Data Lake Consulting

Building a data lake that performs at enterprise scale takes more than the right architecture. Data lake consulting at N-iX handles design, cloud migration, and analytics implementation with AI-native engineers working on your actual pipelines.

Industry leaders that benefit from our data expertise

Enterprise data lake consulting services with N-iX

N-iX offers a wide range of data engineering and analytics services - from Data Strategy and Data Governance to Big Data engineering, Business Intelligence, and Data Science. We have completed dozens of projects for many industry-leading enterprises and Fortune 500 companies, helping them turn their data into insights,business value and increased revenues.

Data lake is often a vital step in the digital transformation journey of many organizations. It allows storing and processing large amounts of raw data to ultimately turn it into insights and desired business outcomes. With over 200 highly-skilled data experts, N-iX is prepared to handle any data lake consulting services need, from design and development to support and implementation of additional features.

Research, analysis, and design

N-iX experts will analyze your requirements, design the architecture, and create a clear implementation roadmap.

Development and optimization

Our end-to-end data lake development services include establishing effective data governance, setting up and optimizing the entire Extract-Load-Transform (ELT) process, and more.

Cloud development and migration

Our engineers will help you migrate your data solution to the cloud to optimize maintenance costs and streamline operations.

Implementation of advanced data capabilities

N-iX will help you get more from your data by building Business Intelligence, AI and Machine Learning, and other types of data solutions on top of your data lake.

A new standard for data lake engineering: AI-augmented, driven by APEX

Data lake projects have a predictable problem: engineers spend the majority of build time on work that doesn't require their expertise. ELT boilerplate, data quality rule writing, cloud infrastructure scripting, schema documentation: that's where timelines slip and costs compound.

At N-iX, our data engineers run AI-augmented development workflows that absorb exactly that work. The repetitive engineering work like pipeline boilerplate, quality checks, and infrastructure scripting gets handled by AI tooling. This is Pragmatic AI Software Engineering, applied to every data lake build we run. N-iX measures what AI delivers on your data lake codebase first, then scales what works.

What's left is the work that requires expertise. Our data engineers spend their time on schema design, data modeling, ingestion logic, and governance architecture. Those are the decisions that determine whether your data lake delivers on what you built it for. APEX (Assess · Pilot · Expand · eXcel), our custom framework for embedding AI across engineering workflows, structures how that shift is implemented on your project. We baseline your data engineering processes, co-implement on real pipelines, and track throughput and data quality at every stage.

96%

faster data lake infrastructure and pipeline documentation

94%

data quality validation time cut from 3 days to 4 hours

92%

faster ELT transformation layer refactoring cycles

85-95%

workflow time savings across piloted data lake use cases

How can you benefit from enterprise Data Lake Consulting?

Flexible and inexpensive scaling

Data lakes can be quickly scaled to the required data storage capacity without incurring significant additional costs.

Data democratization

Data in its original format from all sources is stored in a single place and can be easily accessed when necessary.

Integration of AI and Machine Learning

A centralized repository that stores data in raw format enables easy deployment and training of AI/ML models.

Implementation of advanced analytics

Deep learning algorithms and complex queries can be applied to stored data to find hidden patterns and generate valuable insights.

Get lake flexibility and warehouse reliability in a data lakehouse system with N-iX

What happens when your AI models need raw data, and your BI dashboards need governed data, and both are pulling from two disconnected systems? Teams end up maintaining duplicate schemas, reconciling mismatched numbers between the lake and the warehouse, and waiting on whichever platform kept up that week before a report goes out.

N-iX delivers data lakehouse services that bring warehouse-grade reliability to the data lake you already run. Our teams build one governed platform that serves raw AI workloads and structured BI reporting.

Our data engineers design lakehouse architectures using Databricks, Snowflake, and open table formats like Iceberg and Delta Lake, matched to your existing cloud environment. We handle the full path: platform selection, staged migration, governance, and the analytics layer built on top.

