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

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

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

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

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

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.

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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

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

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

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

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

FAANG engineers use high-level design principles to create scalable systems.

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

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

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

MongoDB uses replica sets for data redundancy and sharding for scalability.

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

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

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

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

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

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

Cache stampede occurs when many requests hit the database due to a cache miss.

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

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

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

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Kafka uses brokers for data handling and KRaft controllers for metadata management.

Choosing the right distributed lock depends on your system's needs and existing tools.

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.

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

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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Stateless servers are easier to scale because they don't store user session data locally.

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

Cassandra uses clustering keys to sort rows within a partition for efficient querying.

MongoDB uses sharding to distribute data across multiple servers for better scalability.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Database replication helps keep data available and allows systems to handle more read requests.

Event Sourcing stores every action as an event to recreate the current state.

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.