Data Engineering with AI Training in Vizag
Build reliable data pipelines and prepare trustworthy datasets for analytics and AI applications. This 3-month course at Softenant Technologies in Visakhapatnam connects SQL and Python foundations with ingestion, transformation, orchestration, data quality and carefully validated AI assistance.
What you will learn
- Design repeatable SQL and Python ingestion jobs with clear inputs, outputs and error handling.
- Transform datasets with PySpark and organise lakehouse tables for analysis.
- Validate AI-generated mappings, extraction results and code against known data examples.
- Document a pipeline, test reruns and explain its reliability, access controls and limitations.
SQL, Python, pandas, PySpark, Parquet, Delta concepts, Git, orchestration concepts and AI API workflows. The emphasis is on understanding and testing the pipeline rather than relying on a particular AI tool.
From source data to a useful result
Files, APIs and SQL sources
Clean, model and enrich
Keys, counts and totals
Trusted tables and AI-ready data
A successful pipeline is repeatable and understandable. It should make errors visible, preserve source context and produce results that another person can verify.
Data Engineering with AI syllabus
Follow 20 modules from foundations to a documented end-to-end project. Exercises use sample or appropriately authorised data.
01. Data engineering and AI foundations
Understand the data-engineering lifecycle, analytical systems, batch versus streaming and the role of trustworthy data in AI applications. Map a source-to-consumption workflow.
02. SQL fundamentals for data work
Practise SELECT, filters, grouping, joins and data types. Read relational schemas and identify primary keys, foreign keys and the grain of a table.
03. Advanced SQL and analytical transformations
Use CTEs, subqueries and window functions to build analytical datasets. Test duplicate handling, null rules and aggregations against independently calculated examples.
04. Python programming for pipelines
Write reusable functions and modules, manage environments and dependencies, and handle exceptions. Use configuration and logging to make scripts understandable and repeatable.
05. API and file ingestion with Python
Collect CSV, JSON and paginated API responses. Handle timeouts, retries, malformed records and partial downloads while preserving source identifiers and extraction timestamps.
06. File formats, schemas and data contracts
Compare CSV, JSON and Parquet. Define explicit types, required fields and schema-change rules; identify incompatible source changes before loading a curated dataset.
07. Data modelling and dimensional design
Build facts and dimensions, define business keys and consider slowly changing dimensions. Match the model to the business questions rather than simply copying source tables.
08. ETL and ELT pipeline construction
Separate extraction, staging, transformation and serving. Implement reusable loading steps, validate outputs and compare where transformations belong in different architectures.
09. Apache Spark foundations
Learn distributed processing, DataFrames, lazy evaluation and the purpose of partitions. Explain how Spark reads files and turns a sequence of transformations into execution work.
10. PySpark transformations and performance
Apply schema-aware cleaning, joins, aggregations and window operations. Explore skew, partition sizes and basic execution-plan interpretation using controlled sample workloads.
11. Delta Lake and lakehouse tables
Study transaction-aware lakehouse storage, merge operations, schema evolution and table history concepts. Track how a changed record moves into the curated layer.
12. Analytical serving and transformation design
Prepare reporting tables and reusable SQL transformations. Introduce warehouse and dbt-style model concepts, document metric definitions and compare outputs with source totals.
13. Workflow orchestration with Airflow concepts
Define tasks, dependencies, schedules and retry behaviour. Plan a backfill and distinguish a dependency-aware workflow from a set of independently scheduled scripts.
14. Incremental loads and change handling
Use watermarks, keys and change-capture concepts. Handle late records and deletes, and design idempotent reruns that do not double-count the same event.
15. Data quality checks and automated tests
Test schema, uniqueness, nulls, freshness and business rules. Quarantine invalid records, reconcile counts and totals, and create small known-answer datasets for regression checks.
16. Monitoring, recovery and operational notes
Collect useful logs and run metadata. Diagnose a failed transformation, define recovery steps and explain freshness expectations and a repeatable troubleshooting process.