1.5-hour

max reporting delay, down from 3-4 hours

data volume growth, 10% cost increase

10x

faster queries than legacy Hadoop

75%

lower equipment failure rate

Our data lakehouse consulting and implementation services

At N-iX, we run consulting and implementation as one continuous build, from the first architecture decision to the dashboard a business user opens on day one. The same team designs your schema, migrates the data, sets the governance rules, and builds the analytics layer on top.

Lakehouse architecture and platform selection

A lakehouse combines data lake storage with warehouse-grade transactions, usually built on Databricks, Snowflake, or an open table format like Apache Iceberg. Our team selects between them based on your actual workload mix, cloud commitments, and query patterns. We recommend Databricks for heavy ML workloads, Snowflake for SQL-first analytics teams, and an Iceberg-based lakehouse for teams that want maximum platform flexibility.

Data lakehouse migration services

N-iX migrates data, schemas, and access controls from a legacy warehouse or ungoverned lake into a governed lakehouse, domain by domain. We run the legacy and new systems in parallel, validating outputs before any cutover happens. Reporting keeps running the entire time. Our consultants hold the cutover until every number reconciles.

Open source lakehouse consulting

Apache Iceberg and Delta Lake keep your schema and transaction history portable across compute engines, independent of any single vendor. N-iX implements schema evolution, time-travel queries, and partition strategies through data lakehouse consulting services from day one. Changing your compute engine later becomes a configuration change.

Data governance on the lakehouse

We build access controls and audit logging directly into the lakehouse layer, using tools like Unity Catalog for access enforcement along with Iceberg’s schema evolution and snapshot history for versioning. Every query against sensitive data is logged at the storage layer, capturing the identity, timestamp, and accessed columns. Compliance teams get a queryable audit trail that satisfies GDPR, HIPAA, or SOC 2 review.

Analytics and AI enablement

Once the foundation is governed, our team builds BI dashboards, Machine Learning models, and AI agent workloads on the same dataset. One semantic layer, defined once, feeds Power BI, a Databricks notebook, and a RAG pipeline alike. N-iX designs that semantic layer once and reuses it across every downstream workload.

Data lakehouse support services

After go-live, N-iX handles monitoring, cost tuning, and pipeline maintenance for the platform we built. Our support services keep the same engineers who designed your architecture on call when something breaks. That continuity is what keeps a lakehouse from drifting back into the fragmentation it replaced.

What drives enterprises to data lakehouse consulting

Most enterprises run a data lake and a data warehouse that were never built to agree with each other. Every report gets reconciled by hand, and every AI model waits on data the team doesn't fully trust yet. A data lakehouse closes that gap, and it's what most of our clients come to us for.

1. Unify raw and governed data on one platform

2. Add ACID transactions and schema enforcement without a rebuild

3. Migrate off a legacy warehouse without stopping reporting

4. Keep the table format open on Iceberg or Delta Lake

5. Run AI and BI on the same data

6. Cut costs by retiring duplicate infrastructure

Our data lakehouse implementation process

Step 1:
Consulting and architecture assessment

Goal

Identify the right lakehouse platform for your workload before any build begins. Within data lakehouse implementation services, we map your current data lake or warehouse, your analytics and AI use cases, and your team's existing skills against Databricks, Snowflake, and open lakehouse architectures.

What we do

Assess current infrastructure, data volumes, and query patterns
Map AI and BI workloads against platform capabilities
Recommend the platform, table format, and migration sequence

Output:

Written architecture assessment
Platform recommendation with cost and timeline estimates
Migration roadmap sequenced by domain

Step 2:
Migration and governance design

Goal

Define how data moves and who can access it before a single pipeline gets built. We design the staged cutover plan, the table format, and the access model together, so governance isn't retrofitted after the migration ships.

What we do

Design the domain-by-domain migration sequence
Define schema versioning and access controls in Iceberg, Delta Lake, or Unity Catalog
Set validation criteria for every cutover

Output:

Migration and governance blueprint
Access control matrix
Validation checklist per domain

Step 3:
Pipeline development and staged migration

Goal

Move data into the lakehouse without breaking the reporting that depends on it. We build ingestion and transformation layer domain by domain with the validation checks at every step.

