Data Lakehouse Services
One copy of your data for reporting, data science and AI, in open formats you own.
Stack we work in
- Databricks
- Unity Catalog
- Snowflake
Microsoft Fabric- OneLake
- Apache Spark
dbt- Apache Airflow
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs a lakehouse could help
A data lakehouse keeps all your company data in one low-cost store that reporting, data science and AI can all use, without copying it into separate systems.
Read the full overview
We design and build lakehouses on open table formats, Apache Iceberg and Delta Lake, so your data stays readable by more than one engine and you are not tied to one vendor. We work on Databricks, Snowflake, Microsoft Fabric, AWS and Google Cloud: medallion layers (bronze, silver, gold), catalogs and access control with Unity Catalog, Polaris or AWS Lake Formation, incremental pipelines with Spark and dbt, and table maintenance that keeps queries fast and storage bills predictable. We also move existing data lakes and warehouses onto the lakehouse in planned stages.
Several copies of the same data
The warehouse, the data science team and the AI project each keep their own copy, and the numbers drift apart.
A data lake nobody trusts
Files land in storage with no tables, no schema and no owner, so people avoid using them.
Warehouse bills rising with volume
Logs, events and history are loaded into an expensive warehouse when cheaper storage would do.
Locked into one engine
Your data can only be read by one vendor's tools, so changing platform means a full migration.
No home for documents and AI data
PDFs, emails and images sit outside any governed platform, so AI projects start from scratch every time.
Outcomes
What you get
- One governed copy of data serving BI, data science and AI
- Open Iceberg or Delta tables readable by more than one engine
- Incremental pipelines that process only new and changed data
- Access control, lineage and audit logs in one catalog
- Table maintenance jobs that keep query times and storage costs steady
What we deliver
What you receive
Architecture and decision record
Platform, table format, catalog and layer design, with the reasons for each choice written down.
Lakehouse foundation as code
Storage, catalogs, permissions and environments defined in Terraform and deployed through CI/CD.
Ingestion and transformation pipelines
Incremental pipelines from your sources into raw, cleaned and business-ready tables.
Access and governance
Roles, row and column rules, lineage and audit logs set up in the catalog.
Maintenance and cost controls
Scheduled compaction and clean-up jobs, plus alerts on compute and storage spend.
Runbooks and handover
Documentation and training so your team can add sources and run the platform.
Lakehouse architecture
- Lakehouse design on Databricks, Snowflake, Microsoft Fabric, AWS or Google Cloud
- Medallion layers: raw, cleaned and business-ready data
- Choosing between Apache Iceberg, Delta Lake and Apache Hudi
- Storage on Amazon S3, Azure Data Lake Storage or Google Cloud Storage
- Interoperability with Delta UniForm and Iceberg REST catalogs
Built with
- Apache Iceberg
- Delta Lake
- Apache Hudi
- Databricks
- Snowflake
- Microsoft Fabric
- Azure Data Lake Storage
- Amazon S3
Pipelines & table design
- Incremental loads with MERGE, change data feeds and CDC
- Batch and streaming ingestion into the same tables
- Spark, SQL and dbt transformations
- Partitioning, clustering and file sizing for fast queries
- Schema evolution without breaking downstream reports
Built with
- dbt
- SQL
Catalog, security & governance
- Unity Catalog, Snowflake Horizon, Apache Polaris or AWS Glue Data Catalog
- Role-based, row-level and column-level access control
- Lineage, tagging and audit logs
- Data sharing with partners through Delta Sharing or Snowflake shares
Built with
- Unity Catalog
- Delta Sharing
- Snowflake
- Snowflake Horizon
- Apache Polaris
- AWS Glue
Operations & migration
- Compaction, snapshot expiry and vacuum jobs
- Cost monitoring for compute and storage
- Migration from Hadoop, Hive or older data lakes
- Moving warehouse workloads onto lakehouse tables
- Infrastructure as code with Terraform and CI/CD
Built with
- Terraform
- 01IngestBatch, CDC and streams
- Apache Kafka
- Fivetran
- AWS Glue
- Debezium
- 02StoreWarehouse and lakehouse
- Databricks
- Snowflake
- Snowflake Horizon
- Microsoft Fabric
- 03TransformModels and tests
- Apache Spark
- dbt
- Python
- 04OrchestrateScheduling, CI/CD, catalog
- Apache Airflow
- Unity Catalog
- Terraform
Why teams choose Equitysoft for a data lakehouse
Platform-neutral advice
We build on Databricks, Snowflake, Microsoft Fabric, AWS and Google Cloud, and partner with Snowflake and Databricks, so the design follows your needs.
Open formats by default
Apache Iceberg or Delta Lake tables keep your data readable by more than one engine, so you are not tied to one vendor.
Maintenance built in
Compaction, snapshot expiry and cost alerts are part of the handover, so query times and storage bills stay steady.
