Getting Started with Data
How should a company start building a modern data platform?
A modern data platform should start with business goals, not technology selection. The first step is to identify the key reporting, analytics, automation, and AI use cases the platform needs to support. From there, companies should assess their current data sources, data quality, governance requirements, and existing BI tools such as Power BI. A successful data platform roadmap usually includes architecture design, data engineering, cloud infrastructure, security, and a phased implementation plan.
What are the first steps in a greenfield data warehouse or lakehouse project?
A greenfield data warehouse or lakehouse project should begin with a clear definition of business priorities and target use cases. The next step is to design the core data architecture, including source system integration, data modelling, orchestration, governance, and BI consumption. At this stage, companies should also decide whether a traditional data warehouse, a lakehouse architecture, or a platform such as Databricks is the best fit. A good first implementation should deliver a usable business outcome quickly while creating a scalable foundation for future analytics and AI use cases.
Choosing the Right Platform
How do we know whether we need a data warehouse, a lakehouse, or a simpler reporting setup?
A simpler reporting setup may be enough if the company has only a few data sources, limited data volume, and straightforward Power BI reporting needs. A data warehouse becomes important when multiple departments need consistent KPIs, governed data models, and reliable reporting across the organization. A lakehouse is usually a better fit when the company also needs to handle large volumes of raw, semi-structured, or machine learning-ready data. The right choice depends on reporting complexity, data maturity, future AI ambitions, and the need for scalability.
When is Databricks the right choice for a data or AI platform?
Databricks is a strong choice when a company needs a scalable platform for data engineering, analytics, machine learning, and AI use cases in one environment. It is especially useful when the organization works with large data volumes, complex pipelines, streaming data, or advanced analytics workloads. Databricks can also be a good fit for companies that want to build a lakehouse architecture instead of maintaining separate data lake, warehouse, and ML environments. The business case is strongest when the platform is designed around real production use cases, not just experimentation.
When should a company consider a Databricks Lakehouse instead of a traditional data warehouse?
A company should consider a Databricks Lakehouse when it needs more flexibility than a traditional data warehouse can provide. This is often the case when the organization wants to combine BI reporting, data science, machine learning, and AI workloads on the same data foundation. A lakehouse architecture can support structured, semi-structured, and raw data while still enabling governed analytics through tools like Power BI. It is usually a good fit for companies that want a future-proof data platform instead of a reporting-only solution.
How should a company choose between Databricks, Snowflake, Microsoft Fabric, and a traditional data warehouse?
The choice should be based on use cases, data maturity, existing cloud environment, team capabilities, and long-term analytics or AI goals. Snowflake is often strong for governed enterprise data warehousing, while Databricks is well suited for lakehouse, data engineering, machine learning, and AI-heavy workloads. Microsoft Fabric can be attractive for organizations already deeply invested in Microsoft and Power BI, while a traditional data warehouse may still be enough for simpler reporting needs. The best decision usually comes from an architecture assessment rather than choosing a platform based on market popularity.
PowerBI and Reporting
How can we make our Power BI reports reliable, consistent, and easier to maintain?
Reliable Power BI reporting starts with a strong data model and consistent business definitions. Instead of building isolated reports directly on top of raw source systems, companies should use a governed data warehouse, lakehouse, or semantic layer. This helps ensure that KPIs such as revenue, margin, active customers, or churn mean the same thing across departments. A maintainable Power BI environment also needs clear ownership, reusable datasets, performance optimization, and controlled release processes.
When is Power BI enough on its own, and when does it need a data warehouse or lakehouse behind it?
Power BI may be enough on its own for smaller reporting environments with limited data sources and simple business logic. As soon as reports depend on multiple systems, complex transformations, historical data, or shared KPIs across departments, a proper data warehouse or lakehouse becomes important. Without a governed backend, Power BI can easily turn into a collection of disconnected reports with duplicated logic. A scalable BI setup usually combines Power BI with a reliable data platform behind it.
Why do Power BI reports often become fragmented across departments?
Power BI reports often become fragmented when each department builds its own datasets, transformations, and KPI definitions independently. This creates multiple versions of the truth, where finance, sales, operations, and management reports may show different numbers for the same metric. Fragmentation usually comes from missing data governance, unclear ownership, and the lack of a shared data model. The solution is not only report cleanup, but a proper BI architecture with governed datasets and a reliable data platform underneath.
How should a company connect Power BI reporting to a scalable data platform?
Power BI should connect to a curated, governed data layer instead of relying directly on raw operational systems. This layer can be built in a data warehouse, a lakehouse, Databricks, Snowflake, Microsoft Fabric, or another cloud-based platform depending on the company’s architecture. The goal is to separate data ingestion and transformation from reporting, so Power BI can focus on analytics and visualization. This approach improves performance, consistency, security, and long-term maintainability.
AI and Advanced Analytics
What kind of data foundation is needed before implementing advanced analytics or AI use cases?
Advanced analytics and AI use cases require reliable, well-structured, and well-governed data. Before starting AI implementation, companies need to make sure their source data is accessible, documented, integrated, and of sufficient quality. They also need clear ownership, consistent business definitions, and scalable data pipelines that can support production workloads. Without this foundation, AI projects often remain prototypesbecause the underlying data platform is not ready for operational use.
Cloud Migration and Implementation
How should an on-premise data warehouse be migrated to the cloud?
An on-premise data warehouse migration should start with an assessment of existing data models, reports, pipelines, dependencies, and business-critical workloads. Instead of simply copying the old architecture to the cloud, companies should use the migration as an opportunity to modernize data ingestion, modelling, governance, and BI consumption. The migration can be phased by domain, source system, or reporting area to reduce risk. A well-planned cloud migration improves scalability, maintainability, and readiness for advanced analytics or AI use cases.
How long does it take to build a production-ready data warehouse, lakehouse, or BI platform?
The timeline depends on scope, source system complexity, data quality, governance requirements, and the number of business use cases. A focused first release can often be delivered in a few months if the goals are clearand the project starts with a realistic MVP. Larger enterprise platforms involving Databricks, Snowflake, Power BI, cloud infrastructure, data governance, and multiple departments usually require a phased roadmap. The most important success factor is to deliver business value early while building an architecture that can scale over time.
Working with Us
How can we work together?
We offer staff augmentation — meaning we extend your internal team with experienced data and analytics specialists on a flexible basis. Instead of hiring full-time or outsourcing an entire project, you get the right expertise exactly when you need it, fully integrated into your team and processes.
What kind of specialists can you provide?
Depending on your project phase and needs, we can provide data engineers, analytics engineers, BI developers, cloud architects, data modelers, and ML engineers. Whether you are building a new platform, migrating an existing one, or scaling up your reporting capabilities, we match the right profile to your situation.
What does the process look like?
It starts with a short scoping call to understand your current setup, goals, and the expertise you need. We then propose the right specialists, you get to meet them, and if there is a fit, they can typically start within days. Engagements are flexible — you can scale up or down as your project evolves.
How is this different from outsourcing the whole project?
Your team stays in control throughout. Our specialists work within your workflows, use your tools, and align with your leads — they act as a natural extension of your team rather than an external vendor running things independently. This ensures knowledge stays in-house and quality standards remain yours to define.
Squery: Unparalleled IT solutions, data consulting, and business analytics.