SB
Spandana Bala
Data Engineer
Hyderabad · India
Now at · Microsoft
Data Engineering · Spark · Kafka · Lakehouse · dbt · Fabric · AI pipelines · Enrolling now
Data Engineering & AI
A career-focused, code-first program: build strong SQL, Python, Spark and cloud foundations, then specialise in streaming with Kafka and Flink, Iceberg and Delta lakehouses, dbt, Airflow and Microsoft Fabric — and build the governed, AI-ready data platforms that agents, RAG and ML now depend on.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Digital Edify centres
Where our Data Engineering alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the Data Engineering & AI program?
A data engineer builds the platforms that move, store, model and govern data — and now the vector stores, feature pipelines and MCP-connected data services that agents and ML run on. Most data engineering courses end at a batch job. This program ends only when you have shipped a lakehouse with batch and streaming pipelines, dbt models under tests and contracts, orchestration and CI/CD, observability and cost controls, and an AI-ready serving layer that a RAG or agent workload actually uses.
The complete delivery chain
Twelve links, one owner — end to end.
01INGEST & STORE
- •Sources, CDC & APIs into the lakehouse
- •Kafka, Flink & Spark Structured Streaming
- •Iceberg, Delta Lake & object storage
- •Schema evolution & partitioning
02TRANSFORM & MODEL
- •Spark & Databricks batch processing
- •dbt models, tests & semantic layers
- •Dimensional & data vault modelling
- •Airflow / Dagster orchestration
03SERVE & GOVERN
- •Vector, feature & serving layers for AI
- •MCP data services for agents
- •Data contracts, lineage & quality monitoring
- •CI/CD, observability & FinOps
Data engineering in 2026
Every agent needs a data platform. Data engineers build it.
Open table formats wonStorage layer
Apache Iceberg and Delta Lake are the default lakehouse formats across Databricks, AWS, Google and Microsoft Fabric. Engineers now design for open storage and many engines rather than one warehouse.
Streaming became normalMovement layer
Kafka, Flink and Spark Structured Streaming with CDC feed real-time features, agents and dashboards. Batch-only pipelines are the exception in new builds.
Data for AINew '26
Vector stores, embedding pipelines, feature stores and RAG-ready document pipelines are now data engineering deliverables — plus MCP servers that let agents query governed data safely.
AI-assisted data engineeringWorkflow shift
Claude Code, Cursor, Databricks Assistant and dbt Copilot write pipelines, tests and SQL. Engineers direct, review and harden — and build the evals that keep generated code honest.
Microsoft Fabric & unified platformsPlatform layer
Fabric, Databricks and BigQuery bundle storage, compute, orchestration and BI on one lakehouse. Platform fluency and cost control matter as much as any single tool.
Data contracts & governanceTrust layer
Contracts, lineage, quality monitoring and catalogs such as Unity Catalog and Purview — with the EU AI Act and DPDP now applying to the data that trains and grounds AI.
What this means for your career: data engineering roles now ask for streaming, lakehouse formats, dbt and AI-data skills alongside Spark and SQL — the differentiator is a governed platform with batch and streaming pipelines, tests, CI/CD and an AI serving layer you can demonstrate, not a certificate in one tool.
Who should join
Built for people moving into AI-era data engineering.
CS / IT graduates & career switchers
Software developers moving to data
Data analysts & BI developers going deeper
ETL / warehouse developers modernising
DevOps & cloud engineers
Database administrators
Prior experience: basic programming helps; SQL and Python are taught from scratch. The program builds foundations before Spark, streaming, lakehouse, dbt and data for AI.
What you will be able to do
Build the platform — and make it AI-ready.
Engineer with SQL & PythonAdvanced SQL, PostgreSQL, Python for pipelines, testing and packaging.
Process at scaleSpark and Databricks batch jobs, Delta Lake and Apache Iceberg lakehouses, Trino queries.
