ADaSci Certified Data Engineer ACDE
The ADaSci Certified Data Engineer program is a 30-hour self-paced online certification designed to build practical expertise in modern data engineering. It offers end-to-end training in designing scalable data pipelines, managing cloud infrastructure, and ensuring data quality and governance.
- of content
- 30 h
- of content
- exam questions
- 60
- exam questions
- proctored exam
- 1 hours
- proctored exam
- validity
- Lifetime
- validity
10 enrolled · exam retake $20 · member pricing available
What this credential certifies.
The ADaSci Certified Data Engineer (ACDE) is a 30-hour, self-paced professional certification designed to equip engineers, analysts, and developers with end-to-end mastery over modern data architecture.
As enterprises scale generative AI and predictive analytics, traditional data pipelines face a severe bottleneck: over 70% of enterprise AI projects fail due to poor data quality, unmanaged schemas, and unscalable batch processing . ACDE validates your ability to design, build, and operate robust batch and real-time streaming pipelines, open table formats (Apache Iceberg/Delta Lake), cloud…
ADaSci Certified Data Engineer (ACDE)
The Global Credential for Building Enterprise-Scale Data Infrastructure & AI-Ready Pipelines
The ADaSci Certified Data Engineer (ACDE) is a 30-hour, self-paced professional certification designed to equip engineers, analysts, and developers with end-to-end mastery over modern data architecture.
As enterprises scale generative AI and predictive analytics, traditional data pipelines face a severe bottleneck: over 70% of enterprise AI projects fail due to poor data quality, unmanaged schemas, and unscalable batch processing. ACDE validates your ability to design, build, and operate robust batch and real-time streaming pipelines, open table formats (Apache Iceberg/Delta Lake), cloud warehouses (Snowflake, BigQuery, Redshift), and automated orchestration workflows.
2026 Market Dynamics: The Backbone of Enterprise AI
- The AI Data Bottleneck: Enterprise models are only as good as the underlying data. Companies urgently need engineers who can build low-latency ingestion pipelines, enforce data governance/lineage, and structure unstructured data for vector search RAG systems.
Core Curriculum & Production Pillars Mastered
The ACDE program delivers hands-on execution capabilities across six core data engineering pillars:
Pillar 1: Data Architecture & Storage Fundamentals
- Focus: Designing scalable relational, NoSQL, and object-storage layers.
- Skills: Data modeling (Kimball dimensional modeling, Data Vault 2.0), cloud data lakes (AWS S3, GCP GCS), and open lakehouse table formats (Apache Iceberg, Delta Lake).
Pillar 2: High-Throughput Batch & Stream Processing
- Focus: Processing structured and unstructured data at scale.
- Skills: Distributed compute with Apache Spark (PySpark/Scala), real-time event streaming with Apache Kafka, and windowed stream transformations.
Pillar 3: Pipeline Orchestration & Workflow Management
- Focus: Automating complex dependency graphs and monitoring pipeline health.
- Skills: Apache Airflow DAG authoring, Dagster orchestrations, backfilling logic, automated retries, and SLA alerting.
Pillar 4: Cloud Data Warehousing & Modern Transformations
- Focus: Operating high-performance cloud analytical warehouses.
- Skills: Analytics engineering with dbt (data build tool), SQL query optimization, partition pruning, and warehousing on Snowflake, Google BigQuery, and AWS Redshift.
Pillar 5: Data Quality, Governance & Observability
- Focus: Enforcing data contracts, schema evolution, and lineage.
- Skills: Automated data quality testing (Great Expectations, dbt tests), data lineage tracking, and zero-trust data security controls.
Pillar 6: AI-Ready Data Pipelines & Vector Storage Integration
- Focus: Bridging traditional data platform engineering with AI infrastructure.
- Skills: Chunking and embedding generation pipelines, vector database ingestion (Weaviate, Pinecone), and unstructured data preprocessing for LLM workloads.
