--- title: "Azure Synapse Analytics: Series Overview" canonical: "https://data-driven.com/blog/advanced-analytics-with-microsoft-synapse-all-you-need-to-know-series-overview/" description: "Follow a planned Azure Synapse series covering architecture, data engineering, machine learning, business intelligence, cost, and platform fit." --- Azure Synapse · 2020 series archive # A practical map of _Azure Synapse Analytics_ Follow a planned Azure Synapse series covering architecture, data engineering, machine learning, business intelligence, cost, and platform fit. 7 June 2020 3 min read Updated 25 Aug 2026 Reader brief How to read this archive 1. The series was planned as both an executive and technical review. 2. Its 2020 roadmap is preserved as historical context. 3. Use current Microsoft documentation for product and migration decisions. ![Azure Synapse workspace shown as a unified analytics platform](/_astro/On_Screen_011.DE1i6gub_1gzK8B.webp) Dated reference This article was published on 7 June 2020. Product details, interfaces, pricing, and linked resources may have changed since then, so confirm current guidance before acting. This series began in 2020 with a practical question: where did Azure Synapse Analytics fit in a data platform, and which workloads justified using it? The plan covered business fit, architecture, engineering, analytics, and cost. Product scope has changed since publication, and Microsoft Fabric now affects many of the same platform decisions. Treat this page as the original series map, not a current product recommendation. ## Questions for decision-makers A platform review should answer questions that connect technology to an operating need: - Which business decision or analytical workload is the platform meant to support? - What data must it ingest, transform, store, and serve? - Which teams will build and operate it? - What are the expected cost, latency, security, and resilience boundaries? - Which existing services can remain, and which integrations add avoidable work? The original series intended to test those questions with a defined proof of concept rather than repeat a feature list. ![Microsoft Azure Synapse Analytics logo](/_astro/Azure-Synapse-Analytics-Logo-01-1024x931.Cio74zvX_278Gc3.webp) ## Questions for data practitioners For data engineers, data scientists, analysts, and platform teams, the planned review covered: - workspace setup and access - exploratory analysis in notebooks - batch and streaming ingestion - transformations with visual tools, pipelines, SQL, and Spark - warehouse and lakehouse patterns - machine learning workflows - Power BI reporting - security, maintainability, and measured cost ![Azure Synapse analytics services and workspace components](/_astro/synapse-1024x5761-1.D1mhRuf0_5BsSW.webp) A useful technical review should use the same dataset and acceptance criteria across approaches. That makes architecture, delivery effort, performance, and operating cost visible without turning the article into unsupported vendor ranking. ## The original proof-of-concept plan The series proposed a public COVID-19 dataset as a shared test case. The goal was to follow data from ingestion through exploration, transformation, modeling, and reporting. That plan created a consistent thread: 1. define the question and success measures 2. ingest and inspect the source data 3. transform it at a realistic scale 4. build and test an analytical or machine-learning result 5. publish an understandable report 6. compare the result with the original requirements ![Azure Synapse workspace value proposition](/_astro/valueprop31-1024x580.DSRCvBN5_Zkol7d.webp) ## Planned article sequence The working contents list was: 1. Series overview 2. What Azure Synapse is and where it fits 3. Choosing an advanced analytics use case 4. Getting started with Azure Synapse Analytics 5. Exploratory data analysis with notebooks 6. Visual, low-code, and coded transformations 7. Spark transformations and performance 8. Operational and analytical data integration 9. Data warehousing with Synapse SQL 10. Machine learning with Synapse 11. Business intelligence with Power BI 12. An end-to-end demonstration 13. Review against the original objectives Not every planned article is guaranteed to exist. We have retained the sequence because it shows the intended learning path without pretending that the series was completed. ![Advanced analytics team reviewing data](/_astro/analyzing-data-P9MSFE6-scaled-e1590895658326-1024x1024.DAEP0bLo_Z29WzUU.webp) ## Check the current platform before acting For a current architecture decision, start with Microsoft’s [Azure Synapse Analytics overview](https://learn.microsoft.com/en-us/azure/synapse-analytics/overview-what-is) and compare it with the services already in your estate. Confirm current support, regions, pricing, integration boundaries, and Microsoft Fabric guidance. A dated proof of concept can still teach a method, but it cannot establish today’s platform fit. Filed under - [azure-synapse](/tag/azure-synapse/) - [advanced-analytics](/tag/advanced-analytics/) - [business-intelligence](/tag/business-intelligence/) - [data-lake](/tag/data-lake/) - [data-warehouse](/tag/data-warehouse/) - [machine-learning](/tag/machine-learning/) - [service-review](/tag/service-review/) Continue reading ## Related perspectives [![AI agent delivery pipeline with observation and review checkpoints](/_astro/AzureDevOps.BxSQ5lfc_ZJLXmu.webp)](/blog/agent-observability-best-practices-enterprise/) AI ### [Agent Observability: Five Enterprise Practices](/blog/agent-observability-best-practices-enterprise/) Learn how action monitoring, decision traces, evaluation loops, governance controls, and CI/CD checks support reliable AI agents. 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