Stream in real timeKafka, Flink, Spark Structured Streaming and CDC with exactly-once semantics.
Model and orchestrateDimensional modelling, dbt with tests and contracts, Airflow and Dagster, Microsoft Fabric.
Serve data to AIVector and feature stores, embedding and RAG pipelines, MCP data services with permissions.
Govern and operateData quality, lineage, catalogs, CI/CD, observability, FinOps and AI-assisted engineering.
Course curriculum
Twelve sections. 56 modules. SQL → Python → Spark → Lakehouse → Streaming → dbt → Data for AI.
01
Fundamentals of Data Engineering & AI
How data platforms work, the 2026 landscape and where the data engineer sits in an AI organisation.
Data engineer, analytics engineer, platform and ML engineer roles
Lakehouse, warehouse and streaming architectures
Open table formats and multi-engine platforms
Career pathways and certifications
Batch, streaming and lambda / kappa architectures
Medallion architecture — bronze, silver, gold
Data mesh and data products
Choosing patterns for a use case
IaaS, PaaS and SaaS for data workloads
AWS and Azure core services — storage, compute, identity
Object storage and networking basics
Cost models
Linux, shell, Git and Docker basics
Python, uv and VS Code
Claude Code and Cursor for data engineering
Reviewing generated pipelines
How RAG, agents and ML consume data
Vector, feature and serving layers
Data quality as an AI risk
Governance and the EU AI Act
02
SQL & Relational Databases
From first query to production-grade PostgreSQL.
SELECT, JOIN, GROUP BY and set operations
Subqueries and CTEs
Data types and constraints
NULL handling
Window functions and analytical patterns
Recursive CTEs and JSON
Query plans and indexing
Performance tuning
DDL, DML and transactions
PL/pgSQL functions and triggers
Partitioning and extensions
Backups and replication basics
Normalisation and 3NF
Star schema — facts and dimensions
Slowly changing dimensions
Data vault overview
Document, key-value, wide-column and graph stores
MongoDB, Redis and Cassandra overview
Choosing a store
Polyglot persistence
03
Python for Data Engineering
Production Python for pipelines — typed, tested and packaged.
Types, control flow, functions and comprehensions
Modules, packages and environments
Files, JSON and APIs
Error handling and logging
Classes, dataclasses and typing
Decorators, generators and iterators
Functional patterns for pipelines
Concurrency and async basics
DataFrames, joins and reshaping
Polars for performance
DuckDB for local analytics
Parquet and Arrow
pytest and fixtures for data code
Ruff, typing and pre-commit
Packaging and CLIs
Project structure
REST APIs, pagination and auth
Rate limits and retries
Incremental extraction
Scheduling scripts
04
Batch Processing with Spark & Databricks
Distributed processing on the platform most enterprises run.
Drivers, executors and the DAG
RDDs, DataFrames and Datasets
Lazy evaluation and actions
Spark UI
Transformations, joins and aggregations
Spark SQL and UDFs
Reading and writing Parquet, JSON and CSV
Handling skew and partitions
Caching, partitioning and shuffles
Adaptive query execution
Broadcast joins
Cluster sizing
Workspaces, clusters and notebooks
Jobs and workflows
Unity Catalog basics
Databricks Assistant
ACID transactions and time travel
MERGE, schema evolution and optimisation
Delta Live Tables
Medallion pipelines
05
Lakehouse & Open Table Formats
Design open, multi-engine storage that lasts.
S3 and ADLS design
Parquet, ORC and Avro
Compression and partitioning
Cost and performance
Iceberg architecture — metadata, snapshots, manifests
Schema and partition evolution
Catalogs — REST, Glue, Unity
Maintenance — compaction and expiry
Federated queries with Trino
Athena on Iceberg
DuckDB for local and embedded analytics
Engine selection
BigQuery, Redshift and Fabric Warehouse compared
Lakehouse vs warehouse trade-offs
Zero-copy sharing patterns
Migration considerations
06
Streaming & Real-Time Data
Move from batch to continuous pipelines.
