DBMS Topics
HubUnit 1 — Introduction to DBMS & Entity-Relationship ModelAdvantages and Disadvantages of DBMSAggregationCharacteristics of DBMSData IndependenceData ModelsDatabase Users and AdministratorsDBMS LanguagesER DiagramsEntity-Relationship ER ModelIntroduction to DBMSKeys in DBMSRelationships and CardinalitySchema and InstanceSpecialization and GeneralizationUnit 1 Summary — Introduction to DBMS & ER ModelThree-Level Architecture ANSI/SPARC ArchitectureWeak Entity SetUnit 2 — Relational Model & SQLConstraints in SQLCursorsDCL and TCL CommandsDDL CommandsDML CommandsDomain Relational Calculus DRCFunctions and Stored ProceduresJoins in SQLNested Queries SubqueriesRelational AlgebraRelational CalculusRelational ModelSQL IntroductionUnit 2 Summary — Relational Model & SQLTriggers in SQLTuple Relational Calculus TRCViews in SQLArmstrong's Axioms & Closure of AttributesBoyce-Codd Normal Form BCNFClosure of AttributesDependency PreservationFifth Normal Form 5NFFirst Normal Form 1NFFourth Normal Form 4NFFunctional DependencyLossless DecompositionMultivalued DependencyNormal Forms — OverviewNormalization — IntroductionSecond Normal Form 2NFUnit 3 Summary — NormalizationThird Normal Form 3NFUnit 4 — Transaction Management & Concurrency ControlACID PropertiesCheckpointsConcurrency Control & SerializabilityDeadlock in DBMSLock-Based Protocols & Two-Phase LockingLog-Based RecoveryRecovery SystemSerializabilityShadow PagingUnit 4 Summary — Transaction Management & Concurrency ControlTimestamp-Based ProtocolTransaction ManagementTransaction StatesTwo-Phase Locking 2PLUnit 5 — Storage & IndexingB+ TreeB-TreeCost EstimationDynamic HashingFile OrganizationHashing TechniquesIndexing — IntroductionMulti-Level IndexingQuery OptimizationQuery ProcessingSingle-Level IndexingStatic HashingUnit 5 Summary — Storage & IndexingUnit 6 — Advanced Topics in DBMSBig Data and DBMSCloud DatabaseData MiningData WarehouseDatabase SecurityDistributed DatabaseDistributed Query ProcessingDistributed DBMS Architecture, Fragmentation & ReplicationMongoDB — IntroductionNoSQL DatabaseReplication in Distributed DatabasesUnit 6 Summary — Advanced Topics in DBMSViva Voce — DBMSImportant Viva Questions — DBMSInterview Questions — DBMSViva Answers — DBMSDBMS Multiple Choice Questions MCQsDBMS Lab Programs
NoSQL Database
Last Updated : 21 May, 2026
NoSQL "Not Only SQL" databases are non-relational database systems designed for large-scale data storage and real-time web applications. They provide flexible schemas, ho
Unit 6127 words10 headingsExamples included
What is NoSQL?
NoSQL ("Not Only SQL") databases are non-relational database systems designed for large-scale data storage and real-time web applications. They provide flexible schemas, horizontal scalability, and high availability — trading some ACID guarantees for performance and scale.
SQL: Structured, rigid schema, ACID, vertical scale
NoSQL: Flexible schema, BASE, horizontal scale, high throughput
Why NoSQL?
| 1. SCALE | Internet-scale apps have billions of records. |
| 2. SCHEMA FLEXIBILITY | Modern apps have rapidly changing data models. |
| Altering a table schema on a billion-row table | hours of downtime. |
| 3. DATA VARIETY | Not all data is tabular. |
| 4. SPEED | Some use cases need microsecond response times. |
| Redis (in-memory key-value) | sub-millisecond reads. |
Types of NoSQL Databases
1. Key-Value Stores
Simplest model: key → value (like a hash map).
| "user:1001" | { name:"Alice", age:25, city:"Delhi" } |
| "session:abc123" | { expires:1700000000, user_id:1001 } |
| Use cases | Caching, session management, user profiles |
| Examples | Redis, Amazon DynamoDB, Memcached |
| Operations | GET(key), SET(key, value), DELETE(key) |
| Strength | O(1) lookup speed |
| Weakness | Cannot query by value; no relationships |
2. Document Stores
Stores data as self-describing documents (JSON, BSON, XML).
| "_id" | "E001", |
| "name" | "Alice", |
| "dept" | "Computer Science", |
| "skills" | ["Java", "Python", "SQL"], |
| "address" | { |
| "street" | "12 MG Road", |
| "city" | "Delhi" |
| Use cases | Content management, catalogs, user data |
| Examples | MongoDB, CouchDB, Firestore |
| Strength | Flexible schema; nested data; rich queries |
| Weakness | No cross-document joins (aggregations are complex) |
3. Column-Family (Wide Column) Stores
Stores data in rows but groups columns into column families. Optimized for querying large amounts of data on few columns.
| Row Key | "E001" |
| Column Family: PersonalInfo | { Name:"Alice", DOB:"1990-01-15" } |
| Column Family: JobInfo | { Dept:"CS", Salary:75000, Join:"2020" } |
| Use cases | Time-series, IoT, analytics, recommendation engines |
| Examples | Apache Cassandra, HBase, Google Bigtable |
| Strength | Excellent write throughput; flexible columns per row |
| Weakness | Complex query model; no ad-hoc queries easily |
4. Graph Databases
Stores data as nodes (entities) and edges (relationships).
| Nodes | Alice, Bob, CS_Dept, Project_X |
| Edges: Alice | works_in → CS_Dept |
| Alice | collaborates_with → Bob |
| Bob | assigned_to → Project_X |
| Use cases | Social networks, fraud detection, knowledge graphs |
| Examples | Neo4j, Amazon Neptune, JanusGraph |
| Strength | Traversing relationships is O(1) per hop (vs joins) |
| Weakness | Not suitable for tabular/aggregate queries |
SQL vs NoSQL Summary
| Feature | SQL (RDBMS) | NoSQL |
|---|---|---|
| Schema | Fixed, rigid | Flexible, dynamic |
| Data model | Tables/rows | JSON/graph/KV/column |
| ACID | Full | Eventual consistency |
| Scaling | Vertical | Horizontal |
| Query language | SQL (standard) | API / custom query |
| Joins | Excellent | Limited/none |
| Transactions | Full ACID | Limited (improving) |
| Maturity | 50+ years | 10–15 years |
| Best for | Structured, | Large-scale, |
| relational data | flexible data |
BASE Properties (NoSQL Consistency Model)
BASE = Basically Available, Soft state, Eventually consistent
Basically Available:
System guarantees availability — responses always come, but
might not be the most recent data.
Soft State:
System state may change over time even without input
(due to eventual consistency propagation).
Eventually Consistent:
Given no new updates, all replicas will eventually converge
to the same value — but not instantaneously.
Trade-off: BASE accepts temporary inconsistency in exchange
for higher availability and partition tolerance.
When to Choose SQL vs NoSQL
Use RDBMS (SQL) when
✓ Data has clear relational structure
✓ Complex queries, joins, aggregations
✓ Strong ACID compliance required (banking, finance)
✓ Data schema is well-defined and stable
✓ Team is familiar with SQL
Use NoSQL when
✓ Massive scale (millions of writes/second)
✓ Schema evolves frequently
✓ Data is hierarchical/graph/document-oriented
✓ Geographical distribution and high availability required
✓ Real-time applications (gaming, social media, IoT)
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for NoSQL Database.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this DBMS topic.
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