About This Course
The explosion of big data, real-time applications, and cloud-native architectures has fundamentally changed how organisations store, retrieve, and process information. Relational databases, while still essential, are increasingly complemented — or replaced — by NoSQL systems designed to handle massive volumes of unstructured, semi-structured, and rapidly changing data at scale. NoSQL databases power some of the world's most demanding applications, from social media platforms and e-commerce engines to IoT data streams and financial transaction systems.
TrainingComplex's NoSQL Databases Training is a comprehensive, hands-on programme designed for IT professionals, data engineers, software developers, and database administrators who need to work confidently with modern non-relational database technologies. Over ten days, participants gain deep practical experience with the four primary NoSQL data models and the leading platforms that implement them, including MongoDB for document storage, Apache Cassandra for wide-column data, Redis for in-memory key-value operations, and Neo4j for graph-based data relationships.
Beyond platform-specific skills, this course develops participants' ability to make informed architectural decisions — understanding when NoSQL is the right choice, how to model data effectively without the constraints of fixed schemas, and how to ensure availability, consistency, and partition tolerance in distributed database environments. Participants also explore security hardening, backup strategies, cloud deployment options, and integration patterns with existing enterprise systems.
Whether your organisation is migrating legacy workloads to NoSQL, building new data-intensive applications, or scaling out an existing NoSQL infrastructure, this programme provides the technical depth and strategic perspective needed to succeed. Delegates leave with practical skills they can apply immediately, supported by hands-on labs, architecture templates, and real-world implementation guides.
Who Should Attend
- Database Administrators transitioning from relational to NoSQL environments
- Software Developers and Backend Engineers building data-driven applications
- Data Engineers designing pipelines and storage layers for big data platforms
- Cloud Architects and Solutions Architects evaluating NoSQL for enterprise workloads
- System Analysts and IT Architects involved in database selection and design
- DevOps Engineers managing database deployment and operations in cloud environments
- Technical Team Leads overseeing data infrastructure projects
- IT Managers seeking a deep technical understanding of NoSQL technologies
Learning Objectives
- Explain the fundamental principles of NoSQL databases and how they differ from relational systems
- Identify and compare the four core NoSQL data models: document, key-value, column-family, and graph
- Design effective data models for NoSQL systems, applying schema-less design patterns appropriate to each model type
- Install, configure, and administer MongoDB, Apache Cassandra, Redis, and Neo4j in development and production environments
- Write efficient queries, aggregations, and data manipulation commands across multiple NoSQL platforms
- Apply the CAP theorem and BASE consistency principles to make informed architectural trade-offs in distributed database design
- Implement indexing, replication, sharding, and partitioning strategies to optimise performance and scalability
- Secure NoSQL databases through authentication, authorisation, encryption, and audit controls
- Integrate NoSQL databases with enterprise applications, REST APIs, and big data processing frameworks
- Design and execute backup, recovery, and disaster recovery procedures for NoSQL environments
- Deploy and manage NoSQL databases on major cloud platforms including AWS, Google Cloud, and Microsoft Azure
- Evaluate real-world use cases and select the most appropriate NoSQL technology for a given business requirement
Course Modules
Module 1: Introduction to NoSQL: Concepts, History, and the Case for Non-Relational Databases
- Evolution from relational to NoSQL: drivers, limitations, and inflection points
- Core characteristics of NoSQL: schema flexibility, horizontal scalability, and high availability
- The CAP theorem and BASE vs. ACID consistency models explained
- NoSQL adoption patterns across industries: e-commerce, social media, IoT, and finance
- Taxonomy of NoSQL database types: document, key-value, column-family, and graph — when each category fits
