Any concept.
clear in 60 seconds.

Batch processing collects data to process later, while stream processing handles data in real-time.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

OLTP handles transactions while OLAP is for data analysis and reporting.

Data warehouses help businesses analyze large amounts of data for better decision-making.

Downsampling and aggregation reduce data volume while preserving essential trends.

Transactions ensure database operations are reliable using ACID properties.

B-Trees help databases find data quickly without scanning every row.

Time-series databases are designed to store and query time-stamped metrics efficiently.

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.

Data residency is crucial for compliance with laws about where data can be stored.

Late-arriving events can be dropped, updated, or routed based on system needs.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Retention policies help manage how long data is kept in databases.

The choice between SQL and NoSQL databases depends on your application's specific needs.

Fetch limits control data size from Kafka, while poll records limit processed records.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Cache penetration occurs when requests for non-existent data overload the database.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Redis is a fast in-memory data store used for caching and low-latency applications.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Database per Service ensures each microservice owns its data, reducing dependencies.

HTTP methods tell the server what to do, and status codes tell us the result.

Disaster recovery ensures systems can restore services and data after major disruptions.

REST API design focuses on creating clean, efficient, and scalable web services.

Functional requirements define what a system does, while non-functional requirements define how it performs.