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Summary – Faced with exponential volume growth and source heterogeneity, a Data Lake without distinct zones, governance, or traceability quickly becomes a data swamp and inflates costs. A modular open source architecture combines continuous ingestion and streaming pipelines, S3-compatible object storage with columnar formats, Medallion layering (Bronze/Silver/Gold), unified batch and streaming processing, centralized orchestration, security, and interactive exploration to ensure performance and compliance.
Solution: Deploy this production-ready blueprint to control your TCO, avoid vendor lock-in, and scale your data platform.
Modern data lakes have evolved beyond mere file repositories into full-fledged platforms capable of ingesting, storing, transforming, orchestrating, and querying large, heterogeneous datasets on a schema-on-read basis.
To avoid the data swamp trap, it’s essential from the outset to define a modular architecture, clear zones (Bronze, Silver, Gold, Sandbox), rigorous governance, and end-to-end lineage. Open source delivers a twofold benefit: it eliminates vendor lock-in and enables independent evolution of storage, compute, and query layers. Before launching an industrialization project, an IT/Finance committee must quantify license savings while forecasting integration, maintenance, and upskilling costs.
Establishing the Foundations of a Modern Data Lake
An agile data structure relies on continuous ingestion and column-optimized storage. It leverages schema-on-read to accelerate availability and minimize upfront transformations.
Scalable Ingestion Strategies
To onboard diverse sources (operational databases, IoT, application logs), it’s crucial to combine streaming tools (Kafka, Debezium) with flow-based pipelines (NiFi). This approach ensures rapid, reliable replication while preserving raw event history. For a deeper dive, see our iPaaS connector comparison.
Kafka handles queuing and buffering, while Debezium captures transactional schema changes. NiFi offers a visual interface for orchestrating, filtering, and enriching streams without custom code.
A mid-sized Swiss industrial firm deployed Kafka and NiFi to ingest real-time data from its PLCs and ERP system. This case illustrates how Bronze zones store raw streams, ensuring full auditability and resilience against load spikes.
Object Storage and Columnar Formats
S3-compatible solutions (MinIO, Ceph) paired with columnar formats (Parquet, ORC, Avro) form the storage backbone. They provide fast read access and effective compression to lower infrastructure costs.
MinIO and Ceph, on-premises or in a private cloud, deliver the horizontal scalability needed for petabyte-scale data. Columnar formats segment data by field and compress low-cardinality regions, boosting analytical performance.
Parquet enables selective column reads, reduces disk I/O, and speeds up query response times. Avro, by contrast, is often used for inter-service exchanges due to its built-in schema evolution support.
Medallion Architecture for Initial Structuring
The Medallion approach segments the data lake into distinct zones: Raw/Bronze for unprocessed streams, Processed/Silver for cleaned and enriched datasets, Curated/Gold for business-ready tables, and Sandbox for ad hoc exploration. This structure prevents confusion and data swamps.
In the Bronze zone, data is retained in its native format. The Silver zone applies quality rules, cleanses, and standardizes, while the Gold zone serves aggregated tables and standardized business views.
The Sandbox zone is reserved for analysts and data scientists experimenting with new models without impacting production pipelines. Each zone has its own access policies and lifecycle settings to optimize retention and security.
Orchestration and Large-Scale Processing
A unified pipeline blends batch and streaming to meet both analytical and operational requirements. Robust orchestration ensures workflow reproducibility and traceability.
Unified Batch and Streaming Processing
Apache Spark and Apache Flink offer engines that handle both batch and stream processing. Spark Structured Streaming and Flink DataStream unify their APIs to simplify development and reduce technical debt.
This convergence allows you to test a job in batch mode, then deploy it as a stream with minimal rewrites. Schema-on-read applies identical transformation rules to both historical and incoming data.
A major Swiss retailer implemented Spark Structured Streaming to aggregate daily sales while processing returns in near real time. This flexibility cut reporting delays by hours and boosted logistics team responsiveness.
Pipeline Orchestration and Automation
Airflow and Dagster orchestrate workflows via DAGs that define dependencies, schedules, and failure-recovery rules. They provide maintenance, alerting, and centralized logs for every run. Learn how platform engineering can strengthen this orchestration.
Airflow boasts a mature ecosystem, diverse connectors, and a powerful monitoring UI. Dagster, newer on the scene, emphasizes code quality, versioning, and native pipeline observability.
In industrial contexts, programmatic scheduling and priority management are vital for meeting Service Level Agreements (SLAs). Orchestration tools incorporate retry, backfill, and self-healing mechanisms to ensure reliability.
