MTech Projects for Big Data — 2026 Guide
Big Data continues to be one of the highest-demand specialisations for MTech and final-year engineering students in India and abroad. Modern projects move beyond classic MapReduce to real-time streaming, data lakehouses, ML at scale, graph analytics and privacy-preserving pipelines on cloud platforms.
Why choose Big Data projects at ProjectsatBangalore?
- IEEE / top-conference 2026 base papers
- Complete PySpark / Scala / Java code
- Architecture + data-flow diagrams
- Cloud (AWS / Azure / GCP) ready setups
- VTU / Anna / JNTU format reports
- 20–25 slide PPT + 50+ viva Q&A
- Dataset links or sample data included
- Mentoring via WhatsApp / Zoom
Spark & Streaming
PySpark, Structured Streaming, Kafka, Flink end-to-end pipelines
Cloud Native
AWS EMR, Azure Databricks, GCP BigQuery & Dataflow projects
ML at Scale
Spark MLlib, distributed TensorFlow, large-scale recommendation systems
Privacy First
Differential privacy, federated analytics and secure multi-party computation
Tools & Platforms Used
Industry-standard open-source and cloud tools for every MTech Big Data project.
Hadoop / HDFS
Apache Spark
Apache Kafka
Apache Flink
AWS EMR / Glue
Azure Databricks
GCP BigQuery
PySpark / Python
Spark MLlib
MongoDB
Neo4j
Delta Lake
65+ Latest MTech Big Data Project Topics (2026)
Carefully curated, research-oriented and industry-relevant topics with recommended tools. All projects come with IEEE / conference base paper, source code, report, PPT and viva support.
| # | Project Title | Domain | Tools / Stack |
|---|---|---|---|
| 🐘 Hadoop, HDFS & Batch Analytics | |||
| 1 | Large-Scale Log Analysis and Anomaly Detection using Hadoop MapReduce | Hadoop | Hadoop · MapReduce · Hive · Pig |
| 2 | Distributed Text Mining and Topic Modelling on Wikipedia Dump with Hadoop | Hadoop | Hadoop · Mahout · NLTK · HDFS |
| 3 | Weather Data Analytics Pipeline using Hadoop and Hive for Climate Trends | Hadoop | Hadoop · Hive · Sqoop · Tableau |
| 4 | Clickstream Analysis and Sessionisation on E-commerce Logs with MapReduce | Hadoop | Hadoop · Pig · HBase · Hive |
| 5 | Genomic Sequence Alignment and Variant Calling at Scale using Hadoop | Hadoop | Hadoop · ADAM · Spark · HDFS |
| ⚡ Apache Spark — Analytics, SQL & MLlib | |||
| 6 | Real-time and Batch Unified Analytics Platform using Spark Structured Streaming | Spark | PySpark · Spark SQL · Delta Lake |
| 7 | Scalable Recommendation Engine with Collaborative Filtering on Spark MLlib | ML/Spark | Spark MLlib · ALS · PySpark |
| 8 | Customer Churn Prediction on Telecom Big Data using Spark ML Pipelines | ML/Spark | PySpark · MLlib · XGBoost |
| 9 | Large-scale Sentiment Analysis of Social Media Streams with Spark NLP | NLP | Spark NLP · PySpark · BERT |
| 10 | Optimised ETL Pipeline with Spark SQL and Adaptive Query Execution | Spark | Spark SQL · AQE · Parquet |
| 11 | Fraud Detection in Credit Card Transactions using Spark Streaming + MLlib | ML/Spark | Spark Streaming · MLlib · Kafka |
| 12 | Distributed Graph Processing for Social Network Influence Ranking with GraphX | Graph | Spark GraphX · PageRank · Scala |
| 13 | Time-Series Forecasting of Energy Consumption using Spark + Prophet / LSTM | ML/Spark | PySpark · Prophet · TensorFlow |
| 14 | Image Classification at Scale with Spark + Deep Learning (TensorFlowOnSpark) | DL/Spark | TensorFlowOnSpark · PySpark |
| 15 | Spark-based Data Quality Framework with Great Expectations Integration | Spark | PySpark · Great Expectations |
| 🌊 Real-time Streaming — Kafka, Flink & Spark Streaming | |||
