Technology ecosystem
Quorlytic works across the cloud, data and analytics stack: cloud data platforms on AWS, Azure and Google Cloud, warehouse and lakehouse platforms such as Snowflake, Databricks and BigQuery, engineering tools such as dbt, Airflow and Spark, and BI layers such as Power BI, Tableau and Looker. Platform choice follows the workload, not the vendor.
Quorlytic selects technology from architecture, workload, governance, scale and operating requirements — not vendor fashion. The platform is a consequence of the design, never the starting point.
How we choose
Tool selection happens only after we understand the workload, the data path, the operating model, the governance boundary and the business decision the system has to support.
A platform earns its place by matching the workload, data path and operating model — not by being the newest name on the market.
We favour open formats, portable models and documented interfaces so the architecture can evolve without a forced rewrite.
Identity, access, lineage and quality are part of the design, not a control layer added after the platform is live.
If a system cannot be monitored, recovered and handed over, the architecture is incomplete — however elegant the diagram.
Illustrative reference architecture
One way a modern data ecosystem can be organised. It is a reference for discussion, not a mandatory stack — every layer is chosen against the requirement in front of it.
Operational systems that originate data.
Engineering concernOwnership, change rate, source contracts
Move and schedule data reliably.
Engineering concernBatch, CDC, streaming, replay
Model, test and document data products.
Engineering concernQuality, lineage, schema change
Store, govern and serve at scale.
Engineering concernWorkload isolation, access, cost
Analyse, predict and retrieve.
Engineering concernSemantics, evaluation, monitoring
Where insight becomes action.
Engineering concernAdoption, workflow, accountability
Technology domain explorer
Seven practical domains, each with its own purpose, engineering concerns and architecture position. Choose a domain to see how the tools connect to the work.
Technology domain
Purpose
Where the platform runs and scales.
Engineering concerns
Technology domain
Purpose
Store, govern and serve governed data.
Engineering concerns
Technology examples
Technology domain
Purpose
Move, transform and prepare reliable data.
Engineering concerns
Technology domain
Purpose
Turn governed data into decision surfaces.
Engineering concerns
Technology domain
Purpose
Model, experiment and evaluate.
Engineering concerns
Technology domain
Purpose
Grounded, permission-aware applications.
Engineering concerns
Technology domain
Purpose
Release, monitor and operate.
Engineering concerns
Technology examples
At a glance
A workload-selection reference, not a ranking. Each platform is listed with the role it usually plays, the work it suits and what a Quorlytic engagement around it typically involves.
| Dimension | Snowflake | Databricks | Power BI | AWS | Azure | Google Cloud |
|---|---|---|---|---|---|---|
| Primary role | Cloud data warehouse | Lakehouse and processing | Semantic modelling and reporting | Cloud data platform | Cloud data platform | Cloud data platform |
| Typical workload | Concurrent governed analytics | Large-scale processing, streaming and model training | Executive and operational reporting | Storage, processing and warehouse workloads | Lakehouse, integration and reporting workloads | BigQuery analytics with batch and streaming pipelines |
| Storage and compute | Storage and compute separated; warehouses isolate workloads | Object storage with clusters sized per job | In-memory semantic model refreshed from the platform | S3 storage with Redshift, Glue or EMR compute | ADLS storage with Fabric, Synapse or Databricks compute | Cloud Storage with BigQuery, Dataflow and Dataproc |
| Analytics role | Primary serving layer for BI and reporting | Curated layers served to analytics consumers | Decision surface and governed measures | Warehouse plus direct lake query | Fabric or Synapse serving into Power BI | BigQuery as the governed analytical layer |
| AI and ML role | Features and scores consumed from another platform | Primary platform for features, training and notebooks | Consumes model output; does not train | Processing and model hosting across AWS services | Model development on Azure ML or notebooks | Features and models built on BigQuery data |
| Operating model | Warehouse sizing, access and cost per workload | Cluster, job and notebook operations | Model ownership, refresh and workspace governance | Service ownership, IAM and per-workload cost | Workspace, entitlement and capacity management | IAM, policy tags and slot, byte and storage cost |
| Typical Quorlytic engagement | Platform architecture, migration and BI enablement | Lakehouse design, pipeline engineering and ML | Semantic model design and reporting | Service responsibility design and Redshift or Athena modelling | Service boundary decision and Power BI hand-off | BigQuery architecture and processing model choice |
Featured platforms
Selected for the depth of engineering they demand, not as endorsements. Each is relevant to a particular class of workload and operating model.
