Suggested Certification for NoSQL

Oracle NoSQL Database Cloud Training and Certification, MongoDB Associate Developer Exam

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Interview Questions and Answers

Cassandra is ideal for applications that require high availability and fault tolerance, such as time-series data storage, IoT data ingestion, and logging systems.

Indexing in NoSQL is similar to SQL databases, it allows for faster data retrieval by creating data structures that enable quick lookups based on specific fields. The implementation of indexing depends on the type of NoSQL database.

Querying in NoSQL varies depending on the database type. Document databases often use JSON-like query languages. Graph databases use graph query languages like Cypher or Gremlin. Key-value stores typically use simple key lookups.

SQL databases typically have a predefined, rigid schema. NoSQL databases often have a flexible schema, allowing for changes in data structure without requiring schema migrations. Some NoSQL databases are schema-less.

Consider your data model, scalability requirements, consistency needs, query patterns, and development teams familiarity with different NoSQL technologies. Prototype with a few different databases to see which performs best for your specific use case.

MongoDB is well-suited for content management systems, e-commerce platforms, social networking applications, and any application that requires flexible data modeling and high scalability.

Column-family NoSQL databases store data in tables with rows and columns, but the columns are grouped into column families. Column families can have different columns in different rows, offering flexibility in data structure.

Graph NoSQL databases store data as nodes and relationships. Nodes represent entities, and relationships represent the connections between them. They are optimized for querying relationships and traversing graphs.

Popular NoSQL databases include MongoDB, Cassandra, Redis, DynamoDB, Couchbase, Neo4j, HBase, and Elasticsearch.

Transaction handling in NoSQL varies depending on the specific database. Some NoSQL databases offer ACID properties (Atomicity, Consistency, Isolation, Durability) for single-document or single-key operations. Others offer weaker consistency models like eventual consistency.

Document-oriented NoSQL databases store data in JSON-like documents. Each document is a self-contained unit of data, and documents can have varying structures within the same collection.

Key-value NoSQL databases store data as a collection of key-value pairs. The key is used to uniquely identify and retrieve the value, which can be any type of data.

Eventual consistency means that updates to the database will eventually propagate to all replicas, but there might be a delay. During that delay, different clients might see different versions of the data.

The CAP theorem states that a distributed system can only guarantee two out of three properties: Consistency, Availability, and Partition tolerance. NoSQL databases often choose to prioritize Availability and Partition tolerance over strong Consistency.

Horizontal scaling (scaling out) involves adding more machines to your resource pool. NoSQL databases are designed for horizontal scaling, making them suitable for handling increasing data volumes and traffic.

Advantages include scalability, flexibility, high availability, handling large datasets, faster development, and suitability for agile methodologies.

Disadvantages can include eventual consistency, lack of ACID properties in some cases, complexity in joining data across different collections or stores, and a steeper learning curve for developers accustomed to SQL.

Use NoSQL when you need high scalability, flexibility, and performance for handling large volumes of unstructured or semi-structured data. Also, consider it if you have rapidly changing data requirements and need to iterate quickly.

NoSQL (Not Only SQL) is a type of database that is non-relational, distributed, often open-source, and horizontally scalable. They are designed to handle large volumes of unstructured, semi-structured, and structured data.

The main types are: Key-Value stores (e.g., Redis, DynamoDB), Document databases (e.g., MongoDB, Couchbase), Column-family stores (e.g., Cassandra, HBase), and Graph databases (e.g., Neo4j, JanusGraph).