| 16 | Real-time Fraud Detection Pipeline with Kafka + Flink CEP | Streaming | Kafka · Flink CEP · Redis |
| 17 | Clickstream Analytics and Personalisation Engine using Kafka Streams | Streaming | Kafka Streams · ksqlDB · Avro |
| 18 | IoT Sensor Data Ingestion and Anomaly Detection with Kafka + Spark Streaming | IoT | Kafka · Spark Streaming · MQTT |
| 19 | Exactly-Once End-to-End Streaming Pipeline with Kafka Transactions + Flink | Streaming | Kafka · Flink · Checkpointing |
| 20 | Real-time Dashboard for Stock Market Tick Data using Kafka + Flink + Grafana | Streaming | Kafka · Flink · InfluxDB · Grafana |
| 21 | Change Data Capture (CDC) Pipeline from MySQL to Kafka to Data Lake | Streaming | Debezium · Kafka · Spark · S3 |
| 22 | Multi-tenant Real-time Analytics Platform with Kafka and Flink SQL | Streaming | Kafka · Flink SQL · Hive Catalog |
| 🏞️ Data Lakes, Lakehouse & Modern Data Platforms | |||
| 23 | Building a Medallion Architecture Data Lakehouse with Delta Lake | Lakehouse | Delta Lake · Spark · Unity Catalog |
| 24 | Open Table Formats Comparison: Delta Lake vs Iceberg vs Hudi Performance Study | Lakehouse | Delta · Iceberg · Hudi · Spark |
| 25 | Serverless Data Lake on AWS S3 + Glue + Athena with Lake Formation Governance | Cloud | AWS S3 · Glue · Athena · Lake Formation |
| 26 | ACID Transactions and Time Travel Analytics on a Spark Lakehouse | Lakehouse | Delta Lake · Spark SQL · Time Travel |
| 27 | Data Mesh Implementation with Domain-Oriented Data Products on Kafka + Spark | Data Mesh | Kafka · Spark · DataHub / Amundsen |
| ☁️ Cloud Big Data — AWS, Azure, GCP | |||
| 28 | End-to-End ETL and ML Pipeline on AWS EMR + SageMaker | AWS | EMR · S3 · Glue · SageMaker |
| 29 | Real-time Analytics on Azure Databricks with Delta Live Tables | Azure | Databricks · Delta Live Tables · Azure Event Hubs |
| 30 | Serverless Big Data Warehouse Analytics with Google BigQuery + Dataflow | GCP | BigQuery · Dataflow · Pub/Sub · Looker |
| 31 | Cost-Optimised Spark Workloads on AWS EMR Serverless vs EC2 Comparison | AWS | EMR Serverless · Spot · Cost Explorer |
| 32 | Multi-Cloud Data Pipeline Orchestration with Airflow and Kubernetes | Multi-Cloud | Airflow · Kubernetes · Helm · Terraform |
| 33 | Secure Data Sharing Across Organisations using AWS Clean Rooms / Snowflake | Privacy | AWS Clean Rooms · Snowflake · Differential Privacy |
| 🤖 Machine Learning & AI on Big Data | |||
| 34 | Distributed Hyperparameter Tuning and Model Training with Spark + Optuna | ML | PySpark · Optuna · MLflow |
| 35 | Large-Scale Anomaly Detection using Isolation Forest and Autoencoders on Spark | ML | Spark MLlib · PyTorch · Autoencoder |
| 36 | Feature Store Design and Implementation for Real-time ML Serving | ML Ops | Feast · Redis · Spark · Kafka |
| 37 | Scalable NLP Pipeline for Document Classification with Spark NLP + BERT | NLP | Spark NLP · Hugging Face · BERT |
| 38 | Graph Neural Networks for Fraud Detection on Transaction Networks | GNN | PyTorch Geometric · Neo4j · Spark |
| 39 | Online Learning and Model Drift Detection for Streaming ML Pipelines | ML Ops | River · Kafka · MLflow · Prometheus |
| 40 | Federated Learning Simulation for Privacy-Preserving Healthcare Analytics | Privacy | Flower · PyTorch · Differential Privacy |
| 🕸️ Graph Analytics & NoSQL Stores | |||
| 41 | Social Network Community Detection and Influence Maximisation with Neo4j | Graph | Neo4j · Cypher · Graph Data Science |
| 42 | Knowledge Graph Construction from Unstructured Text for Enterprise Search | Graph | Neo4j · spaCy · Spark NLP |
