How do you compare data warehouse performance across vendors?

1 Define your performance goals

Before you start comparing data warehouse vendors, you need to define your performance goals and expectations. What are the key performance indicators (KPIs) that matter most to you? For example, you may want to measure query response time, data loading speed, concurrency, scalability, availability, or cost. You should also consider your data volume, variety, and velocity, as well as your analytical workloads and use cases. By defining your performance goals, you can narrow down your options and focus on the vendors that meet your requirements.

2 Understand the vendor's architecture

Another important step is to understand how each vendor's data warehouse architecture affects performance. Data warehouse architectures can be classified into two main types: cloud-based and on-premise. Cloud-based data warehouses are hosted and managed by a third-party provider, such as Amazon Redshift, Google BigQuery, or Microsoft Azure Synapse Analytics. On-premise data warehouses are installed and maintained by your own IT team, such as Oracle, IBM, or Teradata. Cloud-based data warehouses offer more flexibility, scalability, and cost-effectiveness, but they may also have more latency, security, and compliance issues. On-premise data warehouses offer more control, reliability, and performance, but they may also have more complexity, maintenance, and cost overhead.

3 Compare the vendor's features

It's essential to have a clear understanding of the vendor's architecture before comparing their features and capabilities that affect performance. When assessing a vendor, consider data compression, partitioning, indexing, caching, formats, query optimization, and monitoring. Data compression is key for reducing storage space and improving query speed. Data partitioning optimizes parallel processing and concurrency. Indexes speed up data retrieval and filtering. Caching uses memory or disk to store frequently accessed data and reduce I/O operations. Data formats impact performance and compatibility. Query optimization reduces resource consumption and improves response time. Query monitoring reports query performance and provides tools for troubleshooting and tuning.

4 Use benchmarking tools

The final step is to use benchmarking tools to measure and compare data warehouse performance across vendors. These applications generate and run standardized queries and workloads on different data warehouse platforms, then report the results. Popular benchmarking tools include the Transaction Processing Performance Council (TPC), which provides industry-standard benchmarks such as TPC-H, TPC-DS, and TPCx-BB. Additionally, there's HammerDB, an open-source tool that supports various data warehouse platforms and benchmarks. JMeter is another open-source tool that can simulate concurrent users and load tests on data warehouse platforms. By using benchmarking tools, you can get objective and comparable data on data warehouse performance across vendors. However, you should keep in mind that benchmarking results may not reflect your real-world scenarios and workloads, so you may need to adjust and customize the benchmarks to suit your needs.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?