Data Engineering for AI: Pipelines, Vector Stores and Data Quality
Master data engineering for AI: batch vs streaming, Kafka, Airflow, dbt, lakehouse, data quality, feature stores, embeddings and vector databases. 2026.
The course’s skills. The same ones appear on your Certificate of Completion, so you can show what you covered.
The Data Foundation of AI Systems
Storage and Table Formats: Parquet and Iceberg
Ingestion from Diverse Sources
ETL/ELT and Orchestration
Data Quality, Validation, and Contracts
Preprocessing for LLMs and RAG
Feature and Embedding Pipelines
Vector Databases in Depth
About the course
A premium, advanced course on the data foundation that every serious AI system depends on, updated for 2026. This is not a retrieval-and-generation (RAG) course - it is the data engineering underneath: how raw data becomes trustworthy fuel for LLMs, agents and vector search. You will master batch versus streaming paradigms, real-time ingestion with Kafka and Flink, ETL/ELT and orchestration with Apache Airflow, Dagster and dbt, lakehouse architecture with Parquet and Apache Iceberg, ingestion from diverse sources with change data capture and schema evolution, data quality and validation with Great Expectations, data contracts and governance, testing and CI/CD for pipelines (DataOps), chunking and preprocessing for LLM pipelines, feature stores for ML and AI workloads, embeddings pipelines at scale, and vector databases in depth (pgvector, Pinecone, Qdrant, Weaviate) including HNSW and IVF index internals, hybrid search, sharding and metadata filtering. It closes with retrieval quality evaluation, metadata and lineage, PII detection and anonymization, GDPR-compliant pipelines, cost and performance engineering, pipeline monitoring, and a comprehensive final assessment. Real Python and SQL throughout.
Who it is for
For developers, engineers and technical professionals who build or integrate AI solutions. It belongs to AI for Developers, on the IT track.
What you need
Advanced level: assumes hands-on experience with AI in the course’s field.
Batch vs Streaming: Choosing a Processing Paradigm50 min12 questions
Data Lakes, Warehouses, and the Lakehouse50 min12 questions
2
Module 2: Storage and Table Formats: Parquet and Iceberg
2 lessons · 1 h 40 min · 24 questions
Columnar Storage with Apache Parquet50 min12 questions
Apache Iceberg: Table Format, Partitioning, and Layout50 min12 questions
3
Module 3: Ingestion from Diverse Sources
3 lessons · 2 h 30 min · 36 questions
Ingestion Patterns: CDC, APIs, Files, and Streams50 min12 questions
Connectors, Schema Evolution, and Idempotent Loading50 min12 questions
Real-Time Streaming Ingestion with Kafka and Flink50 min12 questions
4
Module 4: ETL/ELT and Orchestration
3 lessons · 2 h 30 min · 36 questions
ETL vs ELT and Orchestration Fundamentals50 min12 questions
Orchestrating Pipelines with Apache Airflow50 min12 questions
Asset-Oriented Orchestration with Dagster and dbt50 min12 questions
5
Module 5: Data Quality, Validation, and Contracts
4 lessons · 3 h 20 min · 48 questions
Data Quality Dimensions and Great Expectations50 min12 questions
Data Contracts and Schema Enforcement50 min12 questions
Cleaning, Deduplication, and Normalization50 min12 questions
Testing and CI/CD for Data Pipelines (DataOps)50 min12 questions
6
Module 6: Preprocessing for LLMs and RAG
3 lessons · 2 h 30 min · 36 questions
Document Parsing and Text Extraction50 min12 questions
Chunking Strategies for LLM Pipelines50 min12 questions
Preparing and Enriching Chunks at Scale50 min12 questions
7
Module 7: Feature and Embedding Pipelines
3 lessons · 2 h 30 min · 36 questions
Building an Embeddings Pipeline50 min12 questions
Batch Embedding, Caching, and Incremental Updates50 min12 questions
Feature Stores for ML and AI Workloads50 min12 questions
8
Module 8: Vector Databases in Depth
5 lessons · 4 h 10 min · 60 questions
Vector Index Internals: HNSW, IVF, and Quantization50 min12 questions
pgvector: PostgreSQL as a Vector Store50 min12 questions
Dedicated Vector Databases: Pinecone, Qdrant, and Weaviate50 min12 questions
Hybrid Search, Metadata Filtering, and Sharding50 min12 questions
Evaluating Retrieval Quality and Embedding Drift50 min12 questions
9
Module 9: Governance, Lineage, PII, and Operations
4 lessons · 3 h 20 min · 48 questions
Metadata and Data Lineage50 min12 questions
Data Governance and GDPR in Pipelines50 min12 questions
PII Detection and Anonymization50 min12 questions
Cost, Performance, and Pipeline Monitoring50 min12 questions
10
Module 10: Final Quiz - Data Engineering for AI
1 lesson · 45 min · 30 questions
Final Assessment - Data Engineering for AI45 min30 questions
By email
Email me the course syllabus
Modules and lessons in order, plus the link to the preview of lesson 1 — so you can decide at your own pace.