- Total cost of ownership and operational trade-offs: NoSQL vs. RDBMS for greenfield and migration projects
Module 2: NoSQL Data Models: Document, Key-Value, Column-Family, and Graph
- Deep dive into the document data model: structure, nesting, and embedded vs. referenced relationships
- Key-value stores: simplicity, speed, and ideal use cases for session management and caching
- Column-family (wide-column) databases: row keys, column families, and time-series data patterns
- Graph databases: nodes, edges, properties, and relationship-centric data representation
- Multi-model databases: platforms that blend two or more data models and the scenarios that justify them
- Choosing the right data model: a decision framework mapping access patterns, relationship complexity, and scale requirements
Module 3: MongoDB: Document Database Fundamentals
- Installing and configuring MongoDB: standalone, replica set, and sharded cluster architectures
- CRUD operations, the MongoDB Query Language (MQL), and projection operators
- Embedded vs. referenced document design: trade-offs and practical guidelines
- Indexing strategies in MongoDB: single-field, compound, text, and geospatial indexes
- Schema validation with JSON Schema in MongoDB: enforcing data integrity without a rigid schema
- Transactions in MongoDB: multi-document ACID transactions, session handling, and appropriate use cases
Module 4: MongoDB: Advanced Operations, Aggregation, and Administration
- The aggregation pipeline: stages, operators, and real-world analytical query patterns
- Replication in MongoDB: elections, oplog mechanics, and read/write concern configuration
- Sharding architecture: shard keys, chunks, balancers, and routing through mongos
- MongoDB Atlas: cloud deployment, monitoring dashboards, and automated backups
- Change streams: capturing real-time data change events for event-driven and CDC integration patterns
- Performance diagnostics: using explain() plans, the Database Profiler, and slow query log analysis to resolve bottlenecks
Module 5: Apache Cassandra: Wide-Column Database Design and Operations
- Cassandra architecture: the ring topology, virtual nodes (vnodes), and the gossip protocol
- Cassandra Query Language (CQL): creating keyspaces, tables, and executing DML statements
- Data modelling in Cassandra: query-first design, partition keys, clustering columns, and denormalisation
- Consistency levels, replication factors, and tuning for availability vs. consistency trade-offs
- Lightweight transactions (LWT) in Cassandra: compare-and-set semantics, Paxos internals, and appropriate use cases
- Capacity planning for Cassandra: estimating partition sizes, token distribution, and node count for target throughput
Module 6: Apache Cassandra: Performance Tuning, Compaction, and Production Management
- Compaction strategies: SizeTiered, LeveledCompaction, and TimeWindowCompactionStrategy explained
- Read and write path internals: memtables, SSTables, bloom filters, and the commit log
- Monitoring Cassandra with nodetool, JMX metrics, and integration with Prometheus and Grafana
- Repair operations, anti-entropy, and handling node failures and replacements in production
- Garbage collection tuning for Cassandra JVM: G1GC settings, GC pause impact on latency, and heap sizing guidelines
- Upgrade and rolling restart procedures: maintaining cluster availability during version migrations and configuration changes
Module 7: Redis: In-Memory Key-Value Store and Caching Engine
- Redis data structures: strings, hashes, lists, sets, sorted sets, and HyperLogLogs
- Persistence modes in Redis: RDB snapshots, AOF logging, and hybrid persistence configuration
- Redis Pub/Sub, streams, and use cases for real-time messaging and event-driven architectures
- Redis Cluster: data sharding, slot allocation, failover, and high-availability configuration
- Redis Sentinel: architecture, quorum-based leader election, and automatic failover for non-clustered deployments
- Memory optimisation in Redis: eviction policies, object encoding selection, and using OBJECT ENCODING to audit memory efficiency
Module 8: Neo4j: Graph Database Design and Cypher Query Language
- Graph database fundamentals: property graphs, nodes, relationships, labels, and properties
- Cypher query language: MATCH, CREATE, MERGE, WHERE, and pattern-based traversal syntax
- Graph data modelling for real-world use cases: fraud detection, recommendation engines, and network analysis
