Structured project approach for your data platform
Data Lake Implementation
Transform your data landscape with a tailored Data Lake solution. We support you in the successful implementation of a flexible, future-proof Data Lake — from strategic planning through technical implementation to productive operations and continuous expansion.
- ✓Proven implementation methodology for fast and sustainable results
- ✓End-to-end support from requirements analysis through to productive operations
- ✓Integration of existing data sources and legacy systems into modern Data Lake architectures
- ✓Building internal competencies for sustainable operations and further development
Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
30 Minutes • Non-binding • Immediately available
For optimal preparation of your strategy session:
- Your strategic goals and objectives
- Desired business outcomes and ROI
- Steps already taken
Or contact us directly:
Certifications, Partners and more...
Delivering Data Lake Projects with Structure and Speed
Our Strengths
- Comprehensive expertise in modern Data Lake technologies and cloud platforms
- Proven implementation methodology with demonstrable successes
- Interdisciplinary team of Data Engineers, architects, and business consultants
- Vendor-independent consulting and tailored solution concepts
⚠
Expert Tip
The key to a successful Data Lake implementation lies in a balanced relationship between quick wins and strategic, long-term alignment. Our experience shows that an MVP approach (Minimum Viable Product) with a clearly defined, value-creating use case significantly increases the probability of success. Such a "lighthouse use case" not only creates early successes, but also helps to overcome organizational hurdles and gain important learnings for later project phases.
ADVISORI in Numbers
11+
Years of Experience
120+
Employees
520+
Projects
Our proven methodology for Data Lake implementation combines strategic planning, agile development, and continuous improvement. This structured approach ensures that your Data Lake is not only technically sound, but also meets business requirements and is accepted by users.
Our Approach:
Phase 1: Assessment & Strategy - Analysis of the existing data landscape and processes, definition of strategic goals and prioritized use cases, creation of a Data Lake roadmap
Phase 2: Architecture & Design - Development of a future-proof Data Lake architecture, selection of appropriate technologies, definition of data models and governance frameworks
Phase 3: MVP Implementation - Agile delivery of a Minimum Viable Product with the first prioritized use cases, build-out of core infrastructure, integration of initial data sources
Phase 4: Scaling & Expansion - Incremental extension with additional data sources and use cases, performance optimization, expansion of self-service capabilities
Phase 5: Operations & Continuous Improvement - Establishment of operational processes, knowledge transfer, continuous development and optimization of the Data Lake
"A successful Data Lake implementation is a balance of technological expertise and organizational change management. The decisive factor is not the technology itself, but how it is integrated into the organizational reality and delivers genuine value to business units. Our approach therefore combines technical excellence with a pragmatic methodology and intensive involvement of business stakeholders."
Asan Stefanski
Head of Digital Transformation
Expertise & Experience:
11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI
Our Services
We offer you tailored solutions for your digital transformation
Data Lake Consulting & Strategy
Development of a tailored Data Lake strategy with a clear roadmap, prioritized use cases, and technology recommendations. Our experienced consultants support you in defining a future-proof vision for your Data Lake and planning the necessary steps to realize it.
- Assessment of your existing data landscape and identification of optimization potential
- Definition and prioritization of use cases with measurable business value
- Development of a technical target architecture and technology recommendations
- Creation of an implementation roadmap with milestones and resource planning
Technical Data Lake Implementation
Professional implementation of your Data Lake based on modern technologies and best practices. Our experienced Data Engineers and cloud specialists implement your Data Lake architecture efficiently and in a future-proof manner — whether on-premise, in the cloud, or as a hybrid solution.
- Build-out of the Data Lake infrastructure (storage, compute, networking)
- Development and implementation of data pipelines for various data sources
- Integration of data processing frameworks for batch and stream processing
- Implementation of security and governance mechanisms
Data Integration & Migration
Smooth integration of your existing data sources and legacy systems into your new Data Lake. We develop reliable, flexible data pipelines that collect, transform, and make available data from a wide variety of sources in your Data Lake.
- Development of ETL/ELT processes for structured and unstructured data
- Integration of legacy systems and enterprise applications
- Implementation of Change Data Capture (CDC) for real-time data integration
- Data migration from existing data warehouses and data platforms
Data Lake Governance & Operations
Establishment of sustainable governance structures and operating models for your Data Lake. We support you in implementing the necessary processes, roles, and tools to ensure the long-term quality, security, and value of your Data Lake.
