Cloud Data Warehouses: Directory & Guide

The best cloud data warehouses depend on whether your primary problem is governed SQL analytics, real-time event analysis, streaming data mutation, time-series observability, relationship analysis, or operational transactions. This category covers scalable SQL engines and storage systems for analytics and reporting, spanning cloud data warehouses, OLAP databases, lakehouse platforms, time-series databases, table formats, query engines, relational databases, graph databases, key-value stores, document databases, and search engines. A warehouse is only one part of this landscape: the right product type determines whether teams query centrally managed data, analyze events as they arrive, or query data in place across existing systems.

How to Choose

Start with product type, because these tools solve materially different data problems. A cloud data warehouse such as Teradata provides a platform for warehouse workloads across environments. An OLAP database such as Apache Druid is built for fast analytical queries over event and time-series data. A lakehouse platform such as Starburst provides federated SQL access across data lakes, warehouses, and databases, while a table format such as Apache Hudi brings transactional and incremental-processing capabilities to data stored in a lake.

Use a time-series database such as InfluxDB when timestamps, high write volume, windowing, aggregation, and downsampling are central to the workload. Use a query engine such as Amazon Athena when teams want to run standard SQL against data in Amazon S3 without provisioning infrastructure. Choose a relational database such as MySQL for transactional application data; choose Neo4j when the central questions concern nodes, relationships, and connected paths rather than rows and joins. Other types in the category—including document, key-value, and search systems—serve specialized access patterns rather than replacing a general-purpose warehouse.

Then evaluate these practical criteria:

• Where data already lives. Starburst federates queries across data lakes, warehouses, and databases through more than 50 connectors. It is a strong fit when moving all data into one store would create unnecessary duplication or disruption.

• Freshness and ingestion pattern. Apache Druid supports query-on-arrival with native Kafka and Kinesis integration at millions of events per second. Choose it when high-volume events must be available for sub-second analysis shortly after ingestion.

• Mutation and CDC requirements. Apache Hudi supports upserts, record-level indexing, ACID guarantees, incremental processing, clustering, and compaction. That makes it appropriate when streaming changes must be applied to cloud-storage tables rather than handled as batch replacements.

• Query billing model. Amazon Athena charges $5 per TB scanned, with a 10 MB minimum per query. Teams using Athena should design tables and queries around scanned-data control, because the documented rate is tied directly to scan volume.

• Environment and governance requirements. Teradata supports hybrid multi-cloud deployment across AWS, Azure, and GCP and includes data fabric and in-database analytics capabilities. For complex enterprise estates, assess whether cross-environment operation matters more than a warehouse-native, single-environment experience.

Top Tools

Starburst

Starburst is a lakehouse platform built on Trino that provides federated SQL analytics across data lakes, warehouses, and databases. Its ANSI SQL engine, enhanced Trino foundation, single access point, and 50+ connectors make it distinct for teams that need to query distributed data without treating one system as the only destination.

Best suited for: Data teams that need Trino-based SQL analytics across cloud and on-premises data lakes and want a freemium path for evaluation.

Pricing: Freemium from $0.50/mo

The main trade-off is that it is a weaker fit for teams seeking a fully managed, warehouse-native experience from Snowflake. Databricks is a reviewed alternative; Starburst separates itself through Trino-based federation across lakes, warehouses, and databases.

Apache Druid

Apache Druid is an open-source OLAP database designed for real-time analytics across high-volume event and time-series workloads. It combines scatter/gather execution, columnar storage, automatic time indexing, dictionary encoding, type-aware compression, and a SQL API; its stated design supports sub-second queries on high-cardinality datasets with billions to trillions of rows.

Best suited for: Teams that need sub-second OLAP queries on high-volume event or time-series data and can operate a distributed open-source system.

Pricing: Open Source

The trade-off is operational: the available editorial guidance calls for at least two to three engineers able to run a distributed open-source deployment. ClickHouse is a reviewed alternative; Druid differentiates with native Kafka and Kinesis ingestion enabling query-on-arrival at millions of events per second.

Teradata

Teradata Vantage is a cloud data warehouse platform for analytics across data lakes, data warehouses, new data sources, and hybrid multi-cloud environments. Its documented capabilities include data fabric, ClearScape Analytics, in-database analytics, Bring Your Own LLM, Enterprise Vector Store, and data warehouses.

Best suited for: Large data teams that need a usage-based warehouse platform connecting cloud and on-premises workloads.

Pricing: Usage-Based

The trade-off is that the available context does not provide pricing levels or performance details sufficient to estimate a particular deployment. Amazon Redshift is a reviewed alternative; Teradata is differentiated here by its stated hybrid multi-cloud support across AWS, Azure, and GCP.

Influx

DB

InfluxDB is a time-series database for metrics, events, and IoT data, available open source, as a cloud DBaaS option, or through an Enterprise subscription. Its capabilities include real-time querying with Last Value Cache, high-volume ingest with unlimited cardinality, interoperability between real-time and historical data, and enterprise security controls including ISO 27001, SOC2, and end-to-end encryption.

Best suited for: Teams collecting high-frequency sensor data or application metrics that need time-based operations such as windowing, aggregation, and downsampling.

Pricing: Open Source

The trade-off is specialization: InfluxDB is designed around time-series workloads rather than serving as a general-purpose warehouse for every analytical model. TimescaleDB is a reviewed alternative; InfluxDB distinguishes itself with its time-series focus and Last Value Cache for real-time queries.

