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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 product users inside a SaaS app.
The culprit of a slow, heavy dashboard is often the underlying data infrastructure. The key to a smooth dashboard experience is picking the right data warehouse solution that optimally fits your data and use case.
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…
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 reporting and analytics processes.
Although data warehouse and ETL (Extract, Transform, Load) are often mentioned in one breath, they mean different things. Imagine a data warehouse as a library where data is stored, categorized and labeled. You can retrieve your data from the data warehouse, to then analyze it in a BI tool.
ETL, on the other hand, is a process - not a tool - that extracts data from different sources, modifies it to the format you need, and then loads that data into a data warehouse. ETL tools can manage this process for you, but some data warehouses also offer ETL capabilities in their suite.
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, and 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!
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.
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Mozart Data is an all-in-one modern data platform that 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.
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Although it’s not a clear-cut data warehouse like other tools on this list, it’s worth mentioning Druid on this list. 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.
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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.
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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.
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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:
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!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.