As a top-of-the-line cloud data warehouse, Snowflake certainly delivers on its promise and more with the help of third-party data integration and ETL tools. Data is stored and made available in the cloud, and a SQL workbench and user permissions enable multiple users to query data. However, as this powerful data warehousing platform was developed for experienced data architects and data engineers, it can create some technical challenges.
If you're looking for a Snowflake alternative, we’ve got you covered. We researched different variables like cost, ease of use, features, and more. Here are the top 5 alternatives to Snowflake:
Unlike Snowflake, Panoply is both a data warehouse and ETL tool. It's the most straightforward data warehousing solution on the market, combining a secure data warehouse with code-free ETL.
Panoply’s built-in ETL integrations to dozens of data sources are ready to use out of the box, translating into quick and easy setup and minimal upkeep. Panoply users can query data directly within the platform or connect to their preferred analytical notebook or BI tool.
Panoply pricing is both transparent and cost-effective, especially as storage is included in the base cost. As it seamlessly integrates with a wide range of external tools, the overall cost savings from analytics activities are significant.
Panoply pricing: a free trial is available; see all pricing options.
Amazon Redshift is the big kahuna in data warehousing. This fully managed cloud-based data warehouse is fast and ideal for users with large scale data needs (a few hundred GB+). Developed for data engineers and data scientists, Redshift is especially good if you're in deep with AWS/Amazon infrastructure.
While Redshift is certainly high-performing, popular, and powerful, it does have a downside. Users sometimes complain about poor performance when running parallel queries. It also lacks built-in integrations, so you’ll need to factor in the additional cost, setup, and maintenance of third-party tools.
Like Snowflake, Redshift was built for experienced data analysts and data engineers. So, it can be a challenge for inexperienced users.
Amazon Redshift pricing: follows a consumption-based model and is billed on a per-second basis (and a two-month free trial is also available).
Google's cloud-based data warehouse, BigQuery, was developed for experienced data scientists and data engineers, so new users can expect a steep learning curve during the setup phase and beyond.
BigQuery is highly scalable and integrates seamlessly with Google Suite products (like Google Analytics). However, Google BigQuery lacks native integrations to pull data from other non-Google sources. As a result, only users with the necessary technical prowess can leverage third-party tools to pull in data from external sources. Besides storing a massive amount of data, BigQuery is also equipped with a SQL workbench and allows multiple users to query data.
Google BigQuery pricing can be convoluted at times. There are many inputs, including charges for both long-term storage and active storage, flat-rate queries, and on-demand queries. This might prove overwhelming for new users who don't have the necessary experience to forecast consumption rates accurately.
BigQuery pricing: follows a pay-as-you-go pricing structure (and $300 in free credits are available).
4. Microsoft SQL Server
Microsoft SQL Server is one of the most popular SQL database formats that seamlessly combines data analytics and data warehousing. Whether it's an Azure data warehouse or a Microsoft transactional database, Microsoft SQL Server is just about anywhere and everywhere. This translates into relentless demand for robust SQL Server ETL tools for data integration and analytics.
With the emergence of the all-encompassing Azure Synapse Analytics, Microsoft shifted its focus to creating a unified platform with a closed ecosystem for ingesting, preparing, managing, and serving data that can be piped into leading BI and ML tools.
If you're already on Azure, you can leverage Azure Synapse Analytics to almost immediately design the data structure. However, it's vital to note that this no-frills database doesn't come with competitors' rich feature set. This means that Azure Synapse Analytics users have to contend with an intimidating UI and a learning curve that can make the setup seem daunting unless you're already familiar with the Azure environment.
Microsoft SQL Server pricing: volume-based pricing; 180-day free trial available.
If you've worked with data, you're probably more than familiar with the open-source object-relational database system PostgreSQL. Its reputation for performance, stability, and reliability has made it the go-to database solution for large corporations. It also helps that a large and vibrant community supports Postgres.
However, it's basically a database system, and you'll need an ETL tool to push your data into storage. The good news is that the Postgres environment was built to enable considerable savings in operating costs. In this scenario, PostgreSQL maintenance costs can be lower than that of competing products. However, this solution only makes sense for highly experienced data engineers who appreciate its flexible configuration options.
Postgres pricing: is open-source and free (but demands hands-on management).
Comparing ETL Tools
As the big data wars heat up, the data analytics market has grown fiercely competitive, offering more options when it comes to features and costs for data users than ever before. However, the best ETL tool and data warehouse for your organization depend on your project and available resources (including skill set and experience).
In sharp contrast to some of the complex and consumption-based pricing models inherent in the industry, Panoply offers transparent pricing, starting at just $200 per month with no hidden fees. You'll pay for the amount of data stored and the number of data sources used and never pay more to add or remove users or run more queries.