16 Best Data Warehouse Tools To Explore in 2024

October 13, 2023

Mieke Houbrechts

The culprit of a slow-loading analytics dashboard? Your underlying infrastructure. Explore the 16 best data warehouse tools to use in 2023.

A smooth, fast-loading analytics dashboard is key to a good user experience. Especially for embedded charts and data visualizations that are shared with your product users, inside your SaaS application.

The culprit of a slow, heavy dashboard, however, is often its underlying data infrastructure. With the right data warehouse solution that optimally fits your data and use case, you'll get one step closer to a smooth dashboard experience.

But navigating all the options can be overwhelming. Yet don’t worry, because we’ve done the research for you, listing the 16 best data warehouse tools in this guide. But first…

What is a data warehouse?

Every company needs a single source of truth: one place where all their data is stored. A data warehouse is a centralized tool where organizations can integrate data from all of their different data sources, store it, and use it to get valuable insights from their data.

Compared to relational databases like PostgreSQL or SQL server, which are best for operational processes and transactions, a data warehouse is perfect for business intelligence. It can handle other processes like data modeling, ETL, aggregation,... which makes it a better base for all reporting and analytics processes.

Data warehouse vs ETL tools

Although data warehouse and ETL (Extract, Transform, Load) are often mentioned in one breath, they are in essence different tools. Imagine a data warehouse as a library where data is stored, categorized and labeled. You can retrieve your data from the data warehouse, then analyze it in a BI tool.

ETL, on the other hand, is a process - not a tool - that extracts data from different sources (Extract), modifies it to the format you need (Transform), and loads that data into a data warehouse (Load). ETL tools can manage this process for you, but some data warehouses also offer ETL capabilities in their suite.

Sample architecture of a data  stack with data warehouse and ETL

Data warehouse vs data lakes

If a data warehouse is like an organized library, data lakes are more like a book drop bin. Data lakes store vast amounts of raw, unorganized data in their original format, both structured or unstructured data types. With a data lake, you can do deeper data exploration, but you will need to put in a lot more effort to gain insights from your data.

A data warehouse is all about easy access. It stores structured data that you can query quickly and easily. If you’re using a business intelligence or embedded analytics tool, a data warehouse will give you much better performance and faster loading.

You don’t need to choose one or the other though. You can store data in your data lake, then move it into a data warehouse for faster, optimized querying. And if you want to combine both in one, there are “data lakehouses” for that too - which you’ll find a few examples of later on.

Why use data warehousing tools?

Using a data warehouse to store and structure your data comes with many advantages. Especially for companies that sit on a boatload of data and need to make sense of it quickly.

One source of truth

Data warehouses can integrate data from many different data sources. Put all of your sales, marketing or product data in one single place.

Better business intelligence

Data warehouses are one of the best infrastructures to run business intelligence and analytics processes. You can easily hook them up to a data visualization tool for data-driven decision-making.

Faster user experience

No one likes eternal spinning loaders. By using a data warehouse as the data source for your analytics dashboards, you’ll boost the performance and loading time of dashboards. Especially for customer-facing analytics in your SaaS app, this is crucial to a good user experience.

Smoother operations

By separating operational workloads from data analysis, you’ll put less strain on your IT systems in place.

Data warehousing tools: Luzmo's top picks

Now onto the main question: which data warehouse is best for you? The answer to that question, unfortunately, isn't always clear-cut. It depends on a bunch of factors:

  • Budget
  • Use case (e.g. monthly internal reports, real-time data exploration, customer-facing reporting inside a SaaS app,...)
  • Data infrastructure (e.g. other tools in your data stack it needs to integrate with)
  • Complexity (e.g. a low-maintenance tool vs high level of control)
  • The type of data (e.g. high vs low volume, frequently updated data, structured vs unstructured,...)

As embedded analytics company, our expertise at Luzmo lies in data infrastructure for client-facing analytics. Below, you'll find Luzmo's top picks for data warehousing, based on our experience helping numerous SaaS companies finding the best match for their specific setup. Although any of these options can work well for other use cases too, we will focus on evaluating them for embedded dashboards in SaaS products or web applications.

P.S.: Luzmo offers a native connector for all data warehouses below, so you can plug in your data and get started right away!

ClickHouse (Cloud)

ClickHouse is a popular open-source columnar database, built for analytical querying. If you're looking for speed and scalability, ClickHouse is one of the best options out there. ClickHouse Cloud is their cloud-hosted version, which adds on all the advantages of a managed cloud service.

ClickHouse logo

Best for:

  • Cost-effective solution for large-scale data analytics, thanks to efficient compression and processing
  • Real-time analytics and monitoring use cases, or scenarios where high-speed queries on large datasets is crucial
  • Event-based data like logs, transactions, or time-series data
  • (Cloud-only) teams who want the advantages of ClickHouse, but don't want to deal with the complexity of maintaining a ClickHouse infrastructure


Snowflake is one of the most popular and versatile cloud data platforms on the market. Although popular as a data warehouse, Snowflake is more than a cloud data warehouse alone. With data integration, sharing and real-time analytics capabilities, it is a powerful tool for data management.