17. AI-assisted SQL, coding and documentation
Use an AI assistant to propose queries, transformations, test cases and documentation. Review code, compare results and keep private data out of unapproved AI tools.
18. AI document extraction and structured outputs
Convert selected sample-document fields into a validated schema. Evaluate against labelled examples, preserve source references and route uncertain or invalid outputs for review.
19. Embeddings, retrieval and AI-ready datasets
Introduce chunking, embeddings, metadata and retrieval workflows. Prepare a small knowledge dataset and evaluate whether retrieved evidence supports an AI-generated answer.
20. DataOps, governance and capstone presentation
Version code and configuration with Git; document access, lineage, dependencies and validation. Present an end-to-end project with architecture, quality evidence, recovery notes and honest limitations.
Practical projects and portfolio work
Incremental sales pipeline
Ingest sample orders and customers, clean them, load analytical tables and reconcile daily sales. Demonstrate a safe rerun, duplicate handling and a late-arriving record.
AI-assisted document extraction
Extract selected fields from sample documents into a validated schema. Compare results with a labelled test set and route uncertain or invalid records for review.
PySpark lakehouse workflow
Build raw, cleaned and reporting layers from sample event files. Record input/output counts, schema checks and the effect of a changed source field.
Retrieval-ready knowledge dataset
Prepare a small public-document collection with source IDs and metadata. Test retrieval quality and explain when an AI answer is unsupported by the retrieved material.
For each project, keep the business question, architecture, inputs, code, validation evidence and limitations together. Practise explaining a failure, a correction and the reason for your design choices.
Who can join and how to prepare
This learning path is useful for students, freshers, analysts and software professionals who want to build data-pipeline skills. Basic computer use, introductory SQL and Python fundamentals provide helpful preparation. Contact Softenant to discuss your current level before choosing a batch.
Revise joins, functions, CSV/JSON handling and basic command-line use. If you are starting from zero, ask about the foundation work needed to keep pace with the practical modules.
Course fee, duration and batch details
Data Engineering with AI: ₹12,000 for 3 months. Confirm the current weekday/weekend timetable and classroom or instructor-led online availability before enrolling. Payment terms, taxes, installment options and lab arrangements are confirmed directly with Softenant.
Project and interview preparation
Prepare an honest resume description, a clear architecture diagram and answers about SQL, transformations, quality checks and recovery. A course completion certificate follows completion of training and project work. Resume preparation, mock interviews and job guidance support readiness; employment or salary is not guaranteed.
Frequently asked questions
What is the fee and duration?
The Data Engineering with AI course fee is Rs.12,000 and the duration is 3 months. Contact Softenant for the active timetable, payment terms and batch availability.
Does this include Python and SQL?
Yes. The syllabus covers Python ingestion workflows and SQL skills for joins, transformations, incremental processing and data validation. Learners should discuss their starting level before joining.
How is AI used in the course?
AI is used to assist with code drafts, schema mapping, document extraction and test ideas. Generated results are checked against source data and explicit validation rules before being accepted.
Is this the same as Data Science or Data Analytics?
This course focuses on collecting, transforming, validating and delivering data reliably. Data Analytics focuses on answering business questions; Data Science adds statistical and machine-learning modelling. The skills overlap, but the project goals differ.
What background is useful?
Basic computer skills and comfort with spreadsheets help. Python fundamentals and introductory SQL are useful preparation. Ask Softenant about the foundation work needed for your current level.
Are classes available in Vizag and online?
Ask about the current classroom batch in Visakhapatnam and instructor-led online options. Timetables and availability are confirmed when you enquire.
Does training guarantee a job?
No. Training and interview preparation support skill development. Hiring outcomes depend on demonstrated skills, practice, interviews and available opportunities.
Compare related learning paths
Learning references
Explore the official documentation for the technologies discussed in the syllabus.
Speak to Softenant about the next batch
Call +91 9393969628, email info@softenant.com or contact the team for the timetable and enrollment details.
Visit: Flat No.101, Geetha Mansion II, opposite Andhra Bank, Akkayyapalem, Visakhapatnam, Andhra Pradesh 530016.