Architect Trusted Data Pipelines. The Best Python Data Engineering Course in Ahmedabad.

Analytics and AI teams fail without reliable pipelines — they need engineers who ship PySpark jobs, orchestrate Airflow DAGs, and model warehouses with tests. We train you on Python, SQL, Spark, Kafka, and cloud lake patterns used by banks, e-commerce, and GCC data platforms across India to move data from raw events to decision-ready marts.

Enterprise Enrollment Architecture
Duration: 60 Hours
₹38,000₹26,500
Note: Any type of tax would be extra. Global certification cost is excluded.

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Exclusive Program Benefits

  • After the course, Bascom Bridge will share 10–12 sample CVs to help build your resume.
  • Students receive a license for Bascom Bridge’s placement mobile app*.
  • Lifetime access* to the enrolled course for students.
  • If a student does not clear interviews, Bascom Bridge will provide retraining* until employment is secured.
  • Global certification training is included at no extra cost.
  • No-cost EMI* available for 6 months.
  • Discount on global certification fees* available.
  • Hands-on ETL labs: PySpark, Airflow DAGs, Kafka streams, and warehouse modeling with audited pipelines.
  • Capstone medallion architecture with data quality gates — interview-ready for data engineer roles*.
*T&C apply. Valid per strict institutional guidelines upon active enrollment.
Applicable taxes will be added to each instalment.

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About Python Data Engineering

Enterprise Tools Included

  • /Python, pandas & SQL — ETL scripts and warehouse transformations
  • /Apache Spark / PySpark — distributed batch processing at scale
  • /Apache Airflow — orchestration, scheduling, and dependency DAGs
  • /Apache Kafka — event streaming and real-time ingestion patterns
  • /PostgreSQL, dbt-style modeling & AWS S3/Glue data lake basics

Cognitive Prerequisites

  • /Core/Advanced Python and basic SQL (SELECT, JOIN, GROUP BY)
  • /Applied Data Science helpful but not mandatory — ETL focus is pipeline engineering
  • /Comfort with CSV/JSON files, APIs, and logical data modeling
  • /Laptop with 16 GB RAM recommended for local Spark/Airflow labs

Salary Progression (₹)

  • Entry-Level (0-3 yrs)₹6.0L - ₹11.0L
  • Mid-Level (4-7 yrs)₹12.0L - ₹22.0L
  • Senior Level (8-12+ yrs)₹24.0L - ₹40.0L+
*Data benchmarked directly from AmbitionBox / Glassdoor India 2026 enterprise tech verified salaries.

The Enterprise Capstone Architecture

Retail Order Medallion Lakehouse Pipeline. You will ingest bronze-layer JSON from APIs and Kafka, cleanse and conform records in PySpark silver jobs with data-quality checks, publish gold sales and inventory marts in PostgreSQL, orchestrate dependencies in Airflow with SLA alerts, and document lineage and cost metrics — the end-to-end deliverable data engineering teams present before enabling executive dashboards at Indian retail and BFSI enterprises.

Deep-Dive Syllabus Grid

Module 1: Data Engineering Landscape & Pipeline Thinking
Batch versus streaming, ETL versus ELT, data lakes versus warehouses, and the modern medallion pattern. How GCCs and product companies in Ahmedabad staff data platform teams — vocabulary hiring managers expect beyond notebook exports.
Module 2: Relational Modeling & Advanced SQL
Star/snowflake schemas, slowly changing dimensions, window functions, and query optimization. Writing analytics-ready SQL that downstream BI and ML teams trust.
Module 3: Python ETL with pandas & API Ingestion
Extracting from REST APIs, cleaning types, handling nulls, and idempotent loads. Building maintainable Python packages for data movement — not one-off scripts.
Module 4: Apache Spark & PySpark Fundamentals
RDDs versus DataFrames, partitioning, shuffles, and lazy evaluation. Processing large CSV/Parquet datasets on local or cloud Spark clusters — the core skill for scale interviews.
Module 5: Data Quality, Testing & Observability
Great Expectations-style checks, schema contracts, row counts, and anomaly alerts. Failing pipelines loudly instead of silently corrupting dashboards executives use.
Module 6: Workflow Orchestration with Apache Airflow
DAG design, operators, sensors, retries, and backfill strategies. Scheduling nightly warehouse refreshes and dependency chains across finance and operations datasets.
Module 7: Streaming Ingestion with Apache Kafka
Topics, partitions, consumer groups, and at-least-once delivery. Building near-real-time pipelines for clickstream, IoT, and payment events — complementary to batch layers.
Module 8: Cloud Data Lakes & AWS Glue/S3 Patterns
Landing zones, Parquet partitioning, catalog metadata, and cost-aware storage tiers. Deploying lakehouse-style layers without over-engineering for mid-market enterprise budgets.
Module 9: dbt-Style Transformations & Analytics Engineering
Staging, intermediate, and mart models with tests and documentation. Bringing software-engineering discipline to warehouse SQL — the analytics engineer crossover employers value.
Module 10: Medallion Architecture — Bronze, Silver, Gold
Raw ingestion, conformed cleansing, and business-ready marts. Implementing incremental loads and late-arriving facts for retail and BFSI use cases.
Module 11: Security, Governance & Cost Optimization
PII masking, role-based access, lineage awareness, and FinOps for cloud storage/compute. Aligning with DPDP expectations before production data leaves controlled environments.
Module 12: Capstone Review & Data Engineer Interview Drills
System-design scenarios: SLA breaches, backfills, schema evolution, and disaster recovery. Presenting pipeline diagrams, Airflow DAGs, and data-quality scorecards — the 2026 interview bar in India.

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Adani Logo
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Asia MotorWorks Logo
Bharti Airtel Logo
Blue Star Logo
Charotar Institute Of Technology Changa Charusat
Cred Logo
Crest Data System Logo
Dalmia Group
Dhirubhai Ambani Institute Of Information And Comm
Einfochips Logo
Indus University
Jpmorgan Logo
JSW Group Logo
Larsen&Toubro Logo
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