-time dashboards. Cloud-native services facilitate the ingestion and processing of high-velocity data streams.
- The Serving Layer integrates outputs from batch and speed layers to provide a unified interface for querying and analysis. This layer ensures that historical and real-time data are accessible seamlessly, supporting dynamic business needs.
FAIR digital object-based architecture
The FAIR (Findable, Accessible, Interoperable, Reusable) Digital Object-based architecture eschews traditional zoning in favor of a flat, uniform structure. This approach encapsulates data and workflows into self-describing objects, each containing metadata specifying its type, lineage, and applicable governance rules.
Unlike zone-based architectures, which organize data by processing stages, FAIR architecture achieves granularity through typed attributes. For instance, a dataset might have distinct types, such as Scanner-XRaw for raw data and Scanner-XPreprocessed for refined data. This fine-grained partitioning enables detailed data management without increasing system complexity.
How to build a data lake: N-iX approach
Implementing a data lake is a progressive and adaptable process, much like digital transformation, and doesn't demand a significant upfront investment. Below is an outline of the key steps that our engineers follow to develop a high-performing data lake.
Step 1: Setting up a data lake
The first step in building a data lake involves defining and aligning business requirements with organizational goals. We guarantee the data lake supports key use cases, enabling real-time analytics, ML, or long-term data retention. Understanding how to build a data lake starts with enterprise precise requirements that guide architectural decisions, such as choosing storage systems and integration frameworks, which are critical for building a scalable and efficient data lake.
Depending on the use case, data lakes can be constructed using object storage systems (like Amazon S3, Azure Blob Storage, or Google Cloud Storage) or Hadoop Distributed File System for on-premises or hybrid environments. Both options offer scalability and seamless integration with existing enterprise data and tools. Object storage excels in cloud-native environments with its durability and elastic scalability, while HDFS remains effective for high-performance, tightly coupled workloads.
N-iX specializes in building data lake solutions using leading platforms and services:
Our expertise spans diverse platforms and tools, ensuring tailored solutions that address each organization's unique needs. One example is our partnership with Lebara, one of Europe's fastest-growing telecom providers. We implemented an Azure-based data lake to replace their legacy system. This enabled near-real-time data streaming and significantly improved report generation across departments. Our modernized approach eliminated delays, enhanced scalability, and provided actionable insights that supported business operations in six countries.
Read more: Faster time-to-market with full-scale digital transformation in telecom
Step 2: Establishing the landing and raw data zone
Once business requirements are established, our team creates the Landing and Raw Data Zone to create a scalable, cost-efficient data lake. By designing this layer to operate independently of core IT systems, we reduce disruptions to existing infrastructure while ensuring seamless compatibility with diverse data formats, including structured datasets from relational databases and unstructured content like log files and multimedia.
To maintain usability and prevent the data lake from devolving into a "data swamp," we integrate robust governance practices from the beginning. Our team enforces detailed data tagging and classification mechanisms aligned with organizational objectives. Additionally, we prioritize metadata management, ensuring all ingested data remains discoverable, traceable, and ready for future analytical use. By combining a deep understanding of source data with strong governance, we set a solid foundation for the data lake's evolution.
Step 3: Ingesting data
Our focus shifts to designing data ingestion pipelines to handle data from multiple sources while ensuring consistency and scalability. We create workflows that efficiently process scheduled transfers from ERP systems, CRM platforms, and IoT devices for batch ingestion. Creating a data lake, we implement event-driven pipelines for real-time ingestion using advanced tools such as Apache Kafka or AWS Kinesis, ensuring that continuous data streams are seamlessly captured and processed.
To streamline usability, we establish clear and consistent storage conventions, organizing datasets with naming structures that align with metadata systems. These conventions simplify navigation across production, development, and testing environments. Additionally, we integrate reference data-customer directories and product taxonomies-into the ingestion framework, ensuring workflows remain cohesive and aligned with broader analytics objectives.
Step 4: Developing a data science sandbox environment
After data ingestion is complete, we establish scalable and adaptive infrastructures using AWS S3, Databricks, and Azure Data Factory platforms. These tools allow seamless access to data in various formats-structured, semi-structured, and unstructured-ensuring that no data source is untapped. Integrated metadata systems powered by AWS Glue enrich datasets with context, making them easy to locate and use. This streamlined setup accelerates experimentation and ensures that data scientists can focus on solving business challenges, not searching for or cleaning data.
Equally important is the role of schema design in structuring the data lake to meet specific business needs. We design schemas that align with the organization's objectives, optimizing the data lake for analytics and compliance. We eliminate redundancies by organizing datasets into logical categories and creating a foundation supporting exploratory analysis and production-level workflows.
Step 5: Implementing a data lake architecture
This step builds on the foundation established in earlier phases, where ingested data is organized, refined, and prepared for scalable use cases. Once the data has been transformed and validated, the next step involves structuring it into a logical architecture that ensures accessibility, governance, and performance. Our team employs best practices to build an architecture that supports diverse workloads, including batch processing, real-time analytics, and machine learning applications. We also emphasize metadata-driven designs that enable dynamic data exploration and lineage tracking.
Once the architecture is in place, the focus shifts to embedding the data lake within the organization's broader data ecosystem. We ensure that the data lake evolves into a centralized platform that supports "data as a service" for various business functions.
Step 6: Accelerating time to decisions
We focus on structuring data lakes to maximize accessibility and usability for diverse business teams. Our solutions include integrating business intelligence tools and ensuring seamless insight access through intuitive dashboards and real-time data visualizations.
To achieve high-performance analytics, we optimize data consumption workflows to align with organizational goals. The process consists of refining data structures to support dynamic querying while maintaining low-latency access for machine learning applications. Predictive analytics, for example, can utilize streaming data for real-time forecasting, while batch analytics can uncover long-term trends through historical data stored within the lake.
Wrapping up
With the right architecture, governance, and tools, your data lake can evolve into a hub of discovery, where raw data is transformed into a strategic advantage. The journey might seem complex, but with expert guidance and a clear vision, it's a path that leads to real innovation and measurable impact.
If building a data lake has been on your business agenda, there's no better time to start. Creating a scalable, efficient, and future-ready data lake is a step-by-step process, and we at N-iX are here to guide you every step of the way. With a team of 200+ skilled data experts and partnerships with leading cloud providers such as AWS, Microsoft Azure, Google Cloud, and Snowflake, we provide end-to-end services tailored to your needs.
Have a question?
Speak to an expert
