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Data and Analytics

Practical guidance on databases, pipelines and analytics tooling for engineering leaders deciding how to build and scale their data stack.

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Hand-picked by our editors and refreshed quarterly.

About this category

This hub collects our writing on data analytics and data engineering: the database choices, pipeline architectures and BI tools that determine whether a data platform actually holds up under production load. Most of what we publish here comes out of real project decisions, like choosing between managed query services or picking a database that will not fall over as write volume grows. If you are comparing options before a build, start with current database rankings, look at medallion pipeline design for a structured approach to ingestion and transformation, or read through tools for data-driven decisions if the gap is on the reporting side rather than the storage side.

A lot of teams that read this hub are past the research stage and need people who can build. We staff data engineers and analytics engineers through Staff Augmentation when an internal team is short on hands for a defined piece of work, and we take on end-to-end platform builds through Data Engineering Services when the scope covers pipeline design, warehouse modelling and BI delivery together. For teams that want a dedicated group running the whole data function rather than a single engagement, we set up a Dedicated Development Team that sits alongside your product organisation and takes ownership of the roadmap.

The articles underneath this hub go deep on specific decisions rather than general theory. That includes comparisons like PostgreSQL against Oracle, SQL Server fundamentals, and SQL against T-SQL for teams standardising on Microsoft tooling. It also covers visualisation practice, data mesh trade-offs, and newer engines like DuckDB that are changing how analytics workloads get run locally. If you already know which layer of the stack you are trying to fix, the three subcategories below narrow things down: business intelligence and reporting, data engineering and governance, and databases specifically. Each is worth browsing on its own if you want the full set of articles rather than the highlights collected here.

Frequently asked questions

  • What is the difference between data analytics and data engineering?

    Data engineering builds and maintains the infrastructure that moves and stores data: pipelines, warehouses, and the systems that keep them reliable. Data analytics is what happens on top of that infrastructure, turning stored data into reports, dashboards, and models that inform decisions. Weak data engineering shows up as slow or unreliable analytics, no matter how good the analysts are. Most platform problems trace back to one side or the other, rarely both at once.

  • Which database should we use for a new web application?

    It depends on read and write patterns more than on general reputation. Relational databases like PostgreSQL still cover most transactional workloads well, while document or key-value stores make sense when schema flexibility or horizontal scale matters more than joins. Our database options for web apps article walks through the trade-offs by workload type, which is a more useful starting point than picking whatever is trending.

  • Do we need a full data platform or just better BI tools?

    If your source data is already clean, consistent, and centralised, better BI tooling alone can close the gap quickly. If teams are pulling from spreadsheets, disconnected exports, or systems that do not agree with each other, no BI tool fixes that. That is a data engineering problem first. Diagnose which one you actually have before committing budget, since the fixes and timelines are very different.

  • How do we choose between AWS analytics services like Athena and Glue?

    Athena is built for ad hoc SQL queries directly against data in S3, with no infrastructure to manage. Glue is the transformation and cataloguing layer that prepares and moves that data in the first place. Most production setups use both together rather than choosing one. The comparison in this hub breaks down when each pulls its weight on its own.

  • How much does data platform work typically cost to build?

    It depends heavily on data volume, number of sources, compliance requirements, and whether you are migrating existing systems or building fresh. There is no reliable industry average worth quoting, and anyone giving you one without seeing your architecture is guessing. Scope a discovery phase first so estimates are based on your actual pipelines and data volume rather than a generic template.

  • Where do machine learning and infrastructure topics fit if they touch our data stack?

    Model training and MLOps questions belong in AI and Machine Learning, since they build on top of the data layer rather than defining it. Deployment, scaling, and infrastructure questions around where your data platform runs are covered in Cloud and DevOps. Both assume the data foundation covered here is already solid.