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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

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

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

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

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

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

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Tokenization and analyzers help search engines process and match text effectively.

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

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

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

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

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

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

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

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

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

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

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