Building Houseware on Open Platform principles for extensible Product Analytics

The Vision of Open Platformization

I'm thrilled to share our vision of open platformization in product analytics. This concept involves making our platform open and extensible, allowing third-party developers and partners to build on top of it and integrate it with other systems and tools.

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At Houseware, we recognized the limitations of traditional, monolithic analytics platforms. We set out to create a modular, composable, and extensible solution that empowers organizations to build custom product analytics workflows.

Houseware’s architecture allows for easy integration with multiple data sources, providing customization, flexibility, and a promise of accuracy to the product teams while maintaining strong governance and observability in the data warehouse for data teams.

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In this blog, we will cover the following aspects of Houseware’s platform embracing an open platform approach:

• The Power of Warehouse-Native Architecture
• The Art of Composability
• The Foundation: Houseware’s Data Platform
• Cost management
• Query observability
• Unleashing Innovation with an API-First Platform
• Prioritizing Security and Compliance

The Power of a Warehouse-Native Architecture

At the core of Houseware lies our warehouse-native architecture, designed to leverage the center of gravity of your organization's data. Houseware sits directly on top of your existing data warehouse, eliminating the need for complex data transfers and modelling. This approach ensures a secure, scalable, and reliable solution.

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Here's what this means for you:

• Effortless integration with multiple data sources.
• Flexibility and accuracy for your product teams.
• Strong governance and observability for your data teams.

The Art of Composability

Composability is a key tenet of Houseware's platform. Our modular approach enables individual components to be integrated, extended, or replaced without affecting the overall system, ensuring compatibility with your existing data infrastructure.

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We also follow the principle of "Configuration over Convention", offering maximum flexibility to design use cases and solve your unique challenges. This gives you the power to:

• Choose your preferred data warehouse (Snowflake, BigQuery, or others).
• Fit any data model or event stream, updated at any frequency, whether batch or real-time, into the Houseware schema event schema. 
• Build custom events, cohorts, and more.
• Integrate with a wide range of marketing, sales, and business tools.

Houseware empowers you to create a best-of-breed analytics stack that adapts to your evolving needs. Here's how:

The Foundation: Houseware’s Data Platform

A warehouse-native architecture demanded a unique perspective at building multiple workloads that worked agnostic of the storage layer or the query engines.

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Listed below are some of the components of the Data Platform:

Data Schema

Houseware employs an activity schema that organizes events into mandatory and optional attributes. This enables:

• Scalability: The schema efficiently handles large volumes (up to tens of billions) of event data at runtime. It supports high-throughput data ingestion and processing, maintaining robust performance as data scales.
• Flexibility: Accommodates new event types and attributes without requiring schema changes. This allows for seamless integration of new data points as the product evolves, avoiding extensive redesigns.
• Simplicity: The schema is user-friendly and adaptable to various scenarios, making it easy to model and query data. It minimizes the learning curve for users and simplifies data management.
• Efficiency: Optimizes query performance by structuring data for fast retrieval and analysis. The schema supports efficient indexing, clustering, and partitioning, which speeds up query execution times.
• Consistency: Ensures uniform data formatting and structuring across the organization. This standardization simplifies data integration, governance, and compliance.
• Reduced Complexity: This approach minimizes the complexity of data transformations by using a straightforward schema that maps easily to various data sources. This simplification accelerates the ETL process and reduces the potential for errors.

Cost Management (Budgeting, Quotas, and Cost Observability)

Houseware provides tools to manage and observe data processing and analytics costs, ensuring fine-grained control over data warehouse expenditures while optimizing query performance. These tools are:

• Cost Observability: Houseware breaks down costs by query patterns, monitors compute costs and sends alerts for anomalies. If there is an unusual spike or unexpected cost pattern, alerts are sent to the designated Slack channels, enabling prompt investigation and resolution.

• Budgeting & Quotas: Customers can define usage quotas and budgets directly on their Data Warehouse(Snowflake/BigQuery). Budgets for overall data warehouse usage and specific query types or runtimes can be defined. This helps track and control spending, ensuring costs remain within the planned limits.

Query Observability

The Houseware Data Platform has internal query observability to monitor and optimize query performance. These capabilities ensure that queries run efficiently, resource usage is optimized, and any issues are quickly identified and resolved.

Query Logging

Houseware tracks and logs all query executions, providing a detailed record of each query's lifecycle:

• Execution Tracking: Logs the start time, end time, and duration of each query, along with the query text and execution context.
• Error Logging: It captures errors or exceptions during query execution, facilitating troubleshooting and root-cause analysis.
• User and Session Information: Logs metadata about the user and session that initiated the query, helping to correlate queries with specific users or applications.

These logs provide a comprehensive history of query activity, which is essential for performance analysis and security auditing.

Performance Metrics

Houseware collects detailed performance metrics for each query, enabling the identification and resolution of performance bottlenecks:

• Resource Utilization: Tracks metrics such as CPU usage, memory consumption, and I/O operations for each query.
• Execution Plan Analysis: This process analyzes the execution plan of each query to identify inefficiencies, such as suboptimal joins or missing indexes.‍
• Latency Metrics: Measures queries' response times and latency to ensure they meet performance expectations.

Unleashing Innovation with an API-First Platform

Our open platform approach extends beyond data integration. Houseware provides a robust API-first platform, allowing you to build custom analytics interfaces, integrate with existing systems, or develop entirely new applications on our foundation.

This extensibility has fostered innovation among our customers, with some leveraging our APIs to:

• Build custom dashboards
• Automate reporting pipelines
• Develop machine learning models for predictive analytics

Further, Houseware APIs are designed to be easily integrated with your existing systems and applications:

• Authentication: Supports API Key and JWT (JSON Web Token) authentication methods to secure access and ensure that only authorized users and applications can interact with the API.
• Documentation: Comprehensive and clear API documentation, including example requests and responses, parameter descriptions, and use case scenarios, is provided to guide developers through integration.

To start with the Houseware Platform API, refer to our detailed guide here.

Prioritizing Security and Compliance

Security and compliance are paramount in our design philosophy. With our bring-your-own-warehouse model, your sensitive data never leaves your controlled environment, ensuring utmost data protection. This has been a critical selling point for businesses operating in highly regulated industries, such as healthcare and finance, where data privacy and security are paramount.

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Additionally, Houseware is SOC 2 Type II compliant, with annual renewals and rigorous security practices. Read our documentation for detailed information on our core security principles.

Success Stories: Houseware in Action

One of our customers, a leading Edtech platform, has leveraged Houseware's platform to build a comprehensive product analytics ecosystem. By integrating their data warehouse with Houseware, they've consolidated customer journey data from multiple sources, including mobile app usage and transactional data.

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Through Houseware's cohort builder, they've created granular user segments based on user behaviors and properties, enabling targeted analysis and personalized user experiences. Additionally, they've utilized Houseware’s APIs to set pipelines to marketing automation tools, facilitating data-driven campaigns and optimizing customer engagement strategies.

Our platform's success stories and customer testimonials testify to the power of open platformization. We are committed to continuously innovating and pushing the boundaries of what's possible in the realm of product analytics.

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With Houseware, you'll be free to build, integrate, and scale your analytics ecosystem, empowering you to make data-driven decisions that drive growth and success.