Topics, partitions, producers and consumers
Consumer groups and offsets
Kafka Connect and Schema Registry
Confluent Cloud
CDC concepts and Debezium
Database to Kafka to lakehouse
Handling deletes and schema changes
Exactly-once considerations
Flink architecture and state
DataStream and Table API
Windows, watermarks and event time
Flink SQL
Micro-batch and continuous modes
Stateful operations and watermarks
Streaming into Delta and Iceberg
Monitoring streaming jobs
Real-time features for ML and agents
Event-driven microservices
Kinesis and Pub/Sub alternatives
Cost and complexity trade-offs
07
Data Modelling, dbt & Semantic Layers
Transform raw data into governed, tested models everyone can reuse.
Kimball methodology
Fact and dimension design
SCD types and surrogate keys
Conformed dimensions
Models, sources, refs and materialisations
Tests and documentation
Jinja and macros
dbt Core and dbt Cloud
Incremental models and snapshots
Packages and CI
Exposures and metrics
dbt Copilot
dbt Semantic Layer and MetricFlow
Power BI semantic models and Tableau Knowledge
Metrics for AI copilots and agents
Governance of definitions
Data vault 2.0 concepts
One Big Table and activity schema
Choosing a modelling approach
Modelling for AI consumption
08
Orchestration, DataOps & Microsoft Fabric
Run pipelines reliably — and on the unified platforms enterprises are adopting.
DAGs, operators and sensors
Scheduling, backfills and dependencies
Astronomer and managed Airflow
Best practices
Software-defined assets
Dagster vs Airflow vs Prefect
Asset lineage and observability
Choosing an orchestrator
GitHub Actions for data pipelines
Terraform for data infrastructure
Environments and promotion
Testing pipelines
OneLake, Lakehouse and Warehouse
Data Factory, notebooks and pipelines
Real-Time Intelligence
Capacities, licensing and Copilot
Pipeline monitoring and alerting
Data observability tools
Cost tracking and optimisation
Incident response
09
Data Quality, Governance & Security
Make the platform trustworthy — and auditable.
Great Expectations and Soda
dbt tests and anomaly detection
Quality dashboards
Handling bad data
Data contracts and schema enforcement
Lineage with OpenLineage and catalogs
Impact analysis
Producer-consumer agreements
Unity Catalog and Microsoft Purview
Classification and tagging
Access policies and row / column security
Audit
Encryption, IAM and network security
PII handling and masking
GDPR, DPDP and the EU AI Act for data
Compliance evidence
10
Data for AI — Vector, Feature & Agent Data Services
The new deliverables data engineers own in AI organisations.
How LLMs consume data — context, embeddings, tools
Structured outputs and tool calling
Hallucination and grounding
Cost and model choice
Document ingestion, chunking and metadata
Embedding models and batch pipelines
Vector stores — pgvector, Qdrant, Pinecone, Databricks Vector Search
Refresh, versioning and evaluation
Feature store concepts — Feast, Databricks Feature Store
Training-serving consistency
Point-in-time correctness
Serving features to models
Model Context Protocol basics
Exposing lakehouse, dbt and APIs as MCP tools
Permissions, row-level security and audit for agents
Text-to-SQL agents on semantic layers
PDF, image and audio pipelines
LLM-based extraction and classification at scale
Quality checks on extracted data
Storing unstructured outputs
11
AI-Assisted Data Engineering
Use the tools that write pipelines — and keep them honest.
Generating pipelines, tests and SQL
Spec-driven development for data
Reviewing and hardening generated code
Team conventions
Databricks Assistant and Genie
dbt Copilot
Fabric Copilot for pipelines
Verifying assistant output
Agents for pipeline triage and incident response
Connecting Claude and ChatGPT to Airflow, Databricks and Slack via MCP
Workflow automation with n8n and Make
Guardrails for automated operations
Test-first workflows
Data diff and regression testing
Security review of generated code
Measuring productivity
12
Capstone, Portfolio & Career
A production lakehouse with streaming, dbt and an AI serving layer — verifiable by employers.
