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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.

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

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

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

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

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

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

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

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

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

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

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

Apache Kafka is a system for managing large volumes of events efficiently.

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

Choosing between a shared database and a database per service affects system design significantly.

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

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

You can replay Kafka events safely by separating live and replay processes.

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

Replicas keep your system available, while snapshots allow for data recovery.

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

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

Backpressure prevents system overload by managing message processing rates in Kafka.

Database replication enhances data availability and read performance but has challenges like replication lag.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Cross-region database replication keeps databases synchronized across different geographical locations.

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

Transactions ensure database operations are reliable using ACID properties.