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Azure Synapse Analytics

Unified analytics: data warehouse, big data, and machine learning

⬢ LIVELLO 2Tecniche
Alto
Impatto sullo stipendio
5 mesi
Tempo di apprendimento
Difficile
Difficoltà
12
Carriere
In sintesi

Azure Synapse Analytics integrates data warehousing, Spark, and ML. Mid-level data engineers earn $130-165k; seniors architect lakehouses and analytics platforms at $230-310k.

Cos'è Azure Synapse Analytics

Azure Synapse Analytics is a unified analytics platform that combines enterprise data warehousing (SQL), big data processing (Apache Spark), and machine learning into one service. It provides a single workspace for data engineers, analysts, and scientists to collaborate. Synapse integrates with Azure Data Lake Storage, Power BI, and AI/ML tools. - Unified platform: Data warehouse, Spark, and ML in one workspace

🔧 STRUMENTI ED ECOSISTEMA
Synapse StudioSQL PoolsSpark PoolsData IntegrationPower BI IntegrationPipelinesNotebooksSQL ScriptsLinked ServicesMonitoring & Analytics

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$88k$155k$270k
UK£64k£113k£196k
EU€60k€106k€186k
CANADAC$97kC$171kC$298k

🎓 Certificazioni

Azure Data Engineer Associate (DP-203)
Azure Synapse Analytics certification

⚖ Confronta con

Databricks Lakehouse

❓ Domande frequenti

What's the difference between Synapse SQL and Spark?
Synapse SQL is for structured data warehousing; Spark is for big data processing and unstructured data.
Can I use Synapse SQL and Spark together?
Yes, both share same metastore. Spark processes raw data, SQL queries refined datasets.
What's the pricing model?
SQL: pay per DWU-hour. Spark: per vCore-hour. Storage separate. On-demand SQL cheaper per query.
Can I connect Power BI directly?
Yes, native integration. Direct query or import; live dashboards update as data changes.
How do I handle data governance?
Column-level security, row-level security, data classifications, and audit logging.
What's the lakehouse pattern in Synapse?
Combine data lake (cheap storage) + warehouse (fast queries) using Delta Lake and unified metadata.

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