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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.

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

A CDN speeds up web applications by caching content closer to users.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

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

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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

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

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

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

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

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

Encryption at rest and in transit are essential for protecting data both stored and in motion.

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

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

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

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

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

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

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

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

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

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

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

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

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

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

OAuth 2.0 lets apps access user data securely without passwords.

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

Cache eviction policies determine which data to remove when caches are full.

CQRS separates how we change data from how we read data, improving scalability.

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

Availability is about access; reliability is about correct performance.

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

Hashing is one-way for verification, while encryption is two-way for confidentiality.

Consistent hashing helps distribute data across servers with minimal movement.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

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

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

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Cassandra uses partition keys to determine how and where to store data across nodes.

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

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

Logs, metrics, and traces help engineers debug issues in software systems.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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

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

Elasticsearch uses semantic search to find relevant documents based on meaning, not just keywords.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

HTTPS is a secure version of HTTP that encrypts data for safety.

Elasticsearch ILM automates data management through defined lifecycle phases.

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

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