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How you learn
You read the lesson, ask the AI Professor right on the page and check what you remember.
Lesson 1 · 50 min
The Role of Data Engineering in AI in 2026
Every impressive AI system you have ever used — a support agent that answers from a company knowledge base, a code assistant grounded in a monorepo, a semantic search over millions of documents — is standing on a data pipeline. The model is the visible tip; the data engineering underneath is the iceberg.
The first lines of lesson 1 of this course.
Read the free preview, no account needed.
You read the lesson at your own pace
Written lessons, with examples and exercises. Your progress is saved automatically, so you can pick up where you left off.
You ask the AI Professor inside the lesson
Select a passage and it explains it, gives you an example or tells you why it matters. You can type or talk to it, in English.
You check what you remember
Every lesson has its own quiz, with explanations for the answers: 390 questions across the course. The professor also summarizes the lesson and prepares review flashcards.
You get the Certificate of Completion
Once you complete the lessons and reach the minimum quiz score, you issue it from your account, with a unique number, publicly verifiable.
The AI Professor is included in your subscription, per-course access included, with a monthly fair-use limit. It is an AI system, not a human teacher, and its answers may contain errors; details in the Terms. See how the AI Professor works.
You leave with proof anyone can check
The Certificate of Completion for “Data Engineering for AI: Pipelines, Vector Stores and Data Quality”: a private attestation with a unique number and a QR code. A recruiter confirms it is authentic on the public verification page, with no account and no cost.
The real document, an A4 PDF, shown here with sample data. Yours carries your name, the course “Data Engineering for AI: Pipelines, Vector Stores and Data Quality”, the serial number, the issue date and the tier you earned; the QR code in the image is decorative.
Anyone can verify it
Public verification page, QR code and cryptographic fingerprint, with no account for the person checking.
You show it where it counts
Add it to your CV and LinkedIn profile; a recruiter confirms authenticity with one click.
It says exactly what it attests
The course, the tier you earned (Pass, Merit or Distinction), the serial number and the issue date, plus the percentage of lessons completed.
A private attestation of course completion. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion. Certification policy.
Pricing
Just this course, or more with this one included
A subscription by number of courses: 1, 3, 5, 10, 15 or 25 courses a month, or the full catalog. You pick them and can change them at every renewal; your progress and certificates stay. Billed monthly or yearly (12 months for the price of 10), in EUR. You can stop the renewal anytime from your account, and access starts right after payment. Prices are totals, with VAT included at the rate of your country.
How many courses a month?
This course goes first on your list; you pick the others in your account and can change them at every renewal.
Total prices, VAT included. Move up anytime and pay only the difference for the days left; move down at renewal. Renews automatically, you stop it anytime from your account; your progress and certificates stay. Digital content with immediate access: see the withdrawal terms.
About the course, the price, the AI Professor and the certificate.
What will I learn in “Data Engineering for AI: Pipelines, Vector Stores and Data Quality”?
