peterindia.netCloud Data Analytics
Managed warehouses, lakehouses, query engines, streaming services and BI tools that run analytics at cloud scale, without servers to manage.
Serverless data warehouse that separates storage from compute and adds built-in machine learning, so teams can run SQL over very large datasets.
Best for: Ad hoc SQL analytics at scale with no infrastructure to run.
Visit Google BigQuery →AWS's cloud data warehouse for SQL analytics, available as provisioned clusters or serverless, with the ability to query data in the data lake.
Best for: SQL analytics for teams already on AWS.
Visit Amazon Redshift →Cloud data platform with separated storage and compute, secure data sharing, and availability across the major clouds.
Best for: Multi-cloud warehousing and data sharing between organisations.
Visit Snowflake →Lakehouse platform built around Delta Lake that brings data engineering, SQL analytics and machine learning together.
Best for: Combining analytics and AI on one governed platform.
Visit Databricks →Unified software-as-a-service analytics platform covering data engineering, warehousing, real-time analytics and Power BI over a shared data lake.
Best for: Microsoft-centred organisations wanting one analytics suite.
Visit Microsoft Fabric →Teradata's cloud analytics platform for large-scale, mixed-workload enterprise analytics.
Best for: Enterprises with heavy, complex analytical workloads.
Visit Teradata VantageCloud →Managed service for the ClickHouse column-oriented database, built for fast analytical queries on large volumes of event data.
Best for: Real-time dashboards and high-speed aggregation.
Visit ClickHouse Cloud →Open lakehouse for analytics and AI workloads that lets teams use several query engines over shared data.
Best for: Hybrid and multi-engine lakehouse deployments.
Visit IBM watsonx.data →Serverless interactive query service that runs SQL directly on data stored in Amazon S3, charged per query.
Best for: Quick queries on data lake files without loading them.
Visit Amazon Athena →Managed big data platform for running open-source frameworks such as Apache Spark, Hive and Flink on AWS.
Best for: Large batch processing and open-source big data jobs.
Visit Amazon EMR →Query engine built on Trino that runs federated SQL across data lakes, warehouses and other sources.
Best for: Analysing data where it lives, across many sources.
Visit Starburst →Managed service for batch and streaming data processing, based on Apache Beam.
Best for: Unified batch and stream pipelines on Google Cloud.
Visit Google Cloud Dataflow →Managed services on AWS for collecting, processing and analysing streaming data in real time.
Best for: Real-time ingestion and stream analytics on AWS.
Visit Amazon Kinesis →Fully managed Apache Kafka service from Confluent for building real-time data streaming pipelines.
Best for: Event streaming as the backbone of analytics.
Visit Confluent Cloud →Large-scale data warehousing and big data processing service on Alibaba Cloud.
Best for: Big data analytics in Alibaba Cloud regions.
Visit Alibaba Cloud MaxCompute →Hybrid data platform that runs analytics and machine learning across public clouds and on-premises environments.
Best for: Enterprises that need the same data platform across cloud and data centre.
Visit Cloudera →AWS's cloud business intelligence service for interactive dashboards and embedded analytics.
Best for: Dashboards on AWS data sources.
Visit Amazon QuickSight →Google Cloud's business intelligence platform built on a governed semantic model, so metrics stay consistent across reports.
Best for: Governed metrics and embedded analytics.
Visit Looker →Business intelligence and dashboard tool that connects to a wide range of sources and integrates with the Microsoft ecosystem.
Best for: Self-service BI for Microsoft-based teams.
Visit Microsoft Power BI →Oracle's cloud analytics service with self-service BI, augmented analytics and integration with Oracle databases and applications.
Best for: Organisations running on Oracle data and applications.
Visit Oracle Analytics →Match your requirement to a starting point, then compare vendors.
| Large SQL analytics on structured data | A cloud data warehouse such as BigQuery, Redshift or Snowflake |
|---|---|
| Analytics and machine learning on one copy of data | A lakehouse such as Databricks or Microsoft Fabric |
| Querying files in object storage on demand | Serverless query engines such as Athena |
| Real-time events and streams | Kafka-based or native streaming services such as Confluent Cloud or Kinesis |
| Dashboards for business users | BI tools such as Power BI, Looker or QuickSight |
| Data spread across many systems | Federated query engines such as Starburst |
Neighbouring directories and hubs.
Quick answers.
It means running data storage, processing and visualisation on managed cloud services instead of your own servers. Providers handle scaling, availability and upgrades, and you typically pay for the compute and storage you use.
A data warehouse stores structured data optimised for SQL queries. A lakehouse keeps data in open formats in object storage and adds warehouse-style management on top, so the same data can serve both analytics and machine learning.
Start from where your data already lives and which skills your team has. Then compare pricing models, data-sharing needs, governance features and how well each platform connects to the BI and machine learning tools you use.
Separate storage from compute, set budgets and alerts, pause or autoscale compute, and monitor query costs. Cloud cost management tools can help.
When data must be analysed with very low delay, when bandwidth is limited, or when data should stay on site. See the companion page on edge-based data analytics solutions.
Descriptions are summaries of each vendor's own positioning, checked against its website on 4 October 2026. Product names and offerings change often, so confirm details on the vendor site before deciding.