Data Management & Engineering
Accredited by Averest
350 Learners
INTERMEDIATE
Data Management & Engineering training program is designed for data professionals with a foundational understanding of data management and engineering concepts. It delves deeper into critical aspects of building and maintaining robust data pipelines, ensuring data quality, and optimizing data infrastructure for advanced analytics.

Accreditation With .
Data Management & Engineering Overview
This intensive program equips you to tackle the entire data management lifecycle, from designing and implementing scalable data pipelines for diverse sources to mastering advanced data wrangling techniques and data quality practices. You'll delve into distributed data processing frameworks like Apache Spark, explore data warehousing and data lake solutions for efficient storage, and gain a firm grasp of data security best practices and data governance frameworks.
What You Will Learn ?
- Learn data security best practices and data governance frameworks to safeguard sensitive information.
- Design and implement data warehousing and data lake solutions for efficient and scalable data storage.
- Distributed processing frameworks to efficiently handle big data.
- Employ advanced techniques to clean, transform, and validate data.
- Build robust data pipelines
Course Key Features
- Hands-on Development
- Actionable Learning
- In-depth Understanding
- Comprehensive Curriculum
Training Options
In Class
- 5-days in-class training
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- All related Averest's quality control tools and required stationary
- 5 or 4 stars training venue
- Pay later by invoice -OR- at the time of checkout by credit card
- Continuous learner assistance and support
Online
- 5-day instructor-led training course
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- All related Averest's quality control tools
- Pay later by invoice -OR- at the time of checkout by credit card
- Continuous learner assistance and support
Corporate Training
- A highly customized Corporate Training service designed exclusively for corporate employees and teams. Our training programs are meticulously planned and executed to fill knowledge and experience gaps, helping organizations achieve their business goals. With a comprehensive assessment and tailored curriculum, our experienced trainers deliver modules in areas of accreditation requirements as well as complementary practices such as leadership, communication, and technology adoption. Official certification exam voucher is provided upon completion, ensuring professional growth and measurable results. Contact us now to partner with Averest Training in order to bridge the gaps in your workforce and unlock the full potential of your team.
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Data Management & Engineering Training Cirriculum
Eligibility .
To ensure a successful learning experience, participants should possess a basic understanding of SQL, Data Structures & Analysis Concepts, Experience with Python (recommended).
Pre-requisites .
This intermediate-level training is designed for data professionals with a solid foundation in data management and engineering concepts.
Data Management & Engineering Course Content .
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Module 1 Building Scalable Data Pipelines.- Introduction to data pipelines
- ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform) processes
- Data orchestration tools (Airflow, Luigi)
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Module 2 Advanced Data Wrangling and Quality Management.- Data cleaning techniques (handling missing values, outliers, normalization)
- Data profiling
- Data validation rules
- Data quality metrics
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Module 3 Apache Spark for Distributed Data Processing.- Introduction to Apache Spark architecture (RDDs, DataFrames, Spark SQL)
- Distributed processing concepts
- Fault tolerance, working with big data
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Module 4 Data Warehousing & Data Lakes.- Data warehouse design principles (star schema, snowflake schema)
- Dimensional modeling
- Data lake architecture, data ingestion strategies
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Module 5 Data Security & Governance.- Data security best practices (access control, encryption)
- Data lineage, data governance frameworks (HIPAA, GDPR)
- Data privacy regulations
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