| 43 | High-Throughput Time-Series Storage and Query with Cassandra + Spark | NoSQL | Cassandra · Spark Connector · Grafana |
| 44 | Document-Oriented Analytics Pipeline with MongoDB Aggregation + Spark | NoSQL | MongoDB · Spark · Aggregation Framework |
| 45 | Redis Streams and RedisAI for Low-Latency Feature Serving | NoSQL | Redis · RedisAI · Kafka |
| 46 | Elasticsearch-based Log Analytics and Full-Text Search at Scale | Search | Elasticsearch · Logstash · Kibana · Beats |
| 🔒 Privacy, Security & Governance | |||
| 47 | Differential Privacy Implementation for Big Data Release and Analytics | Privacy | PyDP · SmartNoise · Spark |
| 48 | Secure Multi-Party Computation for Collaborative Analytics without Data Sharing | Privacy | MP-SPDZ · PySyft · Secret Sharing |
| 49 | Data Lineage and Governance Platform with OpenLineage + DataHub | Governance | OpenLineage · DataHub · Airflow |
| 50 | Anonymisation and Synthetic Data Generation for Privacy-Compliant ML | Privacy | SDV · Faker · Differential Privacy |
| 📡 IoT, Smart City, Healthcare & Domain Applications | |||
| 51 | Smart City Traffic Analytics using IoT Sensors + Kafka + Spark Streaming | IoT | Kafka · Spark · MQTT · InfluxDB |
| 52 | Healthcare Claims Fraud Detection on Large-Scale Insurance Data with Spark | Healthcare | PySpark · MLlib · GraphX |
| 53 | Predictive Maintenance of Industrial Equipment using IoT Big Data Pipeline | IoT | Kafka · Flink · LSTM · Grafana |
| 54 | Retail Demand Forecasting and Inventory Optimisation on Big Data Platform | Retail | Spark · Prophet · Airflow · Tableau |
| 55 | Energy Grid Load Forecasting and Anomaly Detection with Streaming Analytics | Energy | Kafka · Spark · Prophet · Grafana |
| 56 | Genomic Big Data Pipeline for Variant Calling and Population Analytics | Bioinformatics | Spark · ADAM · Hail · HDFS |
| 🚀 Advanced & Research-Oriented Topics | |||
| 57 | Query Optimisation and Cost-Based Optimiser Internals Study on Spark SQL | Research | Spark SQL · Catalyst · Tungsten |
| 58 | Adaptive Stream Processing under Concept Drift with Flink State Management | Research | Flink · State Backend · Concept Drift |
| 59 | Benchmarking Open Table Formats for Upsert-Heavy Workloads (Hudi vs Delta) | Research | Hudi · Delta · Spark · TPC-DS |
| 60 | Serverless Stream Processing Comparison: AWS Lambda vs Flink vs Spark | Research | Lambda · Flink · Spark · Kinesis |
| 61 | Vector Search and RAG Pipeline on Large Document Corpora with Spark + FAISS | RAG / LLM | Spark · FAISS · LangChain · Embeddings |
| 62 | Observability and Performance Tuning of Large Spark Clusters with Prometheus | Ops | Spark · Prometheus · Grafana · Ganglia |
| 63 | Multi-Modal Big Data Analytics: Text + Image + Sensor Fusion Pipeline | Multi-Modal | Spark · TensorFlow · Kafka · CLIP |
| 64 | Carbon-Aware Scheduling of Big Data Workloads on Cloud Spot Instances | Green IT | Kubernetes · Spot · Carbon APIs |
| 65 | End-to-End MLOps Platform for Big Data: Feature Store → Training → Serving | MLOps | Feast · MLflow · Kubeflow · Seldon |
★ All 65 MTech Big Data project topics are sourced from IEEE Xplore, ACM, VLDB, SIGMOD, ICDE, KDD and leading open-source project roadmaps (2022–2026). Each project includes the base paper / technical report, complete source code (PySpark / Scala / Java), architecture diagrams, sample datasets or generation scripts, university-format report for VTU / Anna University / JNTU, PPT (20–25 slides) and 50+ viva Q&A specific to the topic.
