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

Kafka uses state stores to remember previous events in stateful stream processing.

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

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

Event time is when an event occurs, while processing time is when it is handled.

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

Downsampling and aggregation reduce data volume while preserving essential trends.

Data lakes store large volumes of diverse data for analytics and processing.

Choosing the right labels in time-series data is crucial to avoid inefficiencies.

Transactions ensure database operations are reliable using ACID properties.

ETL transforms data before loading, while ELT loads raw data and transforms it later.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

Data Lakehouse architecture merges flexible data storage with reliable analytics capabilities.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Kafka can help ensure messages are processed once, but requires careful design.

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.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

API pagination helps manage large datasets by breaking them into smaller parts.

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

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

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

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

Container data can be lost unless stored in persistent volumes.

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

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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

Change Data Capture allows real-time syncing of database changes to other systems.

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.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

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

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

CDC with Kafka allows real-time updates from databases to multiple services.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

Backup strategies include Full, Incremental, and Differential, each with unique benefits.

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

Kafka commit strategies determine how offsets are managed during message processing.

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.

Write-Ahead Log ensures data is safely recorded before changes are made.

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

Database isolation levels determine how transactions interact and affect data consistency.