Analytics Lake

Meet the Analytics Lake

Data, metadata, high performance caching, custom services, and analytics all in one platform!

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What is an Analytics Lake?

The Analytics Lake forms a 'composable data service architecture' layer that combines the best in open-source tech and our semantic and analytics layers, to give both human and machine consumers a single place to find all of their analytics assets.

Simplify data access with a unified service layer

No more moving data

Reduce data duplication and latency, and ensure real-time access to the most current data where it sits.

Tools to connect and orchestrate

S3 for business intelligence, TimescaleDB for real-time analytics, Snowflake for data warehousing, and Databricks for data science.

Efficient Data Management

Ensure that each tool and storage solution delivers the most value without unnecessary data movement or complexity.

Benefits of the Analytics Lake

Use Analytics Lake through your favorite interface — Python for data scientists, React for application developers, SQL for data engineers and, no code/low code and chat interfaces for business users.

Save money with advanced caching

Super-fast, in-memory computation on large volumes of data — on full or partial data sets — for reusability.

Transform and federate with agility

Combine data from your apps via SQL or APIs. Materialize data in-memory or on disk — query live from your databases.

Built on open source tools

Built on tools including Apache Arrow, Iceberg, and DuckDB, pre-configured for core BI use cases.

Power new use cases

Develop your own modules to meet data science and engineering needs directly in your analytics platform.

Introducing FlexConnect

Write custom self-service integrations to any data source.

Integrate niche or homegrown data sources rapidly, reducing time to value and empowering your team to immediately leverage unique data without bottlenecks.

Allow your team to independently build and incorporate any data source into GoodData’s data models, reducing dependency on external support and speeding up deployments.

Connect to virtually any system or data source, including niche or homegrown solutions, using familiar SQL and API standards. This flexibility helps incorporate diverse data assets without complex integration projects.

Embed advanced transformations, data federation, or ML tasks within the connection itself, enabling real-time data insights and a streamlined analytics workflow.

By embedding business logic and governance mechanisms directly within the data source connection, FlexConnect ensures consistent, trusted data flows and robust security, enhancing the quality and reliability of your insights.

The unique purpose of an Analytics Lake

The Analytics Lake is designed to integrate with raw data from a data lake and structured data from a data warehouse to provide advanced analytics, comprehensive business intelligence, and accurate predictive insights, driving better decision-making and outcomes.

	Analytics Lake
Solution
Primary Focus
Scalable raw data storage	Business reporting and decision-making	Analytics and machine learning
Storage Format & Optimization
Optimized for low-cost storage of large raw, unstructured data	Structured data optimized for query performance	Optimized compute for ML workflows
Metadata & Semantic Layers
Limited metadata and semantics	Typically has integrated business metadata	Integrated metadata and headless semantic layer
BI & Visualization Capabilities
Minimal BI support	Full support for BI workflows and visualizations	Integrated BI and visualizations environment
Advance Analytics Support
Strong support for advanced analytics	Basic predictive modeling capabilities	Optimized for advanced analytics and ML
Data Science & ML Support
Supports data science through big data tools	Limited support for data science workflows	Tailored for data science and ML with compute optimization and APIs
Compliance & Governance
Limited governance capabilities	Strong auditing, security, and governance capabilities	Robust governance through metadata integration
APIs & Programmatic Access
Access via big data APIs and notebooks	Traditionally accessed through SQL	APIs designed specifically for analytics engineers
Cost Efficiency
Very low-cost storage but unpredictable analytics costs	Predictable but relatively high storage costs	Optimized processing reduces overall costs
Flexibility & Future Proofing
Flexible but typically requires migration for BI use	Schema-on-write limits flexibility	Designed to interoperate with multiple data platforms
Key Strengths
Scalability, cost efficiency for storage	Performance, consistency, reliability	Purpose-built for advanced analytics and ML
Key Limitations
Challenging to apply governance, reuse data for BI and reporting	Limited flexibility and ability to handle messy, large or streaming data	Emerging architecture

Solution	Data Lake	Data Warehouse	Analytics Lake
Primary Focus	Scalable raw data storage	Business reporting and decision-making	Analytics and machine learning
Storage Format & Optimization	Optimized for low-cost storage of large raw, unstructured data	Structured data optimized for query performance	Optimized compute for ML workflows
Metadata & Semantic Layers	Limited metadata and semantics	Typically has integrated business metadata	Integrated metadata and headless semantic layer
BI & Visualization Capabilities	Minimal BI support	Full support for BI workflows and visualizations	Integrated BI and visualizations environment
Advance Analytics Support	Strong support for advanced analytics	Basic predictive modeling capabilities	Optimized for advanced analytics and ML
Data Science & ML Support	Supports data science through big data tools	Limited support for data science workflows	Tailored for data science and ML with compute optimization and APIs
Compliance & Governance	Limited governance capabilities	Strong auditing, security, and governance capabilities	Robust governance through metadata integration
APIs & Programmatic Access	Access via big data APIs and notebooks	Traditionally accessed through SQL	APIs designed specifically for analytics engineers
Cost Efficiency	Very low-cost storage but unpredictable analytics costs	Predictable but relatively high storage costs	Optimized processing reduces overall costs
Flexibility & Future Proofing	Flexible but typically requires migration for BI use	Schema-on-write limits flexibility	Designed to interoperate with multiple data platforms
Key Strengths	Scalability, cost efficiency for storage	Performance, consistency, reliability	Purpose-built for advanced analytics and ML
Key Limitations	Challenging to apply governance, reuse data for BI and reporting	Limited flexibility and ability to handle messy, large or streaming data	Emerging architecture