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Diploma in Graph Analytics Pipeline Production

Online postgraduate diploma of 800 hours in production graph analytics pipelines, with 8 modules covering graph platform architecture, ingestion and graph construction, graph analytics algorithms, orchestration and automation, pipeline quality and testing, performance and scalability, and observability and operations, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor

Diploma in Graph Analytics Pipeline Production — studying from India

India's data and analytics capability is among the largest in the world, with major centres in Bengaluru, Hyderabad, Pune, Chennai and Gurugram. Global technology firms and domestic IT companies such as Tata Consultancy Services, Infosys, Wipro and HCLTech run large engineering and analytics operations. Engineering organisations increasingly apply AI, simulation and digital twin techniques to product design and manufacturing. Data protection rules are shaped by the Ministry of Electronics and Information Technology, while the National Association of Software and Service Companies represents the sector. Engineers here build models, analytics pipelines and simulation workflows across industries.

Fees for India: ₹159,000 (Indian Rupee), with instalments and employer sponsorship available.

Relevant bodies in India

  • Ministry of Electronics and Information Technology (MeitY)
  • National Association of Software and Service Companies (NASSCOM)
  • Bureau of Indian Standards (BIS)
  • Indian Statistical Institute

Typical roles

  • Data Scientist
  • Machine Learning Engineer
  • Simulation Engineer
  • Data Analytics Engineer

Questions from India

Are there professional bodies in India that recognise data and AI engineering?

India does not have a statutory licence for data scientists or AI engineers. Relevant professional bodies include the Computer Society of India (CSI), the Institution of Engineers (India) and IEEE India sections. SEIUM's qualifications are private, not state degrees and not accredited by a national body, so their value depends on how employers view them.

Which employers in India hire data and AI professionals?

Demand is strong in IT services and consulting firms, banks and fintech companies, e-commerce and healthcare organisations, and research labs, especially in Bengaluru, Hyderabad, Pune and the NCR. A private online programme can support a career change or upskilling, but employers mainly assess coding, statistics and project portfolios alongside your qualifications.

Can I study this while working full time in India?

Yes. The programme is fully online and taught in English, so you can study from home without a student visa. Schedules are set in IST, so plan around your working hours. Specialist courses are about 300 hours, diplomas 800 and master's about 1,900. Each student has an assigned technical tutor, and fees can be paid in instalments. Ask your employer whether they will sponsor the fees.

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Learning outcomes

What you will be able to do

01

Select the right graph platform for the volume, latency and query type.

02

Design bulk and incremental ingestion processes into graph databases.

03

Apply centrality, community and embedding algorithms in production.

04

Orchestrate graph analytics pipelines with Airflow and Kubernetes.

05

Implement testing and quality controls on graph data.

06

Optimise the performance of queries and algorithms at scale.

07

Operate graph platforms with observability, security and high availability.

Profile

Who it's for

  • Graph data engineers taking pipelines from prototype to production.
  • Analytics platform engineers.
  • Data pipeline developers adding graph workloads to their stack.
  • Data architects designing graph-based platforms.
  • MLOps and DataOps engineers.
  • Graph analysts who want to industrialise their work.
  • Engineering or science graduates aiming to enter banking and fintech.
Standards & tools

What you'll work with

  • ISO/IEC 39075:2024 (GQL graph query language)
  • ISO/IEC 9075-16:2023 (SQL/PGQ, property graph queries)
  • Regulation (EU) 2016/679 – GDPR (General Data Protection Regulation)
  • ISO/IEC 27001:2022 (information security)
  • ISO 8000 (data quality)
  • ISO/IEC 25012 (data quality model)
  • W3C RDF 1.1 and SPARQL 1.1 (semantic web data model and query language)
  • Neo4j Graph Data Science
  • Apache Spark GraphFrames
  • Apache Airflow
  • Great Expectations
  • Grafana
Syllabus