What we do

Build ingestion pipelines and quality checks per domain
Run legacy and lakehouse systems in parallel
Reconcile outputs and cut over domain by domain

Output:

Production ELT pipelines
Reconciliation reports per domain
Fully migrated lakehouse platform

Step 4:
Analytics and AI enablement

Goal

Turn the validated foundation into dashboards, models, and AI workloads teams actually use. Once data is governed and reconciled, we build the semantic layer once and connect BI, Machine Learning, and AI agent workloads to it.

What we do

Build the shared semantic layer and metric definitions
Connect BI tools, ML pipelines, and AI agents to the lakehouse
Validate outputs against business logic

Output:

Production BI dashboards and ML pipelines
Documented semantic layer
AI-ready dataset with governed access

Step 5:
Optimization and support

Goal

Keep the platform performing and cost-efficient long after go-live. The engineers who built your architecture stay on to monitor it, tune storage and compute costs, and fix pipeline issues before they reach a dashboard.

What we do

Monitor pipeline health and query performance
Optimize storage tiering and compute costs
Maintain and extend pipelines as new sources are added

Output:

Ongoing monitoring and alerting
Quarterly cost and performance review
Ongoing pipeline maintenance and support

Why enterprises invest in data lakehouse services

Lower total cost of data infrastructure

One platform replaces the storage and compute you were paying for twice, once for the lake, once for the warehouse. We achieved a 25-30% cut in infrastructure and maintenance costs on one lakehouse migration for a Fortune 500 industrial supply company, before accounting for the engineering hours no longer split across two systems.

Faster time from data to decision

Reports stop waiting on whichever platform finished syncing last. Business users query current data directly from the lakehouse, without a separate ETL job standing between them and the answer. On that same migration, our team cut data processing time from 15 hours to 6, and brought maximum reporting delay down from 3-4 hours to 1.5.

AI readiness without a separate data project

Machine Learning models and AI agents need raw, high-volume data. BI needs it governed and consistent. A lakehouse gives both from the same source, so an AI initiative doesn't trigger a second data platform build. Our 200+ data engineers, including a dedicated Databricks practice, have unified data from 10 to 100+ sources into a single governed platform for enterprise clients.

Lower compliance and audit risk

Access controls and audit logging live in the platform itself, not in a process someone has to remember to run. Regulated industries get a system of record from a data lakehouse consulting company that holds up under a GDPR, HIPAA, or SOC 2 review, backed by 24 years of enterprise delivery under those exact standards.

Platform flexibility

Open table formats like Iceberg and Delta Lake keep your data portable across compute engines. Switching from Databricks to Snowflake, or the reverse, no longer requires a rebuild. As official Databricks, Snowflake, and AWS partners, we design for that portability from the first architecture decision.

Data lake vs. data warehouse: which one is right for you

Data lakes and data warehouses are the most common solutions for storing data. They have different purposes
and can complement each other to enhance the process of collecting, storing, processing, and analyzing data.

N-iX has profound expertise in data warehouse consulting and offers end-to-end development services. We
can analyze business processes and existing systems, design and implement a solution according to your
requirements, and offer maintenance and support to ensure its smooth operation.

Cloud solutions development: your journey with N-iX

Enhance your efficiency and optimize costs with a cloud-based data lake

Data and analytics cloud services

Modern cloud data platforms

Technologies we work with

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Data Lake Consulting at N-iX: proven expertise and decades of experience

Proven data expertise
With over 60 successful data projects delivered up to date, N-iX offers unmatched expertise in implementing data solutions.

Highly-skilled data experts
Our data practice counts more than 200 experts who can help you with any Data Lake Consulting need.

Strong proficiency in the cloud
With over 400 cloud experts and official partnerships with AWS, Google Cloud, and Microsoft Azure, we offer reliable cloud development and migration services.

Solid experience in tech
N-iX has more than 23 years of experience in software engineering, which allows us to ensure a smooth implementation process, from kick-off to handover.

Industry recognition for data services
N-iX has received many industry recognitions, such as a “Rising star in data engineering” by ISG or a spot in the Global Outsourcing 100.

