10 Data Warehousing Tools for Robust Data Processing

Photo of Ignacy Janiszewski

Ignacy Janiszewski

Updated Sep 6, 2023 • 15 min read

It’s usually a challenge to choose a data warehousing tool, especially if you have little or no data engineering experience.

Even for those who know the field pretty well, there are many types of data warehousing tools to consider. Which one’s the best for you? It’s all about knowing your data – perfectly.

Which data warehousing tool should you use? If you know your data – what you have and what the expected outcome is – the choice is a little simpler.

Almost every business collects a lot of data; it’s so trendy these days. But why do you collect it? How do you process it and extract valuable insights? Every click on the Internet is collected, because “data is the new gold”. Unfortunately, the process often stops there. Data is gathered but hardly used.

Here are a few examples of bottlenecks:

  • Data volume is too high
  • Data format is hard to process
  • Planned or currently used infrastructure is too expensive
  • Value of the data is underestimated due to its quality issues

These obstacles are usually caused by a lack of knowledge, time, or people – or a combination.

In this article, we’ll outline a range of top data warehousing tools that are on the market, and what to think about when choosing one. A quick explanation - in this article I’m writing about data warehouses as tools.

Technically, a data warehouse tool is something more specific - for example a part of a data warehouse which differs it from other data warehouses (example: Snowflake Streams and Tasks). But as we did some research, not-technical people who look for data warehouse tools often mean the entire data warehouse, not a part of it.

Why do you need data warehousing tools?

There’s a lot to be gleaned from data processing – if only it was simpler . Often, the problem is how to process the data in one place, at a price that isn’t hellishly expensive. It’s also about ensuring the data is accessible to everyone who can benefit from it – not just highly specialized employees and data engineers.

It’s important to plan your data pipeline appropriately, based on the data you have. With that in mind, consider:

  • What are the goals of processing the data – i.e., data analytics, or will it benefit your client?
  • Data type
  • Data volume
  • How is the data processed – streaming real-time, batch, micro-batch
  • How quickly is the output needed
  • Who will use the data

If you have high-volume data from many sources and you want to analyze it, you definitely need a data warehouse. To ensure it works effectively, you should use a data warehousing tool.

Simply speaking, a data warehouse tool collects data from many different sources, processes it, and makes it easy for data scientists and business analysts to analyze it. If you have a well-built data flow coupled with a data warehouse tool well-matched with your needs, processing the data shouldn’t be difficult, expensive, or time-consuming.

Furthermore, once you’re all set up, your data processing should be easy to maintain. The main goal of a data warehouse tool is to get your data in one place, at the right time, with minimum human supervision – think of it as a reporting tool.

What exactly is a data warehousing tool?

The main goal of a data warehouse is effective data management. It collects data from many different sources, then cleans and loads it into the warehouse. From there, the data is ready to be used for business and data analysis purposes. The main role of the tool is to make the whole process as easy, cost-effective, or fast as possible, depending on what’s most important to your business.

Newer data warehousing solutions are a compromise between a data lake and a data warehouse, because they process semi-structured data. Some data warehouses also have ETL capabilities but they are not Extract Transform Load (ETL) tools as such.

How is a data warehouse different from a data mart or a data lake?

Usually, a data mart is part of a data warehouse, specially selected for a specific group of stakeholders. It’s smaller than a data warehouse – typically less than 100GB, whereas a data warehouse is usually greater than 100GB, and more often in terabytes or even petabytes.

A data lake is also used to store data, but in this case it’s raw, unstructured data. This is dictated by the purpose of a data lake, that is to gather collected data but without any particular goal – that’s why data isn’t processed there. Here, data scientists may uncover some not-so-obvious insights to analyze in the next steps.

This raw unstructured format allows the data to be pulled directly into other processes such as machine learning or programming based analysis. This is because unstructured data is usually more complex for the human brain to comprehend and that's why machine learning or programmatic approach is used.