Comparative Learning Pathways: ACDE vs. Adjacent Tracks
| Program Name | Primary Technical Focus | Ideal Career Outcome |
| Certified Data Engineer (ACDE) | Data Infrastructure, Spark/Kafka, Cloud Warehousing & Orchestration | Data Engineer / Analytics Engineer / Cloud Data Architect |
| Certified LLMOps Engineer (CLOE) | LLM Lifecycle, CI/CD, Model Observability (Arize Phoenix) & Gateways | LLMOps Engineer / AI Platform Engineer |
| Certified Generative AI Engineer (CGAE) | Application Building, Fine-Tuning (PEFT/LoRA), LangChain & RAG | Generative AI Application Developer / ML Engineer |
Who Should Earn the ACDE Credential?
Software & Backend Engineers: Transitioning into specialized high-paying data platform and data infrastructure roles.
Data Analysts & Analytics Engineers: Moving from standard SQL dashboards to distributed Python/Spark pipelines and orchestration.
Database Administrators & SQL Developers: Modernizing legacy database stacks into cloud-native lakehouses and streaming systems.
Cloud & DevOps Engineers: Expanding cloud infrastructure skills into dedicated data platform operations and pipeline automation.
Early-Career Engineers & Graduates: Establishing an industry-validated credential backed by a premier professional AI and data body (ADaSci).
Enrollment & Member Options
- Standard Self-Paced Tuition: $249 USD
- ADaSci Member Price: $124.50 USD (Save $124.50 instantly with General/Premium Membership)
- Includes: 30 Hours of Content, 60-Question Proctored Exam Voucher, Lifetime Credential Validity, and Verifiable LinkedIn Digital Badge.
Enroll in ACDE Today — https://adasci.org/certifications/adasci-certified-data-engineer-acde
What you'll be able to do.
Every outcome maps to a module in the syllabus and a domain in the exam.
- 01
Qualify for in-demand roles such as Data Engineer, Analytics Engineer, Cloud Data Engineer, and Big Data Engineer across industries.
- 02
Build job-ready skills in designing, deploying, and operating scalable data pipelines used in real-world enterprise environments.
- 03
Strengthen career mobility by enabling transitions from data analyst, backend engineer, or cloud engineer roles into data engineering positions.
- 04
Enhance earning potential by validating expertise in high-demand technologies such as Apache Spark, Kafka, Airflow, and cloud data platforms.
- 05
Gain confidence in architecting end-to-end data platforms, improving technical credibility in interviews, design reviews, and production discussions.
- 06
Position yourself for senior and leadership roles by developing strong foundations in data architecture, governance, and platform scalability.
- Data engineering foundations
- Scalable storage & modeling
- Data ingestion & processing
- Cloud data engineering
- Data quality & governance
What the 30 hours cover.
- 5
- modules
- 35
- lessons
- 4.6 h
- of video
- 1 hours
- proctored exam
Share of content hours by module. Expand a row for its lessons.
- Introduction to Data Engineering6 min
- Fundamentals of Database Systems18 min
- ETL vs ELT: Concepts and Design Patterns9 min
- Understanding Batch and Stream Data ProcessingReading
- Introduction to the Big Data Landscape15 min
- Practical Implementation with Apache Hadoop15 min
- Data Flow Scripting with Apache Pig5 min
- Understanding OLTP vs OLAP systems6 min
- Data Warehousing vs Data Lake Architectures10 min
- File Formats for Big DataReading
- Strategies for Data PartitioningReading
- Performance Tuning with Bucketing & ClusteringReading
- Data Modeling Techniques7 min
- SQL for Data Engineering18 min
- Relational Data Management with PostgreSQL10 min
- NoSQL Data Modeling with MongoDB28 min
- Visual Data Exploration with MongoDB Compass9 min
- Getting Started with Building Data Pipelines12 min
- Distributed Computing with MapReduce7 min
- Large-scale Data Processing with Apache Spark17 min
- Event-Driven Data Streaming with Apache Kafka15 min
- Workflow Management and Orchestration with Apache AirflowReading
- Stream Processing with Apache FlinkReading
- Introduction to Cloud-based Data Warehouses9 min
- Leveraging Managed Spark for Scalable Processing18 min
- Designing Cloud-based Data Pipelines with AWS18 min
- Delta Lake Implementation on AWS S314 min
- Serverless Architecture in Data EngineeringReading
- Modern Cloud-native Storage SolutionsReading
- Data Engineering in the Modern StackReading
- Governance Practices for Reliable Data Models10 min
- Data Quality FrameworksReading
- Data Lineage, Monitoring, and Observability ToolsReading
- Ensuring Data Privacy and Regulatory ComplianceReading
- Data Contracts & Schema EvolutionReading
Earned, never granted.