Interactive Query and Exploration
Distributed query engines like Trino (formerly Presto), Dremio, or ClickHouse deliver interactive performance on petabyte-scale data. They query Silver and Gold zones directly without massive data copying.
Trino breaks queries into parallel fragments across the compute cluster, while ClickHouse optimizes compression and indexing for ultra-fast scans. A Lakehouse setup with Apache Iceberg or Delta Lake further enhances metadata and transaction management.
Self-service querying enables business users to run ad hoc analyses in seconds without involving data engineering for each new request. Performance remains consistent even under heavy concurrency.
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Governance, Security, and Lineage: Avoiding the Data Swamp
Without strong governance and fine-grained access control, a data lake quickly becomes a data swamp. Lineage of streams and transformations is essential for compliance and reliability.
Data Cataloging and Discovery
DataHub and Amundsen centralize metadata, schemas, documentation, and lineage to simplify asset discovery and understanding. They provide search interfaces, relationship graphs, and consultation APIs. Data lineage further strengthens governance.
Each table, file, and pipeline publishes metadata at write time. Data stewards can then annotate, classify, and tag datasets by sensitivity and business usage.
A Swiss public agency adopted Amundsen to inventory its open data tables, making owners, refresh frequencies, and change history transparent. The project cut support requests related to source unfamiliarity by 40%.
Security and Access Control
Apache Ranger and Knox enforce object-level (files, tables) and API security policies. They manage authentication, authorization, and encryption at rest and in transit. A layered security architecture further fortifies defenses.
Ranger defines fine-grained rules based on user attributes, groups, and execution contexts, while Knox serves as a unified gateway to filter and monitor external calls. Detailed audits log every query and modification.
A Swiss canton implemented Ranger to isolate access to sensitive medical data. This policy ensured regulatory compliance and enabled instant audit reports for oversight authorities.
Observability and Monitoring
Prometheus, Grafana, and the ELK stack deliver metrics, logs, and traces to monitor data lake performance and integrity. They detect ingestion bottlenecks, errors, and schema drifts. DevSecOps best practices are indispensable.
Prometheus collects server and job counters and histograms, Grafana presents real-time dashboards, and ELK indexes application logs for deep and rapid searches during incidents.
In production, a centralized dashboard automatically alerts teams on CPU threshold breaches, pipeline failures, or excessive query latency. Such responsiveness is critical to maintaining business user trust.
Open Source Modularity and Cost Management
Using autonomous open source components lets you evolve storage, compute, and query layers independently. It cuts licensing costs while fostering a replaceable ecosystem.
Decoupling Storage, Compute, and Query
Formats like Iceberg, Delta Lake, and Hudi provide versioning, transactional tables, and time travel without tying storage to a proprietary engine. You can swap compute engines without data migration. See our guide on choosing your data platform.
Iceberg separates the metadata catalog from storage, simplifying partition and index optimizations. Delta Lake, born at Databricks, adds ACID reliability and a vacuum process to purge old files.
This decoupling enables gradual innovation: start with Spark, adopt Flink for specific needs, and conclude with Trino or ClickHouse for querying without major overhauls.
Selecting Open Source Components
Component choice depends on volume, latency, and in-house expertise. Kafka, Spark, Flink, Airflow, Trino, Iceberg, Ranger, and DataHub form a proven modular toolkit.
This composition avoids vendor lock-in and leverages active communities for updates, security patches, and support. Any component can be replaced if a superior project emerges, ensuring long-term sustainability.
Selection follows a proof-of-concept that compares operational cost, performance, and the learning curve for technical teams.
Financial Governance: TCO and Skills
While open source licenses are free, integration, monitoring, and maintenance demand specialized skills. Total cost of ownership includes cluster, storage, network, training, and support expenses.
An executive committee (CIO/CDO/Finance) should forecast these operational costs and plan for upskilling or hiring. Consultants can assist to accelerate ramp-up.
A Swiss IT services firm migrated its proprietary warehouse to an Iceberg-and-Trino architecture. It achieved 70% license savings while investing in team training and a support contract to secure operations.
Move Toward Industrializing Your Modern Data Lake
A production-ready data lake rests on four pillars: continuous ingestion with clear Bronze/Silver/Gold zones; unified batch and streaming processing under orchestration; strict governance ensuring security and lineage; and open source modularity to control TCO. Together, these strategic choices prevent the data swamp and guarantee scalability, performance, and resilience for your data platform.
Whether you’re launching a proof of concept or defining a large-scale strategy, our Edana experts will help tailor this blueprint to your business and technical challenges. Let’s discuss your needs and build the optimal solution to unlock the value of your data.