Data lake can also serve as a security measure. It secures the data in case of vendor or tool locking, stored data is cheaper and easier to migrate to a new data warehouse when a better solution comes to the market.

For humans, data needs to be organized to be understandable and that's the purpose of data warehouses where data is broken down into more organized structures. If your goal is more explicit – business analytics or a sharable data analytics solution for clients – you should use a data warehouse, to organize and query data using SQL.

Top data warehouse tools

Not so long ago, most big data projects were built on Hadoop – an open-source framework that uses a network of many computers, making computations like data processing much faster. But data processing on Hadoop demands substantial engineering work, on-premise servers (however, currently you can use Hadoop on cloud), and a lot of everyday maintenance.

Right now, there are many popular data warehouse tools that fall under a software as a service (SaaS) model or are open-sourced. Data warehousing solutions like these don’t require such commitment and knowledge and they have a cloud-based payment model, meaning you only pay for what you use.

The biggest fish in the world of data warehousing

The best-known data warehousing tools may not be the cheapest, but they’re cost-effective and fully supported by providers. That often occurs to be a huge advantage for a client wanting to focus on the output. Most are already cloud-based data solutions in a SaaS model.


Snowflake is a leader, with over 20% market share (after datanyze.com) when it comes to data warehousing tools. The official documentation states:

“Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings.(...) It combines a new SQL query engine with innovative architecture natively designed for the cloud.”

So at first glance, Snowflake looks like a typical data processing tool, but it’s really a game-changer in the industry – affordable, scalable, user-friendly, and improves performance of data governance.

Snowflake can’t be run on-premise – it runs entirely on cloud infrastructure. As a result, there’s no hardware maintenance for the end-user, including installation, configuration, and management. There are a few cloud providers that allow data integration with Snowflake (GCP, Azure, AWS, etc.), so choose whichever suits you and your business needs best.

Google BigQuery

BigQuery (BQ) is a tool provided by Google, meaning it’s only usable on Google Platform Cloud (GCP). For that reason, it may be a no-go for some, but it’s definitely worth getting acquainted with.

BigQuery is a hybrid system, so you can store data in a relational database, but also make the most of additional features from NoSQL. For example, some JSON can be stored, and then you can query for the nested part of it.

This data warehouse is based on many of GCP’s tools, and became generally available in 2011. The official documentation says that BQ is based on serverless architecture and has a scalable, distributed analysis engine. That’s why you can query terabytes in seconds and petabytes in minutes – impressive! BQ also has built-in machine learning, business intelligence, and other key features from the GCP portfolio.

What’s more, new customers get $300 for free to spend on GCP, so that’s a great way to check how BigQuery works and how it can benefit you. They also provide a lot of public datasets, making playing with data easier. Moreover, all BQ customers receive 10GB of storage and 1TB of queries free per month.

Keen to explore Snowflake and BQ more? Our data warehouse comparison article gives you the lowdown.

Amazon Redshift

Redshift is also a petabyte-scale service; it’s provided by Amazon as an Amazon Web Services (AWS) product. This data warehouse is run on a set of nodes, Amazon Redshift cluster, so you need to manage it. That represents an opportunity, but could also be a bottleneck. Redshift is a relational database that connects easily with AWS business intelligence, data analytics, and reporting tools.

Redshift is very effective because of “massively parallel processing, columnar data storage, and very efficient, targeted data compression encoding schemes”.

Azure Synapse Analytics (previously SQL Data Warehouse)

The next tool is from Microsoft Azure – the next huge player in the “cloud services providers” arena. Built on top of Spark, it’s an “improved” version of Hadoop – an open-source tool with distributed data processing (computations split into many computers), but the processing takes place in RAM, making it much quicker.

As with previous cloud providers, Azure also offers well-connected services – for example, PowerBI for visualizations.

It’s not obvious to use SQL and Spark together, but as you can read in the official documentation Synapse Analytics removes the “traditional technology barriers”. Tables defined on files are easily consumed by Spark, and “SQL and Spark can directly explore and analyze Parquet, CSV, TSV, and JSON files”. What’s more, data can be ingested from over 90 data sources.