A real use case first
The first sources go live end to end before wider migration, with a written decision record. 50+ engineers, 20+ of them certified on cloud and data platforms, delivering since 2018.
How we work
Assess
We review your sources, existing lake or warehouse, query patterns, costs and the teams who need the data.
You receive Current-state review and target use cases
Design
We agree the platform, table format, catalog, layers and access model, with the trade-offs written down.
You receive Lakehouse architecture and table format decision
Foundation
Storage accounts, catalogs, permissions and environments are created with Terraform and deployed through CI/CD.
You receive Storage, catalog and security set up as code
Build
We load the first sources through raw, cleaned and business-ready layers and connect a real report or model.
You receive First sources loaded end to end
Migrate
Remaining sources and old lake or warehouse workloads move across in waves, each checked against the old system.
You receive Wave plan and reconciliation reports
Operate
We hand over runbooks and alerts, or keep running the platform for you.
You receive Runbooks, maintenance jobs and cost alerts
Tech stack
Technologies we use
Data platforms
- Apache Iceberg
- Delta Lake
- Databricks
- Unity Catalog
- Snowflake
- Snowflake Horizon
- Microsoft Fabric
- OneLake
- Azure Data Lake Storage
- Google BigQuery
Pipelines and orchestration
- AWS Glue
- Apache Spark
- dbt
- Apache Airflow
- Apache Kafka
- Debezium
- Fivetran
Languages
Cloud and DevOps
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
It is a single store for all your data, usually cloud object storage, with a table layer on top that gives it the reliability of a database: updates, deletes, versioning and access control. Reporting tools, data scientists and AI applications all read the same tables instead of separate copies.
If your needs are mainly SQL reporting on structured data, a well-built warehouse is often enough. A lakehouse pays off when you also have large volumes, semi-structured or unstructured data, streaming, data science or AI work, or you want to avoid locking data into one engine. We recommend the simpler option when it fits.
Both are mature. Delta Lake is native to Databricks and Microsoft Fabric. Iceberg has the widest engine support across Snowflake, AWS, Google Cloud, Trino and others. Delta UniForm and Iceberg REST catalogs now let one table be read as both, so the choice is less final than it used to be. We pick based on the platforms you run today and expect to run.
Databricks, Snowflake (with Iceberg tables and Polaris), Microsoft Fabric with OneLake, AWS with S3, Glue and Athena or S3 Tables, and Google Cloud with BigLake and BigQuery.
Yes. We convert Hive or Parquet folders to Iceberg or Delta tables, rebuild jobs as Spark or dbt pipelines, and run old and new side by side with reconciliation reports until you are ready to switch off the old system.
Partitioning and clustering that match how data is filtered, regular compaction of small files, statistics and Z-ordering or liquid clustering where the platform supports it, and separate compute for heavy jobs and dashboards.
Through one catalog, such as Unity Catalog, Snowflake Horizon, Polaris or Lake Formation, with roles, row filters and column masks. Nobody reads storage directly with shared keys.
Yes. Power BI, Tableau, Looker and other tools connect through SQL endpoints. We build business-ready tables and semantic models so report authors do not have to work with raw files.
Yes. Documents, logs and tables live in one governed place, so the same data can feed machine learning features, vector indexes and AI agents, with access rules and lineage applied once.
A design and foundation usually takes 4 to 6 weeks, with the first sources live soon after. A full migration runs in waves over several months, depending on the number of sources and how much legacy logic has to be rebuilt.
It depends on the number of sources, data volume and whether a migration is involved. Platform and cloud usage are billed to your own account. As a guide, an assessment and architecture usually costs $4,000 to $12,000, and a first production phase of 8 to 12 weeks costs $20,000 to $80,000 or more (USD).
We start with a current-state review and one target use case. We need read access to your cloud accounts and sources, a contact in security, and a business owner for the first use case. After go-live you can take over with runbooks or keep us for maintenance and support.
More about our Data Lakehouse
Working togetherHow we work with your team
Platform-neutral advice
We work across Databricks, Snowflake, Fabric, AWS and Google Cloud, so we recommend on your workloads, not a sales target.
Real use case first
The first release feeds a real report or model, so the business sees value early.
Open formats by default
Iceberg and Delta tables keep your data readable by other engines if your needs change.
Your accounts, your code
Everything runs in your cloud accounts and source control, and you own all of it.
Where lakehouses fit best
Financial services
Transaction history, market data and documents in one governed store, with full audit trails and time travel for regulators.
Retail and ecommerce
Clickstream, orders and product data together for reporting, personalization and demand forecasting.
Manufacturing and IoT
High-volume sensor data stored cheaply and joined with ERP and quality data.
SaaS
Product events and tenant data at scale, shared safely with customers through governed shares.
Talk to a lakehouse engineer
Tell us where your data lives today and who needs to use it.