Apache Hudi

Apache Hudi is an open-source table format and data lakehouse platform designed for transactional cloud-storage tables. It supports mutability across workload shapes, ACID transactions, time travel, an incremental processing framework for low-latency analytics, and automatic clustering and compaction services.

Best suited for: Teams whose pipelines are dominated by streaming upserts and change data capture.

Pricing: Open Source

The central trade-off is workload fit: for analytics-dominant workloads, Apache Iceberg is simpler and has broader engine support, while Delta Lake is native for Databricks environments. Apache Iceberg is a reviewed alternative; Hudi differentiates itself through its streaming-upsert and record-level-indexing orientation.

Neo4j

Neo4j is a graph database that stores data as nodes and relationships rather than tables and rows. It provides a graph database platform, a query language, ACID transactions, cross-platform support, and support for complex relationship-driven queries at scale.

Best suited for: Teams building fraud detection, knowledge-graph, recommendation, dependency, or other applications where paths and relationships are the primary data model.

Pricing: Freemium

The trade-off is conceptual and architectural: Neo4j is appropriate when data is fundamentally a graph, not when conventional relational tables are the natural model. The available data does not list reviewed direct alternatives for Neo4j.

Amazon Athena

Amazon Athena is a serverless query engine for interactive analytics using standard SQL against data in Amazon S3. It analyzes petabyte-scale data where it resides, removing the need to set up query infrastructure for this use case.

Best suited for: Teams that need serverless, interactive SQL analysis of data stored in Amazon S3.

Pricing: Usage-Based

The trade-off is scan-based billing: SQL queries cost $5 per TB scanned and are rounded up to the nearest MB, with a 10 MB minimum per query. The available data does not list reviewed direct alternatives for Athena.

My

SQL

MySQL is an open-source relational database management system used for real-time transactional workloads. It provides high availability, data security, an open-source architecture, and MySQL HeatWave capabilities for integrated analytics and machine learning, alongside MySQL AI capabilities for integrated machine learning and generative AI.

Best suited for: Teams that need a reliable relational database for transactional applications and value a straightforward operational model.

Pricing: Enterprise

The trade-off is that MySQL is not positioned in the supplied evidence as the most sophisticated analytical database; its stated strengths are simplicity, reliability, and ecosystem breadth. PostgreSQL is a reviewed alternative; MySQL differentiates itself here through HeatWave’s integrated analytics and machine learning capabilities.

Comparison Table

These tools should be compared by workload shape rather than as interchangeable warehouses. The table summarizes the product type, fit, published pricing model, and one documented strength for each featured tool.

Our Methodology

We assess cloud data warehouses tools using available vendor documentation, published pricing information, documented capabilities, editorial review, and public adoption signals where supplied. We prioritize product evidence that can be tied to a specific architecture or operating requirement: for example, Apache Druid’s native Kafka and Kinesis ingestion, Starburst’s federated access and connector count, or Athena’s published per-TB scan rate.

Public ratings and stated customer counts can indicate that a product has visible usage, but they do not prove fit for a particular enterprise workload, governance model, data volume, or operating team. Likewise, a vendor feature list is evidence that a capability is offered, not proof that every deployment will achieve a particular performance or cost result.

Pricing evidence also has limits. Some tools publish an explicit rate, while others use usage-based, freemium, or contact-for-pricing models. We therefore state the documented pricing model and rates where available, avoid estimating deployment costs, and recommend validating workload-specific performance, operational burden, and commercial terms before making a production commitment.

Frequently Asked Questions

Should we choose a cloud data warehouse or a lakehouse platform?

Choose a cloud data warehouse such as Teradata when your requirement is a warehouse platform that connects analytics workloads across cloud and on-premises environments. Choose a lakehouse platform such as Starburst when federated SQL across existing lakes, warehouses, and databases is the priority. Starburst provides a single access point and more than 50 connectors, whereas Teradata’s supplied product evidence emphasizes hybrid multi-cloud operation, data fabric, and in-database analytics. The decision is architectural: central platform operation versus federated access to distributed systems.

When is Apache Druid a better fit than Amazon Athena?

Choose Apache Druid when real-time ingestion and sub-second OLAP analysis are mandatory. Druid supports native Kafka and Kinesis ingestion, query-on-arrival at millions of events per second, and scatter/gather execution for high-cardinality datasets with billions to trillions of rows. Choose Amazon Athena when data is in Amazon S3 and serverless standard-SQL querying is the requirement. Athena charges $5 per TB scanned, rounded to the nearest MB with a 10 MB minimum per query.

When should we use Apache Hudi instead of a query engine?

Apache Hudi is appropriate when cloud-storage tables need transactional behavior, streaming upserts, CDC, record-level indexing, and incremental processing. It provides ACID guarantees, time travel, clustering, and compaction for data lake workloads. A query engine such as Amazon Athena serves a different role: it executes interactive SQL against data in Amazon S3 without infrastructure setup. Use Hudi to manage mutable lake data and Athena to query data where it resides.

Is My

SQL a cloud data warehouse replacement?

MySQL is best treated as a relational database for real-time transactional applications, not as a universal replacement for specialized warehouse, OLAP, or time-series systems. Its supplied capabilities include high availability, data security, HeatWave integrated analytics and machine learning, and MySQL AI features. For relationship-centric analysis, Neo4j’s nodes-and-relationships model and ACID transactions are a more direct fit. For high-frequency time-series data, InfluxDB provides time-based querying, Last Value Cache, and high-volume ingest with unlimited cardinality.