- Neo4j performance optimisation: indexes, constraints, query planning, and the EXPLAIN/PROFILE tools
- Neo4j causal clustering: leader-follower replication, routing policies, and achieving read scalability in production
- Graph algorithms in Neo4j: PageRank, community detection, shortest path, and leveraging the Graph Data Science library
Module 9: NoSQL Security: Hardening, Access Control, and Compliance
- Authentication and authorisation in MongoDB, Cassandra, Redis, and Neo4j: user roles and privilege models
- Encryption at rest and in transit: TLS/SSL configuration across NoSQL platforms
- Network security: firewall rules, VPC isolation, IP whitelisting, and binding configurations
- Audit logging, compliance considerations for GDPR and data sovereignty, and security monitoring
- Secrets management and credential rotation: integrating NoSQL platforms with HashiCorp Vault and cloud-native secrets managers
- Vulnerability hardening checklists: disabling default ports, removing anonymous access, and applying CIS benchmark controls for NoSQL systems
Module 10: NoSQL Data Modelling Patterns and Anti-Patterns
- Schema design principles: denormalisation, pre-aggregation, and designing for read patterns
- Common anti-patterns: over-embedding, unbounded arrays, and inappropriate use of secondary indexes
- Polymorphic schemas and versioning strategies for evolving NoSQL data models
- Comparing data modelling approaches across the four NoSQL types using a unified business scenario
- Bucket pattern, outlier pattern, and computed pattern: advanced modelling techniques for high-volume and variable-density data
- Migration paths for schema evolution in production: zero-downtime strategies, dual-write approaches, and backfill scripting
Module 11: Backup, Recovery, and Disaster Recovery for NoSQL Systems
- Backup strategies for MongoDB (mongodump, Atlas snapshots), Cassandra (nodetool snapshot), and Redis (RDB/AOF)
- Point-in-time recovery concepts and their applicability across different NoSQL platforms
- Disaster recovery planning: RTO/RPO objectives, cross-region replication, and failover procedures
- Testing backup and recovery procedures: drills, validation, and documentation best practices
- Incremental and continuous backup approaches: leveraging oplogs, CDC streams, and platform-native tooling to minimise data loss windows
- Backup security and integrity assurance: encrypting backup artefacts, storing checksums, and managing access controls on backup repositories
Module 12: Cloud Deployment and Managed NoSQL Services
- AWS managed NoSQL services: DynamoDB, DocumentDB, ElastiCache, and Amazon Keyspaces
- Google Cloud NoSQL offerings: Firestore, Bigtable, and Memorystore — architecture and pricing considerations
- Azure Cosmos DB: multi-model support, global distribution, consistency levels, and cost management
- Migration strategies: lifting and shifting from on-premises NoSQL to cloud-managed services
- Infrastructure-as-code for managed NoSQL: provisioning and configuring DynamoDB, Cosmos DB, and Bigtable using Terraform and cloud-native templates
- Observability in managed NoSQL environments: leveraging CloudWatch, Cloud Monitoring, and Azure Monitor for alerting, dashboards, and cost attribution
Module 13: Integration, Use Case Selection, and Capstone Project
- Integrating NoSQL databases with REST APIs, microservices, and Apache Kafka data pipelines
- Decision framework: selecting the right NoSQL platform based on data model, scale, consistency, and team capability
- Hybrid architectures: combining relational and NoSQL systems in a single enterprise data landscape
- Capstone project presentations: participants present their NoSQL solution designs for peer and instructor review
- Observability and operational runbook design: defining SLOs, alerting thresholds, and incident response playbooks for NoSQL-backed services
- Total solution cost modelling: estimating infrastructure, licensing, operational overhead, and TCO for a proposed NoSQL architecture
Requirements & Prerequisites
Participants should have a working knowledge of relational database concepts, including tables, queries, and basic SQL. Familiarity with at least one programming or scripting language such as Python, JavaScript, or Java is recommended, as the labs involve writing queries and interacting with database drivers. Prior exposure to Linux command-line operations is helpful, as most hands-on exercises are conducted in a Linux-based environment.
