Data lake services: keep every kind of data, and find it again

Logs, sensor readings, documents, clickstreams and database exports do not fit neatly into a warehouse, but they hold answers your reports and AI projects need. We design data lakes and lakehouses on Azure, AWS, Microsoft Fabric and Google Cloud that store this data once, in its original form, catalogued and secured so the right people can use it.

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What we cover

What a lake is forStorage for the data that does not fit in tables
OrganizationA lake, not a swamp
Big dataFrom a strategy to the first useful answer
PlatformsBuilt on the cloud you already use
SecurityEncrypted, access-controlled and audited
Analytics and AIThe foundation for analytics and AI

What a lake is for

Storage for the data that does not fit in tables

A data lake is a central store for structured and unstructured data, kept in its original format. Where a warehouse holds data that has already been cleaned and modeled for reporting, a lake keeps everything as it arrives, so you can decide later what to do with it.

That makes a lake the right home for data you will analyze in ways you cannot predict yet. Data scientists explore it, AI projects train on it and retrieve from it, and the warehouse loads the parts that become regular reporting.

What we do

Machine, sensor and log data
Documents, images, audio and video
Clickstream and social media feeds
CRM, relational and NoSQL exports

Organization

A lake, not a swamp

A lake without structure soon becomes a pile of files nobody trusts. We organize it in zones: a raw zone that keeps exactly what each source sent, a cleaned zone where formats and types are standardized, and a curated zone with data ready for analysis. Every data set is registered in a catalog with its owner, its source, how often it refreshes and who may read it, so people can find what already exists before they copy it again.

What we do

Raw, cleaned and curated zones
A catalog with an owner for every data set
Open file formats your tools can read
Retention rules for data you no longer need

Big data

From a strategy to the first useful answer

Big data projects fail when they start with the technology, so we start with the questions, in three steps.

Consulting and strategy: we identify the internal and external sources, decide which data is worth keeping, and design the storage so it is easy to extract and analyze. Data capture: we bring in batch and real-time data from logs, images, social media, databases and application records. Exploration and analytics: we work through the questions on the roadmap with your analysts, find the patterns, and turn the ones that matter into reports, models or automated jobs.

What we do

Use cases and a roadmap before any build
Batch and streaming ingestion
Exploration alongside your analysts
Cross-training for your own team

Platforms

Built on the cloud you already use

We build lakes on Azure Data Lake Storage, Amazon S3 and Google Cloud Storage, and lakehouses in Microsoft Fabric and Databricks, which add tables, transactions and SQL on top of the files. Cloud storage is paid for as it is used, so there are no storage appliances to buy and license as the data grows. Processing runs when a job needs it and scales with the volume.

What we do

Azure Data Lake Storage, Amazon S3 and Google Cloud Storage
Lakehouses in Microsoft Fabric and Databricks
Spark and SQL processing that scales with the job
Cost alerts on storage and compute

Security

Encrypted, access-controlled and audited

A lake gathers data from across the business, so it has to be secured as a whole. Data is encrypted at rest and in transit, access is granted by role and by zone, personal and sensitive fields are masked or kept out of the open zones, and every read and change is logged. We connect the lake to your identity provider, so access is managed with the same accounts and groups as the rest of your systems, and it ends when someone leaves.

What we do

Encryption at rest and in transit
Access by role and by zone
Masking of personal and sensitive fields
Access logs you can audit

Analytics and AI

The foundation for analytics and AI

The same lake feeds the data warehouse for reporting, notebooks for data science, and AI agents that search documents or compute answers from the data. AI is only as good as the data it can reach: a catalogued, governed lake lets an agent use the right data, and nothing it should not see.

Questions

Common questions

Do we need a data lake, a data warehouse or both?

If your questions are mostly about structured business data, such as sales, finance and operations, start with a warehouse. If you also have files, logs, documents or high-volume data you want to keep and explore, add a lake. Many companies end up with a lakehouse, which serves both from one platform.

How do you stop a data lake becoming a data swamp?

With zones, a catalog and owners. Every data set has a named owner, a description, a refresh schedule and access rules, and data nobody uses is archived or deleted under a retention policy.

Is a data lake secure enough for sensitive data?

It can be, when security is designed in from the start: encryption, access by role to each zone, masking of personal data and audit logs. We decide with you which data belongs in the lake at all, and which is better left in the source system.

Can we use a data lake for AI?

Yes. A lake is where AI projects usually find their training data and their documents. We make sure what a model or an agent can reach is governed, so it does not see data it should not.

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