One Workspace Instead of Five Tools
Most analytics stacks are assembled from separate parts: one tool to ingest, another to store, another for Spark, another for BI. Azure Synapse collapses those into a single workspace where the pieces are already wired together.
For teams already invested in Microsoft — Azure AD, Power BI, SQL Server skills — that integration is the whole point. Your existing T-SQL knowledge transfers directly, and governance runs through the identity system you already administer.
Ingest, Store, Analyse, Visualise
One workspace covers the whole chain — fewer moving parts, fewer integration seams to break.
SQL and Spark, Side by Side
Analysts write T-SQL, engineers write PySpark — against the same data, in the same workspace.
Microsoft-Native Governance
Azure AD, Purview and Power BI plug straight in — no bolt-on identity or catalogue layer.
Everything in One Place
The capabilities that make Synapse the natural choice on Azure.
Dedicated SQL Pools
Provisioned MPP warehousing with predictable performance for your core reporting layer.
Serverless SQL
Query Parquet and CSV straight out of the data lake, paying only per terabyte scanned.
Apache Spark Pools
Managed Spark for heavy transformation, data science and streaming — no cluster babysitting.
Synapse Pipelines
The Data Factory engine built in: a broad set of built-in connectors and orchestration without a separate service.
Power BI Integration
Author and publish reports from inside the workspace, against a governed model.
Enterprise Security
Azure AD, private endpoints, column-level security and dynamic data masking as standard.
End-to-End Synapse Services
From workspace design to reports your board actually opens.
Workspace Architecture
Pools, lake layout and workspace structure designed around your teams and workloads.
Migration — Synapse & Fabric
Move from SQL Server, APS or legacy warehouses onto Synapse — or take existing Synapse workloads to Microsoft Fabric, where Microsoft is focusing new analytics investment — with validated parity.
Pipelines & Integration
Synapse Pipelines and Data Factory ingesting from ERP, CRM, APIs and on-prem sources - AI drafts the boilerplate and mapping docs, a developer reviews them.
Performance Tuning
Distribution, indexing and result-set caching for pools that have quietly gone slow.
Governance & Purview
Lineage, cataloguing, masking and RBAC so regulated data stays defensible.
Power BI Enablement
Semantic models and reports built on a governed layer — one number, one meaning.
Building on Synapse With Fabric in View
Azure Synapse Analytics is a supported Azure service, and plenty of organisations run their core reporting on it. Microsoft is, however, steering new analytics investment to Microsoft Fabric, so new capabilities arrive there first. That shapes how we approach Synapse work: keep the existing workspace fast, secure and cost-efficient, and make sure anything new you build will move cleanly when you decide the time is right.
In practice that means favouring patterns both platforms share - Delta and Parquet files in the lake, Spark notebooks rather than proprietary mapping data flows where practical, and business logic in Power BI semantic models instead of buried in nested views. Synapse work done this way is not throwaway work; it becomes the starting point for the move.
Dedicated vs Serverless SQL Pools, and What They Cost to Run
A dedicated SQL pool is a provisioned MPP warehouse: every table is spread across distributions using a hash, round-robin or replicated strategy. Choose a poor hash key and queries spend their time shuffling rows between nodes, which is one of the most common reasons a dedicated pool slows down. Clustered columnstore indexes, up-to-date statistics and workload groups that separate loads from reporting do the rest.
A serverless SQL pool has no provisioned capacity. It reads lake files on demand and bills by the data each query processes, which suits exploration, logical warehouses over Parquet and light reporting. Bills grow when queries scan raw CSV or unpartitioned folders, so we partition by date, convert to Parquet and expose curated views instead of raw paths.
Dedicated pools bill for compute while they are online, whether anyone queries them or not. Pausing development and test pools out of hours, scaling production down after the nightly load, and automating both from a pipeline are the simplest controls; storage is still billed while paused. Result-set caching and materialised views then cut the compute each dashboard refresh needs.
Planning the Move From Synapse to Fabric
A good migration starts with an inventory of what the workspace really contains: dedicated pool tables and their distributions, stored procedures, serverless views and external tables, pipelines and triggers, Spark notebooks, linked services, and the Power BI reports that depend on each of them. Every item is tagged move as-is, rework or retire - mature workspaces usually carry pipelines and tables nobody has used in months.
We then migrate by business domain rather than by component, running Synapse and Fabric side by side and reconciling row counts and report totals before each domain switches over. Roles, masking and row-level security are rebuilt and tested inside each wave rather than left to the end, and the Synapse resources for a domain are paused, then removed, only once its reports have been signed off.
Pixel-perfect software, delivered at AI speed
PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.
Pixel
Polished UI and clean, tested code - detail is part of the job, not an afterthought.
Bots
Dedicated developers who join your team and use AI for boilerplate, tests and documentation.
Savings
AI-assisted delivery can save more than 50% of development cost compared with traditional development.
Ready to Unify Your Analytics?
Let our engineers bring your warehousing, big data and BI into a single Azure Synapse workspace.