The Enterprise Ready Conference is a one-day event in SF, bringing together product and engineering leaders shaping the future of enterprise SaaS.
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This week’s system design interview:
Big Data Pipeline Cheatsheet for AWS, Azure, and Google Cloud
A Cheatsheet on Comparing API Architectural Styles
10 Key Data Structures We Use Every Day
A Cheatsheet to Build Secure APIs
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Big Data Pipeline Cheatsheet for AWS, Azure, and Google Cloud
Each platform offers a comprehensive suite of services that cover the entire lifecycle:
Ingestion: Collecting data from various sources
Data Lake: Storing raw data
Computation: Processing and analyzing data
Data Warehouse: Storing structured data
Presentation: Visualizing and reporting insights
AWS uses services like Kinesis for data streaming, S3 for storage, EMR for processing, RedShift for warehousing, and QuickSight for visualization.
Azure’s pipeline includes Event Hubs for ingestion, Data Lake Store for storage, Databricks for processing, Cosmos DB for warehousing, and Power BI for presentation.
GCP offers PubSub for data streaming, Cloud Storage for data lakes, DataProc and DataFlow for processing, BigQuery for warehousing, and Data Studio for visualization.
Over to you: What else would you add to the pipeline?
A Cheatsheet on Comparing API Architectural Styles
It covers the 6 most popular API architectural styles:
SOAP
REST
GraphQL
gRPC
WebSocket
Webhook
Over to you: Which other architectural style have you used?
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10 Key Data Structures We Use Every Day
list: keep your Twitter feeds
stack: support undo/redo of the word editor
queue: keep printer jobs, or send user actions in-game
hash table: cashing systems
Array: math operations
heap: task scheduling
tree: keep the HTML document, or for AI decision
suffix tree: for searching string in a document
graph: for tracking friendship, or path finding
r-tree: for finding the nearest neighbor
vertex buffer: for sending data to GPU for rendering
Over to you: Which additional data structures have we overlooked?
A Cheatsheet to Build Secure APIs
An insecure API can compromise your entire application. Follow these strategies to mitigate the risk:
Using HTTPS
Encrypts data in transit and protects against man-in-the-middle attacks.
This ensures that data hasn’t been tampered with during transmission.
Rate Limiting and Throttling
Rate limiting prevents DoS attacks by limiting requests from a single IP or user.
The goal is to ensure fairness and prevent abuse.
Validation of Inputs
Defends against injection attacks and unexpected data format.
Validate headers, inputs, and payload
Authentication and Authorization
Don’t use basic auth for authentication. Instead, use a standard authentication approach like JWTs
Use a random key that is hard to guess as the JWT secret
Make token expiration short
For authorization, use OAuth
Using Role-based Access Control
RBAC simplifies access management for APIs and reduces the risk of unauthorized actions.
Granular control over user permission based on roles.
Monitoring
Monitoring the APIs is the key to detecting issues and threats early.
Use tools like Kibana, Cloudwatch, Datadog, and Slack for monitoring
Don’t log sensitive data like credit card info, passwords, credentials, etc.
Over to you: What else would you do to build a secure API?
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