Elasticsearch Data Lake
Elasticsearch as a Data Engine.
Why We Chose Elasticsearch As Our Analytics Data Lake.
Elasticsearch powers our multi-tenant data lake that enables development teams to move faster and build less.
The Data Lake Built Specifically for Multi-Tenant Analytics
When Qrvey set out to move embedded 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 built on Elasticsearch that delivers a low-cost and highly-scalable analytics data engine.
Challenges & Solutions With Data Warehouses
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 performance can scale to 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.
CHALLENGE
Cost Optimization
Relational database servers remain expensive because they are not optimized for changing infrastructure. AWS Redshift or Snowflake can cost almost 10x as much as Elasticsearch!
SOLUTION
Up To 75% Cost Savings
Elasticsearch queries require less compute power compared to SQL queries or popular data warehouses. 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 opens up use cases like building a data as a service.
“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
Elasticsearch vs Snowflake: Which is Better for Data Lakes
A high level overview comparing Elasticsearch to Snowflake for multi-tenant analytics.
| Feature | Elasticsearch | Snowflake |
|---|---|---|
| Analytics Type | Real-time, ad-hoc analysis | Structured data warehousing |
| Data Schema | Schema-less | Structured (tables with defined schema) |
| Search Capabilities | Powerful data search | Limited search functionality |
| Scalability | Horizontal scaling | Separate scaling for compute and storage |
| Cost | Lower compute and query costs | Higher cost for large analytics queries |
Advantages of Elasticsearch Data Lake for Analytics:
- Real-time Analytics: Elasticsearch excels at real-time search and analysis, making it ideal for scenarios where immediate insights are crucial.
- Schema-less Design: Elasticsearch’s schema-less nature allows for ingesting data from diverse sources with varying structures without upfront schema definition. This flexibility simplifies data integration and accommodates structured, semi and unstructured data.
- Powerful Search: Elasticsearch boasts powerful full-text search capabilities, enabling users to search across large datasets with ease.
- Horizontal Scalability: Elasticsearch scales horizontally by adding more nodes to the cluster, allowing it to handle massive data volumes efficiently. This is crucial for a multi-tenant data lake where data ingestion is continuous.
Snowflake’s Advantages:
- Structured Data Warehousing: Snowflake is a cloud-based data warehouse optimized for queries on large datasets of structured data. It excels at historical data analysis and complex joins between various tables.
- SQL Support: Snowflake offers native SQL support, making it familiar for users comfortable with traditional data warehouses. This can simplify querying processes for those accustomed to SQL syntax.
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FAQs About Elasticsearch
Elasticsearch is a powerful search and analytics engine that excels at handling large volumes of data from diverse sources.
Elasticsearch offers several advantages:
- Scalability: It can handle massive data volumes from multiple tenants while maintaining fast search and analysis capabilities.
- Flexibility: It has a schema-less design, allowing for easy integration of data from various sources with different structures.
- Multi-tenancy Support: Built-in features help isolate tenant data and ensure data security within the shared data lake environment.
- Real-Time Analytics: Elasticsearch provides near real-time search and analysis capabilities, enabling faster insights for all tenants.
Elasticsearch offers several security features for multi-tenant deployments:
- Role-Based Access Control (RBAC): Define granular access controls to restrict each tenant’s ability to view, modify, or delete data belonging to other tenants.
- Data Encryption: Encrypt data at rest and in transit to further safeguard sensitive information.
- Data Sharding: Shard your data across multiple nodes for redundancy and prevent a single tenant from accessing the entire dataset.
See Qrvey in Action!
Learn about Qrvey’s embedded analytics platform and get quick answers to your questions by scheduling a demo with one of our embedded analytics experts. See why we’re the logical choice for SaaS companies like you.
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