Best for:

  • Cost control, since data storage and cloud computing scale independently, meaning you’ll only pay for what you use
  • Avoiding vendor lock-in, since Snowflake runs on multiple cloud services
  • Low-maintenance: resources scale up or down automatically, and multiple queries can run concurrently, so you’ll get the best performance with the least amount of effort

Google BigQuery

BigQuery is a serverless, cloud-based data warehouse solution, fully managed on Google Cloud Platform. It stores and analyzes massive volumes of data quickly and cost-effectively, making it a popular choice for supporting data analytics and business intelligence.

Google BigQuery logo

Best for:

  • Low management of infrastructure, thanks to its serverless architecture
  • Real-time analytics, with its ability to run real-time SQL queries
  • Smooth integration with other tools in the Google Cloud ecosystem.
  • Use cases where data doesn’t change often, because of its smart and cost-effective caching mechanism

Amazon Redshift

Amazon Redshift is a fully managed cloud data warehouse service by AWS (Amazon Web Services). It stores large volumes of data in a structured way, and is great for reporting and analytics thanks to its columnar data storage.

Amazon Redshift logo

Best for:

  • Maintaining close control over your resources thanks to its cluster architecture
  • High accessibility and reliability, thanks to data replication in each Redshift cluster
  • Easily handling massive volumes of data (up to petabytes)
  • Seamless integration with other AWS services


If you don’t want to use multiple tools in your data stack in parallel, Databricks is a great tool that does it all in one. Their cloud-based unified data analytics platform is built around Apache Spark, and is often called a “data lakehouse” for its combined capabilities.

Databricks logo

Best for:

  • Combining data lake, data warehousing and business analytics in one place
  • Excellent data processing at scale, with super-efficient data transformation and calculations
  • Cloud-agnostic use cases, if you need to integrate with multiple cloud providers

Microsoft Azure Synapse Analytics

Azure Synapse Analytics is an enterprise data warehouse by Microsoft. Besides data warehousing, this tool is well-known for its time series analytics and big data capabilities.

Azure Synapse Analytics logo

Best for:

  • Companies who run on the Microsoft or Azure stack (e.g. Azure Active Directory, Databricks or Power BI)
  • Scaling up on the fly, thanks to on-demand resources and provisioning
  • Unified data management, since it offers ETL, data warehousing, and machine learning


Panoply is a data platform that combines data warehousing with ETL (Extract, Transform, Load) capabilities. It’s an easy-to-use alternative that requires less data engineering and infrastructure management than traditional data warehouses. It ingests data from many different data sources without advanced programming.

Panoply logo

Best for:

  • Ease of use: it makes collecting, storing and querying data much more intuitive, with limited overhead
  • Ingesting data from many different sources without manual ETL, thanks to its many built-in data connectors
  • Saving money if you don’t need separate ETL and data warehousing tools
  • Automation of maintenance tasks, like performance tuning, data storage scaling,...


Built on the legacy of SAP Business Warehouse, SAP BW/4HANA is a powerful data warehouse solution, designed for SAP HANA’s in-memory database. Thanks to its streamlined data model, it simplifies many of the complexity layers in traditional data warehouses. As a result, it handles large volumes of data efficiently, leading to smooth and fast queries.


Best for:

  • Advanced analytics capabilities like predictive modeling and machine learning
  • Eliminating complexity in data modeling
  • Fast, real-time analytics thanks to its in-memory capabilities

Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is exactly what its name suggests. This cloud-based solution automates database tuning, security, backups, and updates, and makes it an easy-to-maintain warehouse for analytics workloads.

Oracle logo

Best for

  • A fully automated experience: it automates tedious manual maintenance tasks like database tuning, backups, and security updates
  • Scalability, with independent resources for computing and storage so you pay only for what you use
  • High level of data security thanks to automated security updates and patches

Other noteworthy data warehouses

Although we highly recommend the data warehouses above for an embedded analytics setup, you may be looking to achieve a different use case. If you couldn't find a good match above, here are few alternative data warehouse tools - ranging from established players to new kids on the block.

If you want to use any of these alternative options in Luzmo, reach out to our product experts. Although we currently don't offer an out-of-the-box connector, you can hook up any data source to Luzmo using our plugin API.


Firebolt is a cloud-native elastic data warehouse solution. It is designed for high-performance analytics on large datasets, because it scales resources based on demand and workloads. It comes with a unique, adaptive indexing technology for laser-fast querying.

