📚 Silabus Materi 7 Pertemuan :

Meeting 1: Introduction to Data Engineering ✨

• Overview of Data Engineering: Roles, Responsibilities, and Importance.

• Data Lifecycle: Ingestion, Storage, Processing, and Visualization.

• Tools and Technologies: Introduction to Popular Tools (e.g., SQL, NoSQL, ETL Tools, Hadoop, Spark, Kafka).

Meeting 2: Data Ingestion and Storage✨

• Data Ingestion Techniques: Batch vs. Real-Time Ingestion.

• Introduction to Databases: SQL vs. NoSQL, Key Concepts, and Differences.

• Hands-On Activity: Setting Up a Simple Database and Ingesting Data.

Meeting 3: Data Processing with SQL ✨

• Advanced SQL Queries: Joins, Aggregations, Subqueries.

• Data Transformation: Techniques for Transforming Raw Data into Usable Formats.

• Hands-On Activity: Writing and Executing Complex SQL Queries.

Meeting 4: Big Data Technologies ✨

• Introduction to Hadoop: HDFS, MapReduce.

• Introduction to Spark: Core Concepts, RDDs, DataFrames.

• Hands-On Activity: Running Simple Spark Jobs on a Local Cluster.

Meeting 5: Data Pipelines and ETL Processes ✨

• ETL Concepts: Extract, Transform, Load Processes.

• Building Data Pipelines: Tools (e.g., Apache Airflow, Luigi).

• Hands-On Activity: Creating a Basic ETL Pipeline Using a Selected Tool.

Meeting 6: Real-Time Data Processing ✨

• Introduction to Kafka: Core Concepts, Producers, Consumers.

• Streaming Data Processing: Using Tools Like Apache Flink or Spark Streaming.

• Hands-On Activity: Setting Up a Kafka Producer and Consumer, Processing Real-Time Data.

Meeting 7: Data Visualization and Reporting ✨

• Data Visualization Tools: Tableau, Power BI, or Open-Source Tools.

• Creating Dashboards: Best Practices for Effective Data Visualization.

• Hands-On Activity: Building a Simple Dashboard Using a Selected Tool.