Why Embedded Analytics Matters: Unleashing Data Insights within Applications

Embedded analytics is becoming an indispensable capability for modern SaaS applications across industries. By embedding analytics directly into applications, insights can guide internal application users and external customers to enable better and faster decision-making.

A strong embedded analytics solution that SaaS companies can benefit from starts with the data layer. Many SaaS companies try to determine the best database for their SaaS solution and quite often it becomes an AWS Redshift vs Snowflake comparison.

Exporting data to external business intelligence tools for analysis is becoming less common. Leading organizations are realizing the competitive advantage and monetization opportunities of using live data within their apps, so choosing the right database matters.

Data Warehousing: The Engine Powering Embedded Analytics 

To enable real-time and/or multi-tenant embedded analytics, applications need a high-performance data warehousing layer. This needs to be an efficient process of queries that serve up data analysis.

The data warehouse organizes and stores data from various sources specifically for:

  • reporting
  • data visualization
  • dashboards
  • automation workflows and alerting
  • and various other analytics applications use cases.

Choosing the right data warehouse is therefore critical.

Choosing the Right Tool: Redshift vs Snowflake

Two leading cloud data warehouse contenders that show great promise for embedded use cases are AWS Redshift and Snowflake. Both platforms offer advantages such as scalability and flexibility which suit them well for embedded analytics. We compare the two options across crucial criteria to determine which choice best meets embedded needs.

Head-to-Head: Unveiling the Strengths and Weaknesses of Redshift vs Snowflake

What is AWS Redshift

AWS Redshift is a fully managed, petabyte-scale data warehousing service provided by Amazon Web Services (AWS). It is a cloud-based, massively parallel processing (MPP) database optimized for analytical and reporting workloads. This makes it useful for powering dashboards, ad-hoc queries, and data warehousing.

Redshift provides fast query performance by using columnar storage and parallel processing to quickly analyze large datasets using multiple nodes.

Many enterprises rely on Redshift given its ability to handle heavy analytics workloads. To manage those larger workloads, Redshift can scale storage and compute capacity independently. This allows flexibility to pay only for what you need.

Scalability and Performance: Brute Force Meets Efficiency with Redshift

A pioneer in cloud data warehousing, Redshift offers fast query performance leveraging a massively parallel processing (MPP) architecture optimized for high throughput analytics workloads. Redshift allows scaling compute and storage separately on demand, automatically distributing data across nodes.

Performance remains high even with ultra-large datasets and complex queries. Users have reported 50-100x faster queries near petabyte scale.

Cost-Effectiveness: Pay-as-You-Go vs Predictability 

As part of AWS, Redshift offers pay-as-you-go pricing allowing optimization of costs based on current needs. However, costs can vary significantly based on changing query volumes, underlying data sizes, and other factors – making longer-term budgets and forecasts difficult. Cost optimization requires continual fine-tuning of Redshift clusters and workload monitoring.

For embedded analytics specifically, this cost model requires careful management as SaaS usage is meant to grow over time.

Deployment and Management: The AWS Ecosystem Advantage

Being natively part of AWS, Redshift enables deployment leveraging other AWS services for storage, ETL, monitoring and more. Companies already using AWS experience less management overhead as a result. But reliance on AWS also leads to vendor lock-in – migrating to other platforms would require significant re-architecture.

User-friendliness: Is Redshift Beginner-Friendly?  

Redshift exposes a standard SQL interface for executing queries. However optimal configuration and cost management require deeper expertise in areas like cluster sizing, workload management and query optimization. The platform may present a learning curve for beginners.

Read: 3 reasons engineers struggle with Redshift for multi-tenant analytics

What is Snowflake

Snowflake is a cloud-based data warehousing service that offers a unique architecture optimized for scalability, flexibility and performance in the cloud. It utilizes a multi-cluster, shared data architecture to efficiently separate storage and computing. This allows independent scaling of resources to match workload demands. Snowflake also has native support for public clouds AWS, Azure, and GCP cloud platforms.

The decoupled storage/compute architecture can auto-scale clusters and warehouse capacity based on query volumes and data sizes. This provides high concurrency and performance, similar to Redshift.

Snowflake uses a SQL database engine optimized for data warehousing workloads such as analytics, dashboards, reporting, etc.

Elastic Power: Scale on Demand, Pay for What You Use with Snowflake

Snowflake pioneered a unique cloud-native architecture optimized for flexibility and scalability. The decoupled storage and compute allow auto-scaling to handle extreme workloads without overload. Snowflake also offers per-second pricing – pay only for the capacity used per query without paying for idle clusters.  

This has similar concerns to Redshift for embedded analytics use cases. As SaaS usage increases, companies realize that usage remains consistent throughout the day, contrary to their initial expectations. These cost increases present challenges with Snowflake embedded analytics.

Cloud Agnostic Freedom: Beyond the AWS Walls 

A multi-cloud and hybrid cloud option, Snowflake avoids vendor lock-in by deploying across AWS, Azure, and GCP. Snowflake offers easy migration between clouds with push-button cloud failover capabilities. Snowflake also offers flexibility to query data in external stores without copying across the warehouse.

Rich Data Ecosystem: Seamless Integration and Collaboration

Snowflake is a strong hub for sharing and exchanging data. It helps teams, partners, and other stakeholders access and collaborate on data easily. Snowflake also offers extensive compatibility with third-party tools.