Iceberg or Delta lakehouse on cloud object storage
Batch with Spark and streaming with Kafka and Flink
dbt models with tests, contracts and lineage
Airflow orchestration, CI/CD, observability and FinOps
Embedding and RAG pipeline on lakehouse documents
Feature pipeline for a model
MCP data service with permissions and evals
Public verification URL
Databricks Data Engineer Associate / Professional
AWS Data Engineer Associate
Microsoft Fabric Data Engineer (DP-700)
dbt Analytics Engineering and Confluent Kafka
GitHub portfolio with architecture diagrams and READMEs
Resume rewrite around platforms shipped
Data engineering interviews — SQL, Spark, system design
Warm introductions to hiring partners
Tools you'll master
32+ data engineering & AI tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three portfolio projects and a partner capstone, each threaded through the entire curriculum — Spark, streaming, lakehouse, dbt and data for AI all land in real deliverables.
Hero project
Production lakehouse + streaming pipeline + AI agent
Ship a full lakehouse on Iceberg/Delta, wire a Kafka + Flink streaming layer into it, orchestrate the whole stack on Airflow with data contracts, and bolt on a LangGraph augmentation agent.
01Lakehouse on Iceberg/Delta — bronze/silver/gold layers, dbt models with tests + docs, partitioned + sorted for query performance.
02Streaming layer — Kafka topics, Flink stateful processing, exactly-once writes to the lakehouse, late-data handling.
03Orchestration on Airflow/Dagster with data contracts, Great Expectations data-quality gates, lineage in the catalog.
04AI augmentation agent — a LangGraph agent that profiles tables, drafts test cases, explains lineage, and answers analyst questions over the warehouse.
Outcome: 4× faster pipeline build
Data SLA: 99.9%
Reviewer: Data Platform panel
SparkKafkaIcebergdbtLangGraph
Enterprise
Streaming CDC pipeline
Build a Postgres → Debezium → Kafka → Flink → Iceberg CDC pipeline with exactly-once semantics, schema-registry contracts, and Monte Carlo monitoring.
DebeziumKafkaFlinkIceberg
Real-time
Self-tuning lakehouse agent
Stand up a LangGraph agent that watches table-level metrics — latency, freshness, cost — auto-files dbt issues, drafts fixes, and benchmarks query plans on Trino.
LangGraphTrinodbtMonte Carlo
Project
Your AI data platform in a controlled project environment.
Pick a real partner data problem. Deploy a production lakehouse + streaming pipeline + AI agent — Iceberg storage, Flink processing, dbt models, LangGraph augmentation — into a partner team that's running it for real users.
Download the real world project
Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Edify · Principal Data Engineering Architect
Spark · Kafka · Airflow · dbt · Iceberg · Fabric · LangGraph
"A 2026 data engineer doesn't stop at moving rows. They ship a lakehouse you can stake
an SLA on, a streaming layer that survives a bad partition key, orchestration that an on-call engineer
doesn't fight with, and an LLM agent that answers analyst questions over the warehouse. That's the bar I
teach to, every class."
15 yrs
DATA & AI
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Digital Edify and brings 15 years of applied data engineering from AT&T, Salesforce, Cox Communications, and Broadcom — where he led lakehouse, streaming, and orchestration platforms for Fortune-500 banks, telcos, and insurers. Most recently he architected production data platforms that pair Iceberg/Delta lakehouses, Flink streaming, and dbt models with a LangGraph augmentation layer that explains lineage and drafts test cases for analyst teams.