Cloud data platform
Governed analytics and shared analytical workloads.
Relevant workloads
Quorlytic capabilities
Lakehouse engineering
Unified data engineering and machine learning workloads.
Relevant workloads
Quorlytic capabilities
Semantic modelling and reporting
Decision-ready reporting on governed semantic models.
Relevant workloads
Quorlytic capabilities
Cloud platform pages
Cloud data platform
Storage, processing and warehouse workloads assembled from AWS services.
Relevant workloads
Quorlytic capabilities
Cloud data platform
Fabric, Synapse and Power BI estates with entitlement built in.
Relevant workloads
Quorlytic capabilities
Cloud data platform
BigQuery, Dataflow and Dataproc arranged around the workload.
Relevant workloads
Quorlytic capabilities
Selection framework
Seven decision factors that shape a platform choice before a licence is signed. Each one changes something concrete in the architecture.
What decision or workflow must improve?
What this changesScope, success measures and the first deliverable.
Volume, velocity, structure and freshness.
What this changesBatch or streaming, partitioning and storage model.
Warehouse, lakehouse, streaming or hybrid.
What this changesPlatform class, system boundaries and integration pattern.
Identity, privacy, lineage and access.
What this changesAccess model, masking and audit requirements.
Latency, concurrency and compute profile.
What this changesCompute model, caching strategy and warehouse sizing.
Skills, ownership, support and deployment.
What this changesManaged or self-operated tooling and handover.
Usage, licensing, FinOps and total cost.
What this changesConsumption controls, commitments and cost observability.
Architecture patterns
The same tools can produce very different systems. Five illustrative patterns show how data, engineering and decisions connect — and what each one asks of the architecture.
A governed path from operational sources to shared analytical decision-making.
System concernFit depends on concurrency, governance boundaries and the cost of keeping semantics consistent.
A low-latency path from events to operational visibility and action.
System concernFit depends on latency tolerance, ordering and replay requirements, and the cost of always-on compute.
A governed path from trusted data to evaluated models and applications.
System concernFit depends on source contracts, authorization boundaries and an evaluation set built from real tasks.
A grounded answer path where every response carries the sources it was built from.
System concernFit depends on whether permissions can be enforced at retrieval and whether quality can be measured rather than asserted.
A repeatable path from features to a deployed model that is still watched after launch.
System concernFit depends on whether features are available at decision time and whether anyone owns the model once it is live.
Engineering principles
Principles we apply to a platform once it is chosen, so the system stays maintainable, observable and affordable over time.
Avoid unnecessary coupling where practical, so the architecture can evolve without a forced rewrite.
Systems should expose quality, failures and operational state rather than hiding them behind a dashboard.
Access, ownership and lineage are designed into the system, not added as an afterthought.
Repeatable infrastructure and deployment reduce operational risk and make change predictable.
Architecture should account for how usage translates into cost, and make that cost observable.
Technology ecosystem
Supporting context, not the main story. These are platforms and tools Quorlytic can work with across cloud, data, engineering, analytics, ML and operations.
Cloud
Data platform
Engineering
Analytics
Data science / ML
Operations
Technologies shown represent platforms and tools Quorlytic can work with. No partnership, certification or endorsement is implied unless explicitly stated.
Technology in context
Every platform choice connects to a Quorlytic capability that designs, builds and operates it.
Explore all servicesQuestions
Snowflake, Databricks, Power BI and the data services of AWS, Azure and Google Cloud, along with the tools around them such as dbt, Airflow and Kafka. No partnership or certification is implied by any name on this site.
From the workload: who reads the data, how fresh it must be, what shapes arrive, whether machine learning uses the same data, and what your team can operate. A representative workload is tested on the leading option before the decision is final.
Yes, and many estates do. The design question is the boundary: which platform defines each business measure, how data moves between them, and who owns each side.
Yes. Cost is traced to the workloads and teams that create it, then reduced through workload isolation, right-sized compute, storage lifecycle rules and query design, and the result stays visible after the work ends.
Your next data decision
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