Future-Proof Innovation: Embracing the Evolution of Analytics

With rapid innovation across query processing, security, compliance, and machine learning capabilities, Snowflake is leading the way in cutting-edge features for modern internal analytics. Their unique architecture choices make it easy to evolve the platform over time. Organizations can benefit from new capabilities without migrations.

Read: 3 reasons engineers struggle with Snowflake for multi-tenant analytics

Embedded Analytics: Where Redshift and Snowflake Shine (and Stumble)

Real-Time Insights: Delivering Data at the Speed of Thought to SaaS Users

The best embedded analytics tools require querying and aggregating live, real-time data with minimal latency. This drives contextual insights and guided action within apps. Both Redshift and Snowflake leverage MPP architectures to enable speedy analysis across large datasets.

Slight advantages go to Snowflake for its adaptive elastic scaling and per-second pricing which optimizes costs for spiky query workloads common in real-time dashboards and applications.  

Simplicity and Integration: Seamless Embedding for User Delight

For delightful embedded experiences, analytics components need easy integration and simple configuration within applications built using various programming languages, frameworks and platforms. Both data warehouses offer standard JDBC/ODBC connectivity for executing SQL queries from within apps.

Redshift may have quicker learning curves for current AWS application teams. But Snowflake offers SDKs for more turnkey embedding across diverse tech stacks.

Security and Compliance: Building Trust with Embedded Data 

Embedded analytics puts live data directly into apps, so security and controls are paramount. Both Snowflake and Redshift enable enterprise-grade user access controls. This includes encryption and data governance capabilities leveraging the underlying cloud infrastructures.

For highly regulated industries like healthcare SaaS, Snowflake offers additional native capabilities to track data usage, mask sensitive data, and implement fine-grained access policies.

Big Data Challenges of Redshift vs Snowflake: When Volume and Variety Demand More  

Use cases are constantly expanding to big data sources like IoT analytics, clickstreams, or genomics data. This also means the volume, velocity, and variety of data can push conventional systems over the edge. Ingesting semi-structured data like JSON events gets tricky. (Although Qrvey handles all data natively)

Serverless options on Snowflake like Snowpark handle varied data with less friction. Handling data volumes above 100s of TB can stretch Redshift capabilities. At massive scales, Snowflake better absorbs extreme spikes in storage and concurrent users.          

Picking the Champion for Your Use Case in This Redshift vs Snowflake Decision   

Cost Considerations: Balancing Budget and Performance

AWS Redshift follows typical cloud pay-as-you-go pricing with node-based commitments. Cost efficiencies kick in at higher scales above a few TB.

Snowflake’s per-second pricing and adaptive scaling remove overhead for idle clusters. However, per-second billing can also lead to unexpected spikes on shared systems with uneven workloads. Cross-cloud deployment, data sharing, and BYOL options on Snowflake provide more levers for optimization. Read more about Snowflake cost optimization or try our Snowflake Cost Optimization Calculator.

Technical Requirements: Matching Capabilities to Needs   

Redshift provides a tightly coupled solution with quick time-to-value for simpler analytics integrated into AWS-centric application environments. More complex use cases like large-scale machine learning, and hybrid transactional/analytical processing may benefit from Snowflake’s more advanced architecture. Snowflake better fulfills needs for multi-cloud flexibility or rich data-sharing ecosystems.

Future Vision: Choosing a Platform to Grow With in Your Redshift vs Snowflake Comparison

Snowflake’s platform is cloud-based offering fast innovation in security, compliance, data science, and governance. This makes it an ideal solution for the long term…assuming costs are kept in check.

The underlying separation of storage and compute eases future migrations. Future-proofing for unforeseen changes favors Snowflake, but Redshift is still likely a good option.

Beyond the Battlefield: Collaboration and Hybrid Solutions 

The data warehousing landscape continues to evolve rapidly, with the boundaries between Redshift, Snowflake and other platforms becoming more porous over time. Rather than a winner-take-all dynamic, we see increasing convergence and collaboration between platforms.  

Many organizations leverage hybrid solutions with Redshift for high-intensity operational workloads integrated with Snowflake for larger-scale data science experiments. Connectors like the recently launched AWS Redshift integration for Snowflake make interoperation easier.

As analytics use cases grow more sophisticated, matching the ideal platform to each specific embedded scenario will unlock more value than a one-size-fits-all choice.  

The Takeaway: Embracing the Right Data Warehouse for Your Embedded Analytics Journey

The data warehousing engine powering embedded analytics should align with technical requirements, cost constraints and future ambitions. Both AWS Redshift and Snowflake bring unique strengths as the foundation for real-time data applications.

How Qrvey is Different

At Qrvey, we know that a strong data layer is the foundation that makes any embedded analytics solution successful. We are the only solution with a built-in data lake instead of a data warehouse made for multi-tenant, security-first embedded analytics. 

Our experience with SaaS companies shows that data warehouses struggle in multi-tenant environments. Their compute costs tend to grow out of control with concurrent usage and high data volumes.

Futhermore, engineering teams still must build the logical components to connect data warehouses to static dashboards. When a SaaS platform wants to deliver custom fields to each tenant, the custom data models create difficult engineering challenges to overcome.

However, did you know that while we connect with Redshift, Snowflake, PostGres and more, we know don’t use any of these for our native data lake. Discover why we chose Elasticsearch to power our embedded analytics for SaaS applications solution.

Want more about our data layer? Learn more here.

Get a demo of Qrvey

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