His classes get you two things other programs don't give you: a founding architect who still ships production data platforms, and a curriculum rewritten every quarter to match what hiring managers actually ask about — credentials like AWS Data Engineer Associate, Databricks Data Engineer Professional, Microsoft Fabric DP-700, Confluent Kafka Developer, and dbt Analytics Engineer included. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Edify · Data Platform & Streaming Lead
Spark · Kafka · Flink · dbt · Iceberg · Airflow · LangGraph
"A data platform earns its keep when the lakehouse is auditable, the streaming layer
keeps its exactly-once promise on a bad day, the data SLAs hold under real load, and an LLM agent answers
analyst questions before the meeting starts. I teach the unglamorous parts that make all of that real in
production."
10 yrs
DATA PLATFORMS
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Edify, where he leads the data platform and streaming practice. After ten years building and running production lakehouses and streaming pipelines across enterprise — telecom, banking, and SaaS — he stepped into the Chief Technologist seat to wire Spark, Kafka, Flink, Iceberg, and dbt into the way data teams actually work — data contracts that hold under schema drift, freshness SLAs that on-call engineers trust, and a LangGraph augmentation layer that explains lineage to the analysts who own the numbers.
His data platform modules are built from real production post-mortems, not slide decks. Expect to leave with working Iceberg lakehouses, Flink streaming jobs with exactly-once semantics, dbt models with tests and docs, Airflow orchestration with data contracts, and a LangGraph augmentation agent wired into the warehouse. Ten years across enterprise data platforms — Hyderabad-based, hands-on, and known for the unglamorous parts of data engineering that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What data engineering employers say about Digital Edify grads.
Real feedback from data and platform leaders at AI-first companies and the firms hiring our Data Engineering & AI graduates.
Digital Edify grads ramp 40% faster on data platform deploys than typical data engineering hires. Best Data Engineering & AI pipeline in India.
Aakash Mehta, Engineering Director, Microsoft
We've onboarded 80+ Digital Edify alumni in 18 months. Lowest ramp time we've seen for lakehouses, streaming pipelines, and AI augmentation practices.
Anita Sharma, Senior Manager, Deloitte
The Data Engineering & AI programme is comprehensive — Spark, Kafka, dbt, LangGraph augmentation. Grads come pre-trained for production data platforms with AI.
Rahul Bhatt, Solutions Lead, Mphasis
Their lakehouse + streaming track produces PMs who ship production-grade pipelines on day one. Rare combination of engineering rigor and platform craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Edify apart is the AI augmentation layer baked into the data engineering track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their Databricks Data Engineer Pro + dbt Analytics Engineer prep is rigorous, and the shipped project — lakehouse, streaming pipeline, AI augmentation agent — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Edify's Data engineers ship reliable pipelines twice as fast in the first 90 days. Our internal platform metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Data Engineering & AI pipeline we've sourced from in India. Their projects are real shipped pipelines, not slide demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong Spark and lakehouse engineering foundation. Their Data Engineering grads need almost zero ramp time on enterprise data platform engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Edify alumni across our data and watsonx engineering teams. Strong fundamentals, sharp on data SLAs and lineage.
Sanjay Verma, Talent Director, IBM
lakehouses + AI augmentation is exactly the talent gap we've been struggling to close. Digital Edify is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their Data Engineering track delivers engineers who navigate Spark, Kafka, and dbt on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Edify graduates for our data engineering practice. Strong on Spark, sharp on Kafka/Flink, fluent in dbt.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Edify grads who blend lakehouses with Azure OpenAI augmentation land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Edify · Institute Certificate
Agent‑Ready Data Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a data platform —
Iceberg lakehouse, Kafka/Flink streaming, dbt models, and an AI augmentation agent — evaluated against the Databricks Data Engineer Pro, dbt Analytics Engineer, and AWS Data Engineer Associate credential rubrics.