Data protection compliance
We make sure that your data remains protected at all times by complying with established service quality and data protection standards, such as GDPR, HIPAA, PCI DSS, ISO 9001:2015, and ISO 27001:2013.

Meet our data experts and technology leaders

Igor Tymchuk

VP, Head of Delivery Unit

Valentyn Kropov

Chief Technology Officer

Rostyslav Fedynyshyn

Head of Data and Analytics Practice

FAQ

How do you make sure AI actually improves our data lake workflows?

Pragmatic AI Software Engineering means we validate the impact of AI on your actual data lake pipelines before rolling anything out. We first baseline your ELT processes, data quality workflows, and infrastructure scripting, run AI-augmented pilots on real codebases, and track throughput and data quality before and after every change.

How long does it take to build a production-ready data lake?

Timelines depend on how many source systems feed the lake and how much upfront data governance work is missing. A single-source pipeline with clear schema requirements can reach a working environment in a matter of weeks. Multi-source enterprise builds with legacy systems and inconsistent data quality take longer because governance and ELT design must be in place before ingestion scales. N-iX baselines your current data landscape first through APEX, which builds the roadmap around what your environment actually needs, not a generic template.

What does data lake consulting cost?

Cost depends on data volume, the number of source systems, and the extent to which the ELT and governance layers already exist. A cloud migration of an existing data lake costs less than a build-from-scratch approach that includes schema design, ingestion logic, and quality rules from day one. N-iX scopes this during the research and design phase, tying the estimate to your architecture and requirements, never a flat package price.

Do we need to clean up our data before starting a data lake project?

No. A data lake stores raw data in its original format, and that's the whole point. You don't need clean, structured data before ingestion. What matters more is understanding your source systems and access requirements, because that shapes the governance rules and schema design N-iX builds around the data. Waiting to "clean up first" usually just delays the project without improving the outcome.

Can a data lake integrate with our existing cloud environment?

Yes. N-iX works across AWS, Google Cloud, and Microsoft Azure, with official partnerships in place, and designs data lakes to migrate onto or connect with infrastructure your team already runs. This includes setting up the ELT pipelines and governance layer to work with your existing BI and analytics tools, avoiding a separate stack. Cloud migration work also targets maintenance cost reduction as part of the same engagement.

How do you keep our data secure and compliant during migration?

Data protection controls are built into the pipeline from the design stage, before the lake goes live. N-iX aligns this work with GDPR, HIPAA, PCI DSS, ISO 9001:2015, and ISO 27001:2013, which are most relevant during cloud migration, when data moves between environments. It's the same compliance framework applied across our data engagements with financial and healthcare clients.

What's the difference between a data lake and a data lakehouse, and which do we need?

A data lake stores raw data cheaply at scale, leaving structure, transactional support, and query performance to whatever tools sit on top of it. A lakehouse adds a structured layer directly on top of raw storage, providing schema enforcement, ACID transactions, and BI-grade query performance without duplicating data into a separate warehouse. N-iX assesses your current analytics and ML workload during the design phase to determine whether a straight data lake, a lakehouse, or a hybrid approach fits your actual query patterns and governance requirements.

Can you migrate our existing data lake to a lakehouse architecture?

Yes. N-iX migrates existing data lakes to lakehouse platforms like Databricks or Snowflake without full data re-ingestion, because the raw storage layer typically stays in place while the structured layer is added on top. The migration includes rebuilding ELT logic to write into the new table format and validating that downstream BI tools and ML pipelines still read data correctly after the switch. We run this the same way as any cloud migration: baseline the current pipeline, pilot on a subset of the data, then scale once the numbers hold up.

Do we need a data lakehouse if we already have a data warehouse and a data lake running side by side?

Not necessarily. Running both makes sense when your warehouse handles structured BI reporting well and your lake handles raw and unstructured data for ML, with acceptable duplication and sync overhead between them. A lakehouse becomes worth the migration once that duplication starts costing more in engineering time and data drift than a consolidated architecture would. N-iX weighs this tradeoff directly against your current pipeline costs and query patterns before recommending a lakehouse migration.