8 modules

  1. 1.1Data models: property graph and RDF
  2. 1.2Native databases versus compute engines
  3. 1.3Neo4j, TigerGraph, Amazon Neptune and Memgraph
  4. 1.4Distributed processing with GraphX and GraphFrames
  5. 1.5Exchange formats: GraphML, Parquet and edge CSV
  6. 1.6Cypher, Gremlin and SPARQL languages
  7. 1.7The GQL standard ISO/IEC 39075
  8. 1.8Selection criteria by volume and latency
  9. 1.9Lambda and kappa architectures for graphs
  10. 1.10Workshop: comparing Cypher and Gremlin queries
  1. 2.1Entity extraction from relational sources
  2. 2.2Entity resolution and deduplication
  3. 2.3Modelling nodes, relationships and properties
  4. 2.4Bulk loading with neo4j-admin and bulk loaders
  5. 2.5Incremental ingestion with CDC and Debezium
  6. 2.6Streaming into the graph with Kafka Connect
  7. 2.7Schema validation and constraints
  8. 2.8Graph schema versioning
  9. 2.9Data lineage with OpenLineage
  10. 2.10Practical: incremental loading pipeline from PostgreSQL
  1. 3.1Centrality: degree, betweenness and PageRank
  2. 3.2Community detection: Louvain and Leiden
  3. 3.3Connected and strongly connected components
  4. 3.4Shortest paths and pathfinding algorithms
  5. 3.5Node similarity and link prediction
  6. 3.6Embeddings: FastRP and node2vec
  7. 3.7In-memory graph projections
  8. 3.8Complexity and computational cost
  9. 3.9Neo4j Graph Data Science in production
  10. 3.10Lab: customer segmentation with Leiden
  1. 4.1DAG design in Apache Airflow
  2. 4.2Dagster and asset-oriented orchestration
  3. 4.3Parameterisation and idempotent re-runs
  4. 4.4Managing dependencies between tasks
  5. 4.5Docker containers for graph workloads
  6. 4.6Deployment on Kubernetes with Helm
  7. 4.7Infrastructure as code with Terraform
  8. 4.8Continuous integration of pipelines
  9. 4.9Secrets and credentials management
  10. 4.10Case study: DAG for daily PageRank recomputation
  1. 5.1Unit testing of transformations
  2. 5.2Integration testing with ephemeral containers
  3. 5.3Data expectations with Great Expectations
  4. 5.4Detecting orphan nodes and invalid edges
  5. 5.5Structural quality metrics of the graph
  6. 5.6Comparing graphs between versions
  7. 5.7Synthetic data for testing
  8. 5.8Data contracts between producers and consumers
  9. 5.9Code review and standards
  10. 5.10Practical: test suite for a graph pipeline
  1. 6.1Indexes and query planning
  2. 6.2Graph partitioning and sharding
  3. 6.3Supernodes and mitigation strategies
  4. 6.4Caching and precomputation of results
  5. 6.5Parallelism in iterative algorithms
  6. 6.6Memory and heap sizing
  7. 6.7Benchmarking with the LDBC Social Network Benchmark
  8. 6.8Cloud cost optimisation
  9. 6.9Profiling slow queries
  10. 6.10Workshop: optimising Cypher queries with PROFILE
  1. 7.1Pipeline metrics with Prometheus
  2. 7.2Operational dashboards in Grafana
  3. 7.3Distributed tracing with OpenTelemetry
  4. 7.4Alerts and incident management
  5. 7.5Graph backup and recovery
  6. 7.6High availability and causal clusters
  7. 7.7Access control and property-level security
  8. 7.8Data protection compliance
  9. 7.9Serving results through a GraphQL API
  10. 7.10Case study: failure recovery runbook
  1. 8.1Definition of the analytical use case
  2. 8.2Graph model design
  3. 8.3Ingestion implementation
  4. 8.4Algorithm orchestration
  5. 8.5Testing and data quality
  6. 8.6Observability and deployment
  7. 8.7Performance and cost analysis
  8. 8.8Technical documentation and runbooks
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Neo4j Graph Data Science, Apache Spark GraphFrames, Apache Airflow, Great Expectations and Grafana, in free, academic or evaluation versions. Real procedures are studied: building an incremental graph pipeline from relational databases, daily orchestration of centrality and community algorithms, and query optimisation and load testing on large graphs. Assessment is continuous by module plus the final integrative project.

Standards covered: ISO/IEC 39075:2024 (GQL), ISO/IEC 9075-16:2023 (SQL/PGQ), Regulation (EU) 2016/679 (GDPR), ISO/IEC 27001:2022, ISO 8000, ISO/IEC 25012, W3C RDF 1.1 and SPARQL 1.1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC 39075 (GQL), ISO/IEC 9075-16 (SQL/PGQ) and the GDPR applied to real cases, not as a theoretical annex.
Industry software:Neo4j Graph Data Science, Apache Spark GraphFrames, Apache Airflow, Great Expectations and Grafana, in free, academic or evaluation versions.
Final project:a documented production graph pipeline for a real analytical use case, from ingestion and orchestration to testing, observability and runbooks, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case and graph model
Define the analytical use case and design the graph model.
Ingestion
Implement bulk and incremental ingestion into the graph.
Algorithm orchestration
Orchestrate the analytics algorithms as an automated pipeline.
Testing and observability
Add testing, data quality checks, observability and deployment.
Final project
Present the performance and cost analysis, runbooks and report for review by your tutor.
Career opportunities

Where this takes you

Graph data engineerAnalytics platform engineerData pipeline developerData architectMLOps and DataOps engineerGraph analyst
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Neo4j Graph Data Science, Apache Spark GraphFrames, Apache Airflow, Great Expectations and Grafana, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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