Training Methodology
- Guided instructor demonstrations on live NoSQL database instances, walking through installation, configuration, and core operations on MongoDB, Cassandra, Redis, and Neo4j
- Hands-on lab sessions using pre-configured virtual environments where participants execute queries, design schemas, and tune performance in real time
- Architecture case studies drawn from e-commerce, telecommunications, healthcare, and financial services sectors, illustrating real NoSQL deployment decisions and their outcomes
- Comparative platform workshops in which participants apply the same data modelling challenge across two or more NoSQL platforms and evaluate trade-offs
- Small-group problem-solving exercises centred on selecting and justifying the appropriate NoSQL technology for a given business scenario
- Performance benchmarking labs where participants test and compare indexing strategies, replication configurations, and query optimisation techniques
- Security hardening walkthroughs covering authentication setup, role-based access control, and encryption configuration on live database instances
- Cloud deployment practicals using managed NoSQL services on AWS (DynamoDB, DocumentDB), Google Cloud (Firestore, Bigtable), and Azure (Cosmos DB)
- Capstone project in which each participant designs a complete NoSQL solution — including data model, platform selection, security plan, and deployment architecture — for a realistic organisational scenario
Organisational Benefits
- Accelerated adoption of NoSQL technologies that underpin modern, scalable application architectures
- Reduced risk of costly database design mistakes through sound NoSQL data modelling principles
- Improved application performance and availability through optimised indexing, replication, and sharding configurations
- Stronger in-house capability to evaluate, select, and implement the right NoSQL platform for each use case
- Enhanced data security posture across NoSQL deployments through consistent application of hardening and access control practices
- Reduced vendor dependency by developing internal expertise across multiple NoSQL platforms
- Faster time-to-market for data-intensive applications built on NoSQL foundations
- Better alignment between database architecture decisions and overarching cloud and digital transformation strategies
- Improved disaster recovery readiness through structured backup and recovery planning for NoSQL systems
Where Is NoSQL Databases Training Offered?
This course runs in-person across: Johannesburg, Nairobi, Kigali, Addis Ababa, Abuja, Kisumu, Arusha, Nakuru, Dubai, Pretoria, Naivasha, Zanzibar, Dar es Salaam, Mombasa, Riyadh, Kampala, Accra, Lagos, Cape Town, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,000 |
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Kigali, Rwanda |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 3,800 |
| Addis Ababa, Ethiopia |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 4,800 |
| Live Virtual |
Online |
17 Oct 2026 – 6 Dec 2026 |
KES 160,000 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 5,600 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Arusha, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,200 |
| Nakuru, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Dubai, United Arab Emirates |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 8,200 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 6,600 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
KES 230,000 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 6 Nov 2026 |
USD 5,000 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,600 |
+237 more dates — view live schedule →
Frequently Asked Questions
Do I need prior NoSQL experience to attend this course?
No prior NoSQL experience is required, but participants should have a solid understanding of relational database concepts and basic SQL. Familiarity with a scripting or programming language and Linux command-line usage will help you get the most out of the hands-on labs.
Which NoSQL platforms does this course cover?
The course covers four major NoSQL platforms in depth: MongoDB (document), Apache Cassandra (wide-column), Redis (key-value), and Neo4j (graph). It also introduces managed cloud NoSQL services on AWS, Google Cloud, and Microsoft Azure.
Will I receive a certificate upon completion?
Yes. Participants who attend and complete the full ten-day programme will receive a TrainingComplex Certificate of Completion in NoSQL Databases Training, which can be used for professional development records and CPD portfolios.
Are the lab exercises conducted on my own laptop or provided infrastructure?
TrainingComplex provides access to pre-configured virtual lab environments for all hands-on exercises, so participants do not need to install any database software in advance. You will need a laptop with a stable internet connection and a modern browser to access the lab portal.
Is this course available online for remote participants?
Yes. The course is available as a Live Virtual delivery for participants who cannot attend in person. The virtual format mirrors the in-person programme, including live instructor sessions, interactive labs, and group exercises, all conducted through a real-time online platform.