Firebolt logo

Best for:

  • Great query optimization and performance, with custom indexes that can be tailored to your specific query patterns
  • Easy to scale resources up and down when needed with decoupled storage and compute
  • Building integrations and advanced functionalities on top of Firebolt, thanks to its powerful API and SDK

Teradata VantageCloud

Although Teradata is best known as a relational database management system, its VantageCloud product is a data platform that offers multiple services, including a data warehousing solution. Similarly to Databricks, it’s popular for companies who want to merge data warehousing, data lakes and analytics capabilities all in one.

Teradata logo

Best for:

  • All-in-one tooling for warehousing, data lakes and advanced analytics
  • Running multiple queries at the same time, especially on large datasets - thanks to its Massively Parallel Processing (MPP)
  • Scaling easily if data volumes grow, without affecting performance because data is distributed across nodes

Apache Hive

Apache Hive is a data warehousing tool built on top of Hadoop. If you’re dealing with big data, Apache Hive turns Hadoop’s big data into structured data, so you can run SQL queries on it.

Apache Hive logo

Best for

  • Big data analytics, since it makes big data accessible through SQL
  • A cost-effective data warehouse with an active community, since it’s open-source
  • Seamless integration with other tools in the Hadoop ecosystem


Cloudera Data Warehouse (CDW) is a hybrid cloud data warehouse, meaning it runs both on-premise and in the cloud. It’s designed for running analytics on large amounts of data, and is known for its smoooth integration with the Hadoop ecosystem.

Cloudera logo

Best for:

  • Near-realtime querying on big datasets, leveraging Hadoop’s big data capabilities
  • Hybrid use cases: Cloudera run on-premise, as well as in public, private or multi-cloud environments
  • Extending its capabilities, since it easily plugs into other data processing tools in the Hadoop ecosystem

Mozart Data

Mozart Data is a relatively new all-in-one modern data platform. It allows anyone to centralize, organize and analyze their data without engineering resources. They pride themselves in being the fastest way to set up a scalable, reliable data infrastructure with zero maintenance. With a few clicks, you can set up integrations, ingest data and start querying your data for analysis.

Mozart Data logo

Best for:

  • Quick setup of a modern data stack, with its user-friendly interface
  • Easy data ingestion, thanks to many out-of-the-box connectors to popular data sources
  • Collaborative features if you want to share insights, queries and analysis with your team

Apache Druid

Although it’s not a clear-cut data warehouse like other tools on this list, it’s worth mentioning Druid. Druid is an open-source “data store” that combines database and data warehouse-like features. It’s optimized for OLAP workloads and specializes in time-series data, which makes it especially suitable for analytics use cases.

Apache Druid logo

Best for:

  • Storing and querying time-oriented data, supporting processes like time-based partitioning
  • Ingesting real-time data, and making it available for querying immediately
  • Using as a data source for interactive analytics dashboards, thanks to their efficient queries and aggregations during data ingestion

IBM Db2 warehouse

IBM Db2 warehouse is IBM’s data warehouse, running both cloud-hosted and on-premises. It’s most well-known for its in-memory processing, making it great for real-time analytics with low latency.

IBM logo

Best for:

  • Fast query performance, with its in-memory processing
  • High data security, with robust built-in security features
  • High-performance machine learning models inside the data warehouse, easily integrated with other ML products of IBM

Yellowbrick Data Warehouse

Yellowbrick Data Warehouse is a modern analytical database. It’s designed for analyzing large volumes of data, and offers features for complex querying and aggregation. This makes it a tailored solution analytical workloads, rather than transactional use cases, and for that reason it’s worth mentioning Yellowbrick in this list.

Yellowbrick logo

Best for:

  • High-speed data processing and real-time analytics, even on petabytes of data, thanks to its adaptive MPP architecture
  • Hybrid use cases: it runs on-premise, in the cloud and in hybrid environments
  • Use cases where business continuity is crucial, thanks to its high availability and resilience - even in the event of failures

Choosing the optimal data warehouse for your embedded dashboards

There are many data warehouses to choose from, but which one is best depends on your specific situation. To make the right decision, you’ll need to take many factors into account:

  • Integrations with your existing cloud provider, data stack or data sources
  • Whether you’re running on the cloud, on-premise or in hybrid environments
  • The type of data you’re visualizing (e.g. time series or real-time data)
  • Your workloads (e.g. predictable or fluctuating)
  • Your specific use case (e.g. the need for machine learning, forecasting, etc.)
  • Your budget
  • Your team’s skillset and the potential learning curve

With the pointers above, you’re well on your way to shortlisting the right solutions and kick-starting your journey to finding the right data warehouse. And if you’re looking for a tool that can visualize all that data in interactive, beautiful reports, seamlessly embedded inside any SaaS product, look no further than Luzmo’s embedded analytics platform.

Grab a free trial today, or get in touch with our product experts for a guided tour. They will be able to advise you on the right data stack for optimal analytics performance too!

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