FAQ — MTech Big Data Projects
Top MTech Big Data project topics for 2026 include: Real-time fraud detection with Kafka + Flink / Spark Streaming, Lakehouse architectures with Delta Lake or Iceberg, Large-scale recommendation systems on Spark MLlib, Graph analytics with Neo4j / GraphX, Privacy-preserving analytics using differential privacy, IoT sensor analytics pipelines, Cloud-native ETL on AWS EMR / Azure Databricks / GCP BigQuery, and RAG / vector-search pipelines on big document corpora. All projects include IEEE or conference base paper, complete code, report, PPT and viva support.
Core stack: Apache Hadoop (HDFS, YARN, MapReduce), Apache Spark (PySpark, Spark SQL, MLlib, Structured Streaming), Apache Kafka and Apache Flink for streaming, Delta Lake / Apache Iceberg / Hudi for lakehouse, Hive / Presto / Trino for SQL, MongoDB, Cassandra, Neo4j, Redis, Elasticsearch for NoSQL / search. Cloud: AWS EMR, Glue, Kinesis, S3; Azure Databricks, Synapse, Event Hubs; GCP BigQuery, Dataflow, Pub/Sub. Orchestration: Airflow, Kubernetes. ML: Spark MLlib, TensorFlow, PyTorch, MLflow, Feast.
Yes. Every MTech Big Data project includes: (1) IEEE / ACM / VLDB / KDD style base paper or technical report; (2) complete, well-commented source code (PySpark / Scala / Java); (3) architecture and data-flow diagrams; (4) sample datasets or data-generation scripts; (5) university-format project report for VTU, Anna University, JNTU, RGPV; (6) PPT (20–25 slides); and (7) 50+ viva Q&A covering theory, architecture, trade-offs, performance and result interpretation.
Yes. All project reports, synopses and presentations are customised to match your university’s MTech / MCA / MSc format — VTU, Anna University, JNTU, RGPV, PES, RV, Manipal, BITS, NIT and autonomous colleges. Chapter structure, abstract style, citation format and evaluation checklist are tailored on request. MTech project approval synopses are also prepared.
Typical completion is 7–21 working days depending on complexity. Batch Spark SQL / Hive analytics projects are ready in 5–10 days. Full streaming + ML pipelines or multi-cloud end-to-end systems take 12–21 days. Research-oriented topics (query optimisers, table formats, federated learning) may take 15–21 days. For urgent deadlines, express delivery is available. Contact us on WhatsApp at +91 95919 12372 with your submission date.
Big Data Project Lab — Bangalore
Inside our Big Data & Analytics lab — multi-node Hadoop/Spark clusters, Kafka streaming setups, cloud sandbox accounts (AWS/Azure/GCP), GPU workstations for distributed deep learning, and dedicated mentoring rooms for MTech and PhD scholars.
Spark
Cluster
PySpark · MLlib · SQLCluster
Kafka
Streaming
Real-time PipelinesStreaming
Flink
CEP
Complex Event ProcessingCEP
AWS EMR
& Glue
Cloud Big Data& Glue
Azure
Databricks
Lakehouse & MLDatabricks
GCP
BigQuery
Serverless AnalyticsBigQuery
Neo4j
Graph
Graph Data ScienceGraph
Delta Lake
/ Iceberg
Modern Table Formats/ Iceberg
Report & PPT
Preparation
VTU / Anna / JNTUPreparation
Viva & Review
Sessions
MTech / PhD Big DataSessions