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About This Program
The high demand of data engineering, both locally and internationally, has prompted atomcamp to launch its data engineering bootcamp. This Bootcamp is project and offers hands-on exercises in tools like Hadoop, Apache Kafka, and AWS. In our 8-week Data Engineering bootcamp, you’ll go from mastering the basics like data pipelines and ETL, to specialized skills in Big Data and cloud computing. Each week tackles a critical aspect of data engineering.The course culminates in a capstone project, ensuring you’re fully equipped to launch a global career in creating and managing sophisticated data architectures.
Complete our Bootcamp and Become Job Ready
We help our successful graduates find Jobs and Internships, and also provide guidance in discovering Freelance opportunities.
What You’ll Learn!
Proficiency in Data Pipelines and ETL
Gain a solid understanding of how to build and manage efficient data pipelines, including Extract, Transform, Load (ETL) processes essential for data flow.
Big Data and Distributed Computing Skills
Acquire hands-on experience with Big Data technologies like Hadoop and NoSQL databases, preparing you for large-scale data engineering projects.
Mastery in Data Warehousing and Real-Time Analytics
Learn the architectural and operational nuances of data warehousing technologies and become proficient in real-time data analytics using tools like Apache Kafka.
Cloud-Based Data Engineering
Get well-versed in using cloud platforms like AWS, Azure, or GCP for scalable and efficient data storage and processing solutions.
End-to-End Project Execution
Complete a capstone project that integrates all the core data engineering components, validating your ability to build a comprehensive data architecture from scratch.
Exclusive Guest Session with
Shafiqa Iqbal
Get the chance to attend Shafiqa Iqbal’s exclusive master class. Learn how to transform raw data into insights from a Google Big Data Engineer herself.
Is This Program a Good Fit for You?
This bootcamp in data engineering is tailored for individuals with some understanding or professional experience in data-related or software engineering roles, aiming to transition into data engineering.
Prerequisites
- Professional experience in analytical roles using SQL or in software engineering with Python, Java, or C++
- Alternatively, a bachelor's degree in Computer Science or a related field involving extensive programming is acceptable.
- Must have proficiency in SQL and basic Python skills.
Technical Prerequisites
- Stable Internet connection
Key Features
Practical Hands-on Training
We prioritize experiential learning. Over 12 weeks, you will undertake a series of real-world projects and case studies to reinforce your theoretical understanding.
Industry Exposure
Learn from the experts. Our course features guest lectures from leading data engineering professionals, providing insights into industry trends and best practices
Internships And Job Placement
We understand the value of real-world experience. To help you transition from classroom to the industry, we offer internships and job placements with our esteemed partners upon course completion.
Guidance & Support
Receive guidance and assistance in your pursuit of data science career opportunities. Skilled teaching assistants are there for you.
Speaker Sessions
Regular speaker series with industry professionals and hiring manager
1-on-1 Coaching & Mentorship
Receive individualized mentorship to navigate your way through your data science career.
Course Content
- Introduction to Big Data, challenges, and the role of data engineering.
Overview of the Hadoop ecosystem. - HDFS: Architecture, data flow, and commands.
- YARN: Concepts, architecture, and resource management.
- Using Ambari for Hadoop cluster management.
- Introduction to data sourcing tools with a focus on Apache NiFi.
- Basics of Pig for data analysis.
- Exploring MapReduce programming model.
- Advanced Hive usage for data warehousing.
- Understanding and implementing TEZ for improved performance.
- Introduction to NoSQL databases: HBase and basics of Cassandra.
- Working with MongoDB for document databases.
- Data ingestion with Sqoop and Flume.
- SQL querying on Hadoop using Drill and Phoenix.
- Interactive querying with Presto.
- Overview of workflow scheduling with Oozie.
- Introduction to Kafka for real-time data pipelines.
- Real-time stream processing with Spark Streaming and Flink.
- Building streaming applications: A hands-on project.
- Exploring Data Bricks Cloud: Architecture, integration, and collaborative data science.
- Data visualization and interactive analytics with Zeppelin.
- Integrating MySQL with big data technologies for structured data storage.
Participants will propose and work on capstone projects incorporating multiple tools from the curriculum.
Learning Objectives:
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Foundations of Data Engineering: Participants will gain knowledge of the role of data engineering and will be able to differentiate between data pipelines, ETL processes, and their significance.
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Data Technologies and Tools: Participants will be able to navigate big data technologies, including Hadoop ecosystem components, and implement data integration, transformation, and extraction techniques.
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Data Warehousing and Architecture: Participants will be able to comprehend data warehousing architecture and develop data models.They will gain practical experience with data warehouse technologies and dimensional modeling.
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Real-time Data Processing and Cloud Computing: Participants will explore real-time data streaming and event-driven architectures and utilize cloud computing platforms for storage, processing, and analytics.They will implement data access controls and security measures.
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Workflow Automation and Impactful Insights: Participants will employ workflow orchestration tools for data pipeline automation, create impactful data visualizations using industry-standard tools and optimize performance and scalability of data processing pipelines.
Trainer
Syed Afroz Pasha
Afroz is the head Of Data Governance at Alibaba Group. He is responsible for Strategizing/Optimizing Engineering & Visuals. He has mastered the skills of Extract, Transform, Load (ETL) as well as Data Warehousing techniques in the course of his profession.