Why We Chose Elasticsearch 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 Elasticsearch, S3 and DynamoDB on AWS to deploy a low-cost and highly-scalable analytics data engine.

Challenges & Solutions With Relational Databases

CHALLENGE

Query Speed

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.

SOLUTION

Query Performance

Elasticsearch has its foundations in search applications and is optimized for performance. Index and aggregation features result in larger queries in less time.

CHALLENGE

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.

SOLUTION

Flexible & Adjustable

Elasticsearch 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.

CHALLENGE

Cost Optimization

Relational database servers remain expensive because they are not optimized for changing infrastructure. AWS Redshift can cost almost 10x as much as an AWS Elasticsearch Service!

SOLUTION

Up To 75% Cost Savings

Elasticsearch queries require less compute power compared to SQL queries or AWS Redshift. This drastic reduction in compute translates to much lower infrastructure costs.

CHALLENGE

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.

SOLUTION

Real-Time Analytics

Given the flexibility benefits, Elasticsearch has been known for analyzing log data that is uploaded in various formats. This mean use cases like IoT analytics are optimized within Elasticsearch.

“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

Additional Resources

Five Facts About Elasticsearch That Can Save Big Money

Learn how a Qrvey realized performance gains with Elasticsearch.

Learn More

Moving Beyond The Data Warehouse

Learn more about how Elasticsearch is used to power the Qrvey platform.

Learn More

Learn How to Save Big on AWS Analytics

If you’re a SaaS provider using AWS, Qrvey can save you up to 92%.

Learn More

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