When making a data warehousing tool decision, whether you already use the services of a particular cloud provider may be a crucial factor. If so, you’ll likely stick with the tools associated with that vendor. If that’s not an all-important consideration, perhaps Snowflake is the best choice. It’s worth sampling all of the tools, to ensure which is the right fit for you and your business.

Can you build an open-source data warehouse?

If you have sufficient budget, use an SaaS model where you don’t have to worry about infrastructure or bugs in code. But, what if you don’t want to pay for a tool? It’s possible to use an open-source data warehousing tool.

This enlightening “Use these open-source tools for Data Warehousing” article describes data warehouses made by hand using Druid (data storage), Superset (query data), and Airflow (task orchestration). Perhaps not the best solution out there, but it shows you that there are other options aside from huge cloud providers.

Building a data warehouse: additional resources

There are other tools that don’t necessarily have all the features of bigger data warehousing solutions, but are still worth considering. Indeed, sometimes you need narrower functionality.


Teradata is a relational database management system (RDBMS), created for huge volumes of data by massively parallel processing. It was designed for on-premise servers only, but right now it’s also available as SaaS in a cloud environment.


This open-source database system has been developed by volunteers from all over the world for over 20 years. It’s an object-relational database that uses SQL language and supports both SQL (relational) and JSON (non-relational) querying. This tool is usually used as a database – often for applications – but you can also use it as the first data management step.

IBM Db2 Warehouse

Here you have a client-managed data warehouse with in-memory data processing. IBM Db2 Warehouse can run in private clouds, so it’s intended for companies that want control over their data coupled with cloud-like features such as flexibility.


HANA – High-performance Analytic Appliance – is an in-memory database, meaning data is stored in memory rather than on disk. That makes it much quicker than regular solutions. It’s also a RDBMS.


This is another open-source tool, developed by former MySQL employees who had concerns about Oracle Corporation’s acquisition. That’s why MariaDB is intended to be highly compatible with MySQL – database overwrite capabilities, plus exact match to APIs and commands.

Oracle Exadata

It’s possible to run Oracle Exadata on-premise or in the Oracle Cloud. On Oracle Cloud Exadata is run on pre-configured hardware (developed by Sun Microsystems) combined with software (compute and storage servers). You can use Exadata as a data warehouse based on its high-performance hardware configuration.

How to choose the best data warehousing tool?

There’s no simple answer to that question. Answering these questions in the first place may come handy in making the decision:

  • Should your data warehousing tool be on cloud or on-premise? Or maybe open-source?
  • Who is currently on board in your team – do you have any experts or data engineers?
  • Do you need a tool that demands as little engagement as possible (or the opposite)?
  • What budget do you have?
  • Which cloud provider are you currently working with?
  • Would you like data warehouse software that’s easy to implement and maintain and scalable?
  • What support is on offer from the community or the provider you’re considering?
  • What data sources and data sinks do you have, and are connections with other sources and visualization tools important?
  • What data do you have – format, overall volume, and how quickly will new data arrive?
  • How quickly do you need to see the output?

At Netguru, we often work with Snowflake, and have Snowflake experts on board – some boast certifications. But it’s not the only data warehousing tool we use, the choice always depends on the goal. Regardless, it's always good to have a clear idea of your expectations, be aware of your options (and their prices), and sample data warehousing tools where possible.

Get to know your data before making a decision

With data engineering, one thing’s for sure: change. Data processing technologies grow fast, and it’s not easy to keep up-to-date. Also, every business case is totally different, and there’s no one data approach that solves every challenge perfectly.

The best data warehousing tool for one organization won’t necessarily be the right choice for another. Therefore, we recommend that you get to know your data. What data do you have and what do you want from it?

Plus, it’s always a good idea to talk to people who know your industry well and get in touch with experts who can guide you in making this decision and help you with the development after choosing your perfect data warehousing tool.

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Ignacy Janiszewski

Data Engineer at Netguru

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