- Development of Data Governance frameworks and policies
- Implementation of metadata management and data cataloging
- Build-out of monitoring, logging, and alerting systems
- Definition of operational processes and training of your teams
Our Competencies in Data Engineering
Choose the area that fits your requirements
Data Lake Setup
Unlock the full potential of your data with a modern Data Lake architecture. We support you in designing and implementing a flexible data infrastructure that integrates diverse data sources and makes them optimally available for analytics applications.
Data Quality Management
Establish systematic data quality management that ensures the consistency, correctness, and completeness of your data. Our tailored solutions help you detect data issues early, resolve them, and prevent them sustainably – providing trustworthy information as the basis for your business decisions.
ETL (Extract Transform Load)
Develop robust, scalable ETL processes that extract data from diverse sources, transform it, and load it into your target systems. Our ETL solutions ensure your analytics systems are always supplied with current, high-quality, and business-relevant data.
Master Data Management
Establish a strategic master data management approach that guarantees consistent, up-to-date, and high-quality master data across all areas of your organization. Our tailored MDM solutions create the foundation for well-informed business decisions, efficient processes, and successful digitalization initiatives.
Frequently Asked Questions about Data Lake Implementation
What does a typical data lake implementation project look like?
A data lake project follows several phases: an initial assessment of existing data sources and business goals, architecture design with technology selection, an MVP phase implementing a first use case for quick results, and iterative expansion with additional data sources and applications. Governance structures and operational processes are established in parallel. This iterative approach reduces risk and delivers measurable value early.
How much does a data lake implementation cost?
Costs depend on scope, technology choice and company size. A cloud-based MVP project typically starts in the mid five-figure range. Ongoing cloud costs begin at a few hundred euros per month and scale with data volume. Key cost drivers are the number of data sources, required processing speed and governance requirements. A consumption-based cloud model avoids high upfront investments.
How long does it take to implement a data lake?
An MVP with a first use case can be delivered in
8 to
12 weeks. A full implementation with multiple data sources, governance and self-service access takes
6 to
12 months. Factors like source system complexity, regulatory requirements and existing cloud infrastructure affect the timeline. The MVP approach ensures usable results within weeks.
Which technologies are used for data lake projects?
Common platforms include AWS (S3, Glue, Lake Formation), Microsoft Azure (Data Lake Storage Gen2, Synapse) and Google Cloud (BigQuery, Dataproc). Apache Spark, Kafka and dbt handle data processing. Table formats like Delta Lake, Apache Iceberg or Hudi enable ACID transactions. The choice depends on existing IT landscape, team skills and specific requirements.
What is the difference between data lake architecture and implementation?
Architecture covers the design and planning of a data platform. Implementation encompasses the actual project: data migration, pipeline development, integration of existing systems, testing and go-live. At ADVISORI we cover both phases. Implementation builds on the architecture and turns the design into a productive, scalable solution with defined operational processes.
How is existing data migrated to a new data lake?
Migration happens incrementally: data sources are prioritized and migration pipelines built. Batch processes suit historical data while Change Data Capture handles ongoing synchronization. Validation steps ensure data quality. Running old and new systems in parallel during the transition minimizes risk. Migration proceeds in iterations, starting with the most valuable data sources.
What mistakes should be avoided in data lake projects?
The most common mistakes: too large a scope without an MVP phase, missing data governance from the start, prioritizing technology over business value, and insufficient involvement of business departments. Without a clear metadata strategy the data lake becomes an unusable data swamp. Inadequate monitoring and missing data quality checks in pipelines also cause problems. An iterative approach with close business involvement prevents these pitfalls.
Success Stories
Discover how we support companies in their digital transformation
Digitalization in Steel Trading
Klöckner & Co
Digital Transformation in Steel Trading
Case Study
Results
Over 2 billion euros in annual revenue through digital channels
Goal to achieve 60% of revenue online by 2022
Improved customer satisfaction through automated processes
AI-Powered Manufacturing Optimization
Siemens
Smart Manufacturing Solutions for Maximum Value Creation
Case Study
Results
Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization
AI Automation in Production
Festo
Intelligent Networking for Future-Proof Production Systems
Case Study
Results
Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products
Generative AI in Manufacturing
Bosch
AI Process Optimization for Improved Production Efficiency
Case Study
Results
Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime
Let's
Work Together!
Is your organization ready for the next step into the digital future? Contact us for a personal consultation.
Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
Ready for the next step?
Schedule a strategic consultation with our experts now
30 Minutes • Non-binding • Immediately available
For optimal preparation of your strategy session:
Your strategic goals and challenges
Desired business outcomes and ROI expectations
Current compliance and risk situation
Stakeholders and decision-makers in the project
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