A proctored, multiple-choice exam closes the programme. No attendance certificates, no self-assessment: the credential is issued only on a pass.
- 01Enroll
Instant access to the curriculum, the brochure and the sample exam.
- 02Learn at your pace
Most people finish the 30 hours in four to eight weeks alongside work.
- 03Sit the proctored exam
60 multiple-choice questions in 1 hour, online, with your result on the day.
- 04Claim your credential
A verifiable digital credential and LinkedIn badge, valid for life.
Built for the people doing the work.
- Data Engineers (All Experience Levels)
- Analytics Engineers and BI Professionals
- Backend and Platform Engineers working with data pipelines
- Database Developers and SQL Professionals
- Cloud Engineers transitioning into data engineering roles
- Data Analysts aspiring to build scalable data pipelines
- Big Data and Distributed Systems Practitioners
- Technology Professionals involved in data platform modernization
- Solution Architects designing data-intensive systems
- Graduate Students and Early-Career Professionals pursuing careers in data engineering
- A quiet room, a webcam and a stable connection for the proctored exam
- Time for 30 hours of self-paced study
Not sure you qualify? Ask the team, or start with the free sample exam.
What the letters signal.
This certification provides industry-relevant recognition by validating practical proficiency in modern data engineering tools, architectures, and cloud-native data platforms. It demonstrates the ability to design, build, and operate scalable batch and streaming data pipelines aligned with real-world enterprise standards.
Awarded by the ADaSci, not a course vendor.
Add the credential letters after your name on CVs and profiles.
Employers confirm your credential ID in seconds online.
No renewal fees and no expiry date on your credential.
- Gain comprehensive expertise in modern data engineering architectures, tools, and design patterns across batch, streaming, and cloud-native systems.
- Build strong hands-on capability through practical exposure to industry-standard technologies such as Spark, Kafka, Airflow, and cloud data platforms.
- Demonstrate validated proficiency in designing, building, and operating scalable, reliable, and high-performance data pipelines.
- Enhance career advancement opportunities by aligning skills with in-demand roles in data engineering, analytics engineering, and platform engineering.
- Develop enterprise-ready knowledge in data quality, governance, lineage, and compliance, essential for production-grade data systems.
- Strengthen architectural decision-making skills by mastering trade-offs across storage, processing, and orchestration technologies.
One price. Everything included.
Start today and learn on your own schedule.
- 30 h of on-demand content
- 60-question proctored exam voucher
- Lifetime access and updates
- Verifiable credential and badge
Train and certify a whole team with one plan.
- Volume pricing for five or more seats
- Private cohort and team dashboard
- Live sessions with an instructor
- Invoicing and procurement support
General and Premium members pay less on every credential, course and event.
- Member rates on this credential
- Credential stays active in the registry
- Community, events and the journal
- Upgrade any time
Prices in USD. Exam retakes cost $20. Refunds follow our terms of service. Prices shown in INR when you switch currency.
In their words.
“This vendor-neutral certification goes beyond the hype and dives deep into what's actually shaping the future of AI: autonomous, multi-agent systems that can reason, collaborate, and act.”
“This comprehensive program from ADaSci gave me a solid foundation to become an Agentic AI Engineer/Architect. The curriculum perfectly balances theory with practical, hands-on implementation.”
“Thrilled to share my Certificate of Achievement for completing the "AI for Business Impact Program"! This training has equipped me with practical AI skills to drive efficiency in finance and accounting processes.”
Online and remotely proctored. You need a quiet room, a webcam and a stable connection.
Course content, updates and your credential are yours for life.
Yes. If you don't pass, you can book a retake for a small fee.
Every credential has a unique ID that anyone can check at adasci.org/verify-certificate.
Earn the ACDE.
Enroll today, pass the proctored exam, and carry a verifiable record of it for the rest of your career.