A premium, advanced course on the data foundation that every serious AI system depends on, updated for 2026. This is not a retrieval-and-generation (RAG) course - it is the data engineering underneath: how raw data becomes trustworthy fuel for LLMs, agents and vector search. You will master batch versus streaming paradigms, real-time ingestion with Kafka and Flink, ETL/ELT and orchestration with Apache Airflow, Dagster and dbt, lakehouse architecture with Parquet and Apache Iceberg, ingestion from diverse sources with change data capture and schema evolution, data quality and validation with Great Expectations, data contracts and governance, testing and CI/CD for pipelines (DataOps), chunking and preprocessing for LLM pipelines, feature stores for ML and AI workloads, embeddings pipelines at scale, and vector databases in depth (pgvector, Pinecone, Qdrant, Weaviate) including HNSW and IVF index internals, hybrid search, sharding and metadata filtering. It closes with retrieval quality evaluation, metadata and lineage, PII detection and anonymization, GDPR-compliant pipelines, cost and performance engineering, pipeline monitoring, and a comprehensive final assessment. Real Python and SQL throughout.
The course has 10 modules and 31 lessons, with 390 quiz questions. The full curriculum, module by module, is shown on the course page.
Do I need prior experience for this course?
Yes. The course is Advanced level and assumes hands-on experience with AI in its field.
How long does the course take?
The estimated duration is about 26 hours, across 31 lessons. You learn at your own pace, and your progress is saved automatically at every lesson, so you can pick up where you left off.
How much does it cost, and which plan do I need?
This course alone: the 1 course plan, €99 per month. The course fits into every plan of the subscription, next to other courses you choose: for example 3 courses for €199, 10 courses for €319 (recommended) or the full catalog for €499 per month.
You pick your courses after paying and can change them at every renewal; starting from here, this one is already picked. Plans are billed in EUR, monthly or yearly (pay for 10 months, get 12). Every price is a total, with VAT included at the rate of your country. See all plans.
Can I read part of the course before paying?
Yes. You can read a free preview of lesson 1, “The Role of Data Engineering in AI in 2026”, without an account. The preview shows you how the lesson is written; the rest of the lesson and the other lessons open after payment.
How does the AI Professor help me in this course?
The AI Professor is built into every lesson and starts from the lesson you have open: it explains the passages you select, gives you examples, summarizes the lesson, tests you with a quiz and prepares review flashcards. You can type or talk to it, in English.
It is included in every plan of your subscription, with a fair-use limit. It is an AI system, not a human teacher, and its answers may contain errors; details in the Terms.
Do I get a certificate when I finish the course?
When you meet the completion thresholds shown on the platform (lessons completed and the minimum quiz score), you can issue your Certificate of Completion for “Data Engineering for AI: Pipelines, Vector Stores and Data Quality” from your account, with a unique number, publicly verifiable on the site.
It is a private attestation of course completion, issued by GALIAN SOFTWARE DEVELOPMENT SRL. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion. Details in the Certification policy.
Does the subscription renew automatically? How do I stop it?
Yes. The subscription renews automatically at the end of each monthly billing period. You can see the next renewal date in your account at any time.
You can stop the renewal at any time, from your account or by email, with no other formality. Access stays active until the end of the period already paid for, and no pro-rata refund is made for a period already started, except in the cases provided by law. Your progress stays saved in your account. Details in the Terms & Conditions, section 5.5.
Can my company buy this course for a team?
Yes. With the team library, the company admin picks 3, 5, 10, 15 or 25 courses a month, or the full catalog, for the whole team, from €49 per member per month, excl. VAT, for 5 members or more. A dashboard shows each member’s progress and manages seats centrally. Price it for your team.
From the same track
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You can pick them together with this one in a 3, 5 or 10-course subscription (3 courses for €199 a month), and the full catalog includes them all. A single course is €99 a month.
Integrate LLMs into production apps: streaming, function calling, MCP, multi-model routing, cost optimization, security and observability. Updated for 2026.
“Data Engineering for AI: Pipelines, Vector Stores and Data Quality”: 31 lessons and about 26 hours of content, with your progress saved at every lesson.
Instant access after payment, on computer, tablet or phone.
AI Professor in every lesson, included in your subscription.
Certificate of Completion, publicly verifiable.
1-course subscription
€99/month
VAT included · stop the renewal anytime from your account
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