Cloud data platforms have matured significantly, and organisations now expect warehousing technologies to support not only reporting but also data engineering, real-time processing, governance and AI. The right platform depends less on headline market share and more on workload, cloud strategy, operating model and existing skills.

To fully utilise big data, and any data sets effectively, you need to have a reliable place to store it, and the right technology partners. There are different solutions available, and one of the most popular is making use of cloud data warehousing technologies, which allow companies to store and use their data, without the cost and requirements of setting up servers of their own.

This article compares several leading data warehousing and analytics platforms: Amazon Redshift, Databricks, Google BigQuery, Snowflake and Microsoft’s current analytics stack, including Microsoft Fabric and Azure Synapse Analytics.

Amazon Redshift - pros and cons

Amazon Redshift is AWS’s managed cloud data warehouse, designed for large-scale SQL analytics and integration with the wider AWS data ecosystem. It is available in provisioned and serverless models, giving organisations different options for performance, control and operational overhead.

Some of the benefits offered by Redshift include:

• High levels of security – including network isolation, end-to-end encryption, and fault tolerance

• Efficient storage – with high-performance processing, zone maps, columnar storage, and effective data compression

• Extensive support – including a large knowledge base, guides, and support functions

• High-performance query processing – with resources and platforms that support the functions, and centralised data structures for time-efficiency

Some of the difficulties that come with the Redshift platform include:

• Redshift is not a multi-cloud platform

• Payment is dependent on usage and can significantly increase the cost

• Redshift is AWS-specific rather than multi-cloud, and cost/performance still depends on workload design, capacity choices and how the surrounding AWS services are used.

Amazon Redshift use case

Managing 4 petabytes of client data for a leading consumer electronics brand with Amazon Redshift. Read the full story here:

Databricks – pros and cons

Databricks is a data and AI platform built around lakehouse architecture, bringing data engineering, SQL warehousing, streaming, governance and machine learning together on a shared data foundation.

Some of the key benefits of Databricks include:

• Flexibility in processing – with the cloud environment supporting Spark R, SQL, Python or Scala

• Aggregation of datasets in the cloud – with the option for in-line visualisations and organised structure through the notebook format

• Supported by significant knowledge bases – including guides, tutorials, documentation, and user interactions

Some of the cons of the Databricks include:

• Less comprehensive selection of tools

• CPU optimisation is not as well performing as some other platforms

• Data backup feature is not consistently reliable

• Users need a certain experience level and knowledge to utilise effectively

Databricks use case

A global media and broadcasting company monetize their broadcasting data with Databricks for trusted and timely data availablility for real-time, mission-critical data. Read the full story here:

Improving data turnaround by 80% for a Fortune 500 company with Databricks as the technology of choice. Read the full story here:

Google BigQuery – pros and cons

Part of the Google Cloud services platform, Big Query allows for processing, storage, and analytics, as well as providing options for machine learning. Used by a number of large organisations across the world, the platform is considered to be an inexpensive data option.

Key benefits of using Google BigQuery for data warehousing:

• Intuitive interface – which is relatively easy to use with minimal experience, and allows for easy builds of new queries

• Data is automatically optimised – When fetching data, the platform automatically optimises the query to reduce time wastage

• Effective across multiple databases – The tools allow for efficient management and access to different databases

Cons that have been highlighted with Big Query:

• Cannot be used as a substitute relational database

• Orientated for analytical queries and not simple operations

• Requires unique SQL implementation for querying data.

Snowflake - pros and cons

Snowflake is a managed, cloud-native data platform that separates storage and compute and supports structured, semi-structured and unstructured data. Accounts can run on AWS, Microsoft Azure or Google Cloud, with workloads scaling independently through Snowflake’s managed architecture.

Some of the key benefits of the Snowflake :

• Fast and flexible options – The data storage, processing and analysis are easy-to-use and designed with a new SQL query engine

• Cloud-agnostic – The service works with AWS, Google Cloud, and Microsoft Azure

• Multiple data workloads can scale independently – this allows effective management, engineering, and data sharing

• Architecture scales as required – Due to the unique build of the platform, Snowflake can scale up and down based on requirements and workloads.

Cons of Snowflake:

• Not as cost-effective as some competitors

• Snowflake is designed to run on supported public cloud infrastructure rather than as an on-premises installation, which can be a limitation for organisations with strict on-premises requirements.

• Small support community when compared to others

Microsoft Fabric and Azure Synapse – pros and cons

Microsoft’s analytics portfolio now includes Microsoft Fabric as an end-to-end SaaS data and analytics platform built around OneLake, alongside Azure Synapse Analytics for established Azure data warehousing and big-data architectures. Fabric Data Warehouse provides a managed relational warehouse on a lake foundation and is tightly integrated with Power BI.

Some of the key benefits of using Azure include:

• Integrated Microsoft analytics – Microsoft Fabric brings data engineering, pipelines, warehousing, real-time analytics and Power BI together around OneLake, while Synapse continues to combine SQL, Spark, pipelines and Azure data services.

• Shared data and reporting – Fabric uses OneLake and open Delta storage across workloads, reducing unnecessary data movement between engineering, warehouse and BI experiences.

• Flexible SQL options – Fabric Warehouse provides managed SQL warehousing, while Azure Synapse continues to offer both serverless and dedicated SQL capabilities for Azure-based analytics solutions.

Some of the disadvantages of using Synapse:

• Microsoft’s analytics portfolio is broad, so choosing between Fabric, Synapse and related Azure services requires a clear target architecture rather than adopting overlapping capabilities by default.

• Migration and governance requirements should be assessed carefully where organisations already have significant Synapse, Power BI or Azure data estates.

• Licensing, capacity and feature availability differ across Fabric and Azure services, so cost and deployment decisions should be validated against the intended workload.

Leading data warehousing technologies - how to choose the right one

To determine the best data warehouse solution, you need to evaluate your own business needs – what you are currently aiming to achieve, where you will be taking your data in the future (and how you will scale this), and what skills your existing team are working with.

You may choose to work remotely with experienced third-party managed services, but you still need to have the right tools in place to ensure they can get on with their work efficiently and effectively.

When evaluating your needs, you should, at a minimum, consider:

• Employee skillsets

• Future developments

• Costs

• Scalability

Once you know what you want to do with your data, you will have a better idea of the tools that you will need to provide that functionality.

Ardent data warehousing services

At Ardent, we have leveraged powerful technologies to deliver secure, robust and scalable data warehouses to our clients to meet their unique needs and requirements. With our data warehousing service, we take a consultative approach to understanding your challenges, your growth plans, and future developments to handpick the technologies we think are the most suitable. If you are looking to build a data warehouse which enables you to:

• Effectively manage large, complex data

• Make your data accessible and secure

• Gain insights to driver better-informed decisions

Get in touch to find out more about how we can together, unlock the potential of your data.