13.1.1: Major Cloud Analytics Services
- Page ID
- 53459
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Major Cloud Analytics Services
AWS: Amazon Web Services provides several analytics offerings. Amazon Redshift is a fast, fully-managed, petabyte-scale data warehouse that uses columnar storage and massively parallel processing (MPP) to deliver high query throughput [2]. Redshift Spectrum extends Redshift by allowing queries over exabytes of data in S3 without moving it. Amazon Athena is a serverless, interactive query service for data in S3. It lets users run standard SQL queries directly on data in object storage; because it is serverless, there is no infrastructure to manage and you pay only for the queries run. For visualization and BI, Amazon QuickSight (formerly “QuickSuite”) is a scalable, in-memory BI service. QuickSight’s SPICE engine provides fast, in-memory querying and it integrates machine learning capabilities (anomaly detection, forecasting) into dashboards. QuickSight can connect to many data sources (S3, Redshift, Athena, on-premise databases, SaaS apps) so organizations can quickly create interactive reports from cloud data.
Azure: Microsoft Azure’s analytics stack centers on Azure Synapse Analytics and Azure Data Lake Storage. Azure Synapse is a “limitless” analytics service that unifies enterprise data warehousing and Big Data analytics. It offers both dedicated (provisioned) SQL pools for data warehousing and on-demand (serverless) query options over the data lake. Azure Data Lake Storage Gen2 is built on Azure Blob Storage but provides hierarchical namespaces and high throughput for big data workloads. It serves as a scalable data lake for storing raw and curated data. For visualization, Power BI is Microsoft’s cloud-based BI platform; it integrates seamlessly with Azure services to build dashboards and reports. In a typical Azure analytics architecture, data is ingested (via Azure Data Factory or Event Hubs), landed in ADLS Gen2 as the “single source of truth,” then Synapse (serverless or dedicated SQL pools) or Azure Databricks is used for processing and analysis, with results exposed through Power BI.
Google Cloud: Google’s core analytics offerings include BigQuery, Dataflow, and Looker. BigQuery is a fully managed, serverless data warehouse built for analytics at scale. It decouples compute and storage, allowing users to run SQL (and even Python-based BigQuery ML) over petabytes of data without managing servers. BigQuery’s architecture automatically scales to many nodes and uses columnar storage and caching (including the AQUA accelerator) for speed. Dataflow is Google’s managed service for both batch and streaming data processing (based on Apache Beam). It provides a unified model for ETL and stream analytics on a serverless infrastructure. For BI, Google offers Looker, a cloud BI and data exploration platform. Looker provides an interface for data analysts and developers (with model versioning) and can embed analytics in applications. Google’s analytics tools are also tightly integrated with AI/ML offerings like Vertex AI for advanced predictive analytics.
