StarRocks Use Case: Data Lakehouse

A data lakehouse is a revolutionary data architecture that merges the best of both data lakes and data warehouses. Think of it as a single, comprehensive data “home” where you can store, process, and analyze all your data – structured, unstructured, and semi-structured – in a flexible and efficient way.

Value of Data Lakehouses:

  • Democratized data access: Everyone, from data scientists to business analysts, can access and explore all data in one place.

  • Increased agility and insights: Analyze data as needed, regardless of schema or format, leading to faster discovery and innovation.

  • Reduced costs and complexity: Eliminates the need for multiple data platforms, streamlining data management and reducing overhead.

  • Faster and more accurate analytics: Leverage diverse data sources to build richer models and make better data-driven decisions.

How StarRocks Uniquely Solves Data Lakehouse Challenges:

Traditional data lakehouses often face these hurdles:

  • Performance bottlenecks: Processing large volumes and diverse data formats can be slow and cumbersome.

  • High operational costs: Scaling and managing a complex data lakehouse infrastructure can be expensive.

  • Limited accessibility: Non-technical users might struggle to navigate and analyze data effectively.

StarRocks tackles these challenges with its unique capabilities:

  • Hybrid storage architecture: Combines columnar storage for performance with row-based storage for flexibility, handling structured and unstructured data efficiently.

  • Massively scalable architecture: Scales horizontally to handle petabytes of data and millions of concurrent users effortlessly.

  • Real-time analytics: Processes data streams in real-time, enabling instant insights and reactive decision-making.

  • Easy-to-use tools: Provides intuitive dashboards and visualizations for self-service analytics, empowering all users.

Data lakehouses hold the key to unlocking the full potential of your data, and StarRocks offers a unique solution to overcome the usual obstacles. Its sub-second query engine, hybrid storage, scalability, real-time processing, and user-friendly tools make it a powerful platform for building a truly unified and insightful data lakehouse.

Is Cloudflare R2 supported?

It’s great that it’s s3 compatible storage but I don’t think it’ll work for data storage for a database. I would have questions around latency and IOPS.

Are you sure? Cloudflare says it is faster than S3, and calls out date lakes as an application. Do you have benchmarks otherwise?

You’re welcome to try it and publish your results.

cloudflare says it’s faster than S3

Cloudflare is making the point that cloudflare will have better latency than storage in S3 in their own AWS data center. Cloudflare is a CDN and they’re good at caching. Specifically they say "Spoiler alert: we’re faster than S3 when serving media content via public access. " I can see that if I pulled a binary file or a PNG file, with a CDN it could be faster. How about data that is mutable like database records? At that point, you can’t use the CDN unless it’s static datasets. Also when you store data in S3, you’re bounded by S3 IOPS.

data lakes

That’s a loaded term. I can see a case where I need to to run my data set in python and I need cheap place to host my files. However all the examples I’ve seen is static parquet files. Not something that is mutable. Closest I’ve seen is Snowflake tables on Cloudflare R2 for global data lakes | by Felipe Hoffa | Snowflake | Medium