What will you learn in Big Data Integration and Processing Course
Retrieve and query data from relational (PostgreSQL) and NoSQL (MongoDB, Aerospike) databases.
Learn data aggregation, manipulation, and analysis using Pandas and data frames.
Explore big data integration tools like Splunk and Datameer for practical insights.
Execute big data processing tasks on Hadoop and Spark platforms.
Understand when data integration is necessary in large-scale analytical applications.
Gain foundational knowledge for handling, managing, and processing large datasets efficiently.
Program Overview
Module 1: Welcome
⏳ 1 hour
Introduction to big data integration and processing concepts.
Installing Docker, working with Jupyter notebooks, and setting up hands-on materials.
3 videos, 5 readings, 1 discussion prompt.
Module 2: Retrieving Big Data (Part 1)
⏳ 1 hour
Covers relational data retrieval and querying using PostgreSQL.
5 videos, 2 readings.
Module 3: Retrieving Big Data (Part 2)
⏳ 2 hours
Explore NoSQL data retrieval, aggregation, and Pandas data frames.
Hands-on assignments with MongoDB, Aerospike, and Pandas.
5 videos, 3 readings, 2 assignments, 1 discussion prompt.
Module 4: Big Data Integration
⏳ 2 hours
Introduction to data integration using Splunk and Datameer.
Practical examples of information integration processes.
11 videos, 4 readings, 2 assignments, 1 discussion prompt.
Modules 5–7
⏳ 2–3 hours each
Focus on advanced big data processing patterns and hands-on exercises with Hadoop and Spark.
Integrate data retrieval, aggregation, and analysis skills in real-world scenarios.
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Job Outlook
Prepares learners for roles such as Big Data Analyst, Data Engineer, and Business Intelligence Specialist.
Skills applicable across tech, finance, healthcare, retail, and e-commerce industries.
Knowledge of big data integration and processing improves employability in data-driven companies.
Provides practical experience with industry-standard tools and platforms.
Specification: Big Data Integration and Processing
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