Aller au contenu principal
JobCannon
Toutes les compétences
⚡

BigQuery Advanced

⬢ NIVEAU 3Techniques
💰
Élevé
Impact salarial
⏱
5 mois
Temps d’apprentissage
📊
Difficile
Difficulté
👥
3
Métiers
En bref

BigQuery is Google Cloud's fully managed data warehouse for analytics at scale. Advanced skills include optimization (slots, clustering), advanced SQL (window functions, arrays, structs), real-time ingestion, machine learning integration, and cost control.

Qu’est-ce que BigQuery Advanced

BigQuery Advanced encompasses optimization, performance tuning, and advanced SQL patterns for Google Cloud's serverless data warehouse. It includes cost management (slots, clustering, partitioning), real-time ingestion (Streaming API, Change Data Capture), machine learning (BQML), and integration with ecosystem tools (Looker, Dataflow, Vertex AI). Advanced practitioners architect data solutions for petabyte-scale analytics. - Petabyte Scale: Handles analytics at unlimited scale without infrastructure management

🔧 OUTILS ET ÉCOSYSTÈME
BigQuery ConsoleBigQuery APIBigQuery MLStandard SQLBigQuery Storage APIData Transfer ServiceBigQuery BI EngineLooker IntegrationApache ArrowOptimization Tools

💰 Salaire par région

RégionJuniorConfirméSenior
🇺🇸USA$100k$165k$280k
🇬🇧UK£80k£132k£225k
🇪🇺EU€85k€140k€240k
🇨🇦CANADAC$120kC$200kC$340k

🎓 Certifications

Google Cloud Associate Cloud Engineer
Google Cloud Professional Data Engineer
BigQuery Specialty Certification

🎯 Métiers qui utilisent BigQuery Advanced

Analytics Engineer
Data Architect
Data Scientist

❓ FAQ

▶What is the difference between BigQuery standard and BigQuery Flex Slots?
Standard = monthly commitment (100 slots minimum). Flex = hourly billing (60 slots minimum). Flex for variable workloads.
▶How do I optimize BigQuery costs?
Partition tables by date, cluster by hot columns, use approximate functions, avoid SELECT *, enable result caching.
▶What is BigQuery BI Engine and when should I use it?
In-memory cache for fast queries on aggregated data. Worth it for dashboards with <100GB data, repeated queries.
▶Can I join large tables in BigQuery without exploding costs?
Yes; use broadcast joins (small table), avoid cross joins, leverage clustering to prune data before joins.
▶What is the BigQuery Storage API and why does it matter?
Allows columnar data export, Apache Arrow integration, faster exports than JSON. Essential for ML pipelines.
▶How do I use BigQuery ML for predictions?
CREATE OR REPLACE MODEL statement with training data; BQML auto-trains and deploys models without Python.
▶What is the typical latency for real-time ingestion into BigQuery?
Streaming API: ~35 seconds latency. Batch: sub-second after load job completion. Real-time dashboards need BI Engine.

🔗 Compétences liées

Apache AirflowApache Beam PipelinesAzure Ml StudioAzure Synapse AnalyticsBusiness Intelligence BiCareer NetworkingConsulting Practice LaunchData AnalysisData Governance PoliciesDbt Data TransformationDigital Pr Media RelationsFreelancing Business
🎯

Vous doutez que cette compétence soit faite pour vous ?

Passez le Career Match — nous vous suggérerons les bonnes pistes.

Trouver mes compétences idéales →

Trouvez votre voie professionnelle

Correspondance par compétences parmi 2 536 métiers. Gratuit, ~2 minutes.

Passer le Career Match — gratuit →
Explorer aussi :Toutes les compétencesBibliothèque des métiersTests de personnalitéMéthodologie