Top 7 Data Warehouse Tools

by on November 1, 2018 in Analytics, Data Management, Data Science, Data Warehouse

The global data warehouse market is expected to grow by approximately 8.3% between 2019-2024! Surpassing a total market value of $20 billion by 2024, a data warehouse is no longer just a buzzword or a novel idea. It is now mainstream as a growing cadre of organizations have turned to this methodology for their data storage needs.

Let’s dive in to know more!


Over recent years, an array of vendors have flooded the market with numerous data warehousing technologies. In this blog post, we’ll be comparing the top seven data warehouses—PostgreSQL, MySQL, MongoDB, Redshift, Snowflake, Oracle 18c, and Microsoft SQL Server on seven primary pointers:

1. Data Volume

  • PostgreSQL and MongoDB are great for handling datasets having a volume up to 1TB and 5TB respectively.
  • Amazon Redshift, Snowflake, Oracle 18c, MySQL, and Microsoft SQL Server support a dataset size up to multiple petabytes.

2. On-premises vs Cloud

  • If you have optimal resources that can handle the maintenance of warehouses, you can go ahead with the PostgreSQL, MySQL, or MongoDB DWHs.
  • But if you do not have as many resources for maintenance, then go for a data warehouse solution that is cloud based such as Redshift, Snowflake, Oracle 18c, or Microsoft SQL Server.

3. Data Format

  • MongoDB uses JSON-like documents with schemata to handle a vast amount of unstructured data. It generally contains hierarchical data which is both an advantage and a disadvantage because very few data warehouses can store unstructured data.
  • In case you want to store or process structured data then PostgreSQL, Redshift, Snowflake, MySQL, Oracle 18c, or Microsoft SQL Server will be an appropriate option.

4. Ease of Integration

  • While PostgreSQL, MySQL, Snowflake, Redshift, Oracle 18c, and Microsoft SQL Server have nearly the same mechanism for integration, MongoDB has a complex integration due to JSON formatting and handling of unstructured data.

5. Performance

  • Owing to functionalities such as parallel processing, cache memory, SSD storage etc. Amazon Redshift performs better than other data warehouses in the paid DWH category.
  • Amongst the open-source ones, PostgreSQL delivers better performance than MongoDB and is easier to use.

6. Scalability

  • In terms of scalability, Redshift is very easy to scale as it lets you increase the number of nodes and configure them to meet your needs.
  • Snowflake can achieve maximum scalability on the storage layer because it is programmed to be completely scalable, independent of computing resources.
  • Microsoft SQL Server can be scaled by sharding or partitioning a larger dataset into small datasets and distribute them across multiple servers.
  • MongoDB implements horizontal scaling using the sharding mechanism. Distributing data to process across several servers makes high throughput operations easy to compile.
  • PostgreSQL is capable of both horizontal and vertical scaling. While scaling vertically makes parallel processing possible which helps utilize multiple cores, thus improving performance.

7. Pricing

  • In comparison with Snowflake, Redshift is slightly expensive if it is to be used for shorter periods of time. However, the cost decreases if it is used in the long term.
  • MongoDB and PostgreSQL are open-source and can be used for free.


Key Takeaway

Among open source models, our concluding lines are that PostgreSQL and MySQL have similar functionalities. However, our vote goes to the former due to the performance, handling, and functionality. MongoDB, on the other hand, is unique as it handles unstructured data (JSON formatted)

In case of paid data warehouses, we prefer using Redshift over Snowflake, Oracle 18c, or Microsoft SQL Server as Redshift’s performance and query optimizing processes are better than the others.

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