Manikanta Kona
CEO · Digital Edify
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the data engineering community and mapped to Databricks Data Engineer Pro
and dbt Analytics Engineer credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — Iceberg lakehouse, Flink streaming pipeline, dbt models, AI augmentation agent — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: lakehouse design, streaming pipelines, dbt models, data contracts,
quality gates & lineage. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
Job roles
Roles this program prepares you for.
Data Engineer
Build and run batch and streaming pipelines on the lakehouse.
Analytics Engineer
Own dbt models, tests, contracts and semantic layers.
Data Platform Engineer
Design and operate lakehouse, orchestration and governance platforms.
Streaming / Real-Time Data Engineer
Kafka, Flink and CDC pipelines with exactly-once guarantees.
Databricks / Spark Engineer
Spark, Delta Lake and Unity Catalog at scale.
Microsoft Fabric Data Engineer
OneLake, pipelines and Real-Time Intelligence on Fabric.
AI Data Engineer
Vector, feature and RAG pipelines and MCP data services for agents.
ML Platform / MLOps Engineer
Feature stores, training data and serving infrastructure.
Data Governance & Quality Engineer
Contracts, lineage, catalogs and compliance.
Data Architect (career path)
Grow toward designing enterprise data and AI platforms.
What employers should see in your portfolio: that you can take raw sources to an AI-ready platform — ingest with CDC and Kafka, store in Iceberg or Delta, process with Spark and Flink, model with dbt under tests and contracts, orchestrate with Airflow and CI/CD, and serve vector, feature and MCP data services that agents actually use.
04Job placement support
Your first Data Engineer offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into data-heavy SaaS and platform teams. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your lakehouse design, streaming pipeline, dbt models, AI augmentation agent, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the data platforms you shipped (lakehouses, streaming pipelines, dbt models), the partner-org project, and the business outcome. Reviewed by engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into data-heavy SaaS and platform teams — Microsoft, Databricks, dbt Labs, Confluent, Fivetran, AWS, Anthropic, Hugging Face, Scale AI, Stripe, Razorpay, plus services that staff data platform teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
Data Engineering alumni
Hundreds of data engineering careers launched — here are eight.
NV
Naveen Vedala
Senior Data Engineer
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff Data Engineer (Streaming)
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal Data Engineer
Seattle · United States
Now at · Confluent
MM
Mujahed Mohammed
Lakehouse Architect
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Streaming Platform Lead
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
Analytics Engineering Lead
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of Data Platform
Hyderabad · India
Now at · dbt Labs
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal Data Engineer classes running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening Data Engineering classes running on IST and PST. Every online class ships
the same shipped project — Iceberg lakehouse, Flink streaming, dbt models, AI augmentation — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Admissions
ENROLLING NOW
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the data platform stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior SQL/Spark experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, and self-taught coders. The opening modules cover the SQL fundamentals, distributed compute, and pipeline design from scratch. What you do need is consistency and regular practice.
Will I actually ship production pipelines, or only do tutorials?
You actually ship. Every learner builds a lakehouse on Iceberg/Delta with bronze/silver/gold layers, a Kafka → Flink streaming layer with exactly-once semantics, dbt models with Great Expectations gates, and a LangGraph agent that augments the platform. The project runs in a partner org — not a notebook.
Which tools, frameworks, and AI models will I use?
Compute: Spark, Flink, Trino, Databricks. Streaming: Kafka, Kinesis, Pub/Sub. Storage: Iceberg, Hudi, Delta Lake, S3, BigQuery, Redshift. Orchestration: Airflow, Prefect, Dagster, dbt. Quality: Great Expectations, Monte Carlo, Data Contracts. AI: OpenAI, LangChain, LangGraph.
Will I prep for AIPMM Data Engineer and Pragmatic Principal Data Engineer certs?
Yes. The curriculum is mapped to the AIPMM Data Engineer track and the Pragmatic Principal Data Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire data engineers?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Digital Edify does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same shipped project — Iceberg lakehouse, Flink streaming, dbt models, AI augmentation — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for learners needing additional support, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
