Why We Chose OpenSearch As Our Analytics Data Engine
Modern analytics requires a new approach to the data warehouse
The Analytics Data Warehouse Needed to Evolve
When Qrvey set out to move analytics beyond just visualizations, we quickly realized that legacy, relational databases simply couldn’t keep up with today’s data needs. That’s why we pioneered a whole new approach to the traditional data warehouse. Qrvey combines the power of OpenSearch, S3 and DynamoDB on AWS to deploy a low-cost and highly-scalable analytics data engine.
Challenges & Solutions With Relational Databases
Relational databases require time-consuming data preparation. With small datasets, this might only be a few seconds, but as data volume grows, so does latency.
OpenSearch has its foundations in search applications and is optimized for performance. Index and aggregation features result in larger queries in less time.
Data Types & Sources
Relational databases need data in very specific formats. This is the primary reason behind the lack of analysis on various data types such as documents, text, and media.
Flexible & Adjustable
OpenSearch is a NoSQL data store. It can handle changing data structures at any time without preprocessing or relationship configuration. This is extremely important for analytics.
Relational database servers remain expensive because they are not optimized for changing infrastructure. AWS Redshift can cost almost 10x as much as an AWS OpenSearch Service!
Up To 75% Cost Savings
OpenSearch queries require less compute power compared to SQL queries or AWS Redshift. This drastic reduction in compute translates to much lower infrastructure costs.
Time Sensitive Analysis
Relational databases need relationships and those take time to build and query. Real-time data is always changing and relational databases don’t adapt to new fields very easily.
Given the flexibility benefits, OpenSearch has been known for analyzing log data that is uploaded in various formats. This means use cases like IoT analytics are optimized within OpenSearch.
“One of the limitations of traditional BI software is it that it requires data to be in rigid, predefined structures. But today’s technology can adapt on the fly to our customer’s ever-changing data needs.”
~ David Abramson, Qrvey CTO
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Moving Beyond The Data Warehouse
Learn more about how Elasticsearch is used to power the Qrvey platform.