Azure Synapse Analytics
Definition
Azure Synapse Analytics is Microsoft's unified analytics platform that integrates big data and data warehousing for comprehensive data analysis solutions.
Use Cases
- Carlsberg Group: Unifying data from breweries, logistics, and sales to improve forecasting and operational reporting across markets. — Built an Azure-based analytics environment using Azure Synapse Analytics to query and model curated datasets, integrating ingestion/orchestration with Azure data services and enabling self-service reporting with Power BI. (Faster access to standardized reporting and analytics across regions, improving decision-making speed and consistency for business users.)
- PwC: Delivering analytics platforms for clients that need to combine structured warehouse analytics with large-scale data processing. — Implemented Azure Synapse Analytics as a central analytics layer, using dedicated SQL pools for warehouse workloads and Spark for large-scale transformations, with governed access and integration into BI tools. (Reduced time to deliver analytics solutions by using a unified platform approach and improved scalability for mixed SQL and big data workloads.)
- Marks & Spencer: Modernizing enterprise analytics to support reporting and insights across retail operations. — Adopted Azure analytics services including Azure Synapse Analytics to consolidate and analyze data at scale, connecting curated datasets to reporting and analytics experiences. (Improved ability to analyze data across the business with scalable performance and more modern data workflows.)
Provider Equivalents
- AWS: Amazon Redshift
- Azure: Azure Synapse Analytics
- GCP: BigQuery
- OCI: Oracle Autonomous Data Warehouse
Frequently Asked Questions
- What's the difference between Azure Synapse Analytics and Azure Databricks?
- Azure Synapse Analytics is a unified analytics service that brings together SQL data warehousing, Spark, data integration (pipelines), and governance in one workspace. Azure Databricks is a separate, specialized Apache Spark platform optimized for data engineering, data science, and ML workloads. Use Synapse when you want an integrated SQL + Spark + pipelines experience in one place; use Databricks when you want a best-of-breed Spark environment with strong collaborative notebooks and advanced ML/engineering features.
- When should I use Azure Synapse Analytics?
- Use Azure Synapse Analytics when you need to: (1) run large-scale SQL analytics (data warehouse-style) on curated data, (2) process big data with Spark, (3) orchestrate ingestion and transformations with built-in pipelines, and (4) serve BI tools like Power BI from a governed analytics layer. It’s a good fit for enterprise analytics modernization, consolidating multiple data sources into a lake/warehouse, and supporting both batch and interactive analytics.
- How much does Azure Synapse Analytics cost?
- Pricing depends on which Synapse capabilities you use and how long they run. Common cost drivers include: dedicated SQL pool compute (measured in DWUs) billed while running, serverless SQL billed per data processed, Spark pools billed per vCore-hour while active, pipeline/orchestration activity runs, and storage (typically Azure Data Lake Storage) plus data movement/egress where applicable. Costs can be controlled by pausing dedicated SQL pools when not needed, using serverless for ad-hoc queries, autoscaling Spark, and optimizing file formats/partitioning to reduce data scanned.
Category: data
Difficulty: advanced
Related Terms
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