Full Stack Data Engineering Career Path

Dive into the fundamentals of data engineering, exploring data types, data structures, and their practical applications. Learn the principles of working with databases, including relational (RDBMS) and NoSQL, and master the art of querying data using SQL.

• 1.1 Introduction to Data Types and Data Structures:
  • ‍Understanding data types and structures is essential for efficient data storage, retrieval, and manipulation in various applications.
• 1.2 Introduction to Data and its Application:
  • Data drives decision-making in industries like retail, healthcare, and finance, influencing strategies for marketing, operations, and research.
• 1.3 Working with Databases (RDBMS, NoSQL), Data Models, and Schema:
  • Utilized in e-commerce platforms for storing customer data and transaction records, facilitating personalized recommendations and sales analysis.
• 1.4 Querying Data with SQL:
  • SQL is used in financial institutions for analyzing transaction data and generating reports on account balances, fraud detection, and regulatory compliance.

Delve into the intricacies of building ETL pipelines using PySpark, enabling you to ingest, process, and transform large-scale datasets efficiently. Explore data ingestion techniques with Sqoop and build streaming data pipelines using PySpark and NoSQL databases.

• 1.1 Building ETL Pipeline with PySpark:
  • ‍PySpark ETL pipelines are employed in e-commerce companies for processing and analyzing large volumes of sales data to optimize inventory management and supply chain operations

• 1.2 Data Ingestion (Sqoop):
  • Sqoop is utilized in healthcare systems for transferring patient data from on-premise databases to cloud-based platforms for analysis and research.
• 1.3 Building Streaming Data Pipeline (PySpark and NoSQL):
  • PySpark streaming pipelines are deployed in social media platforms for real-time analysis of user interactions, enabling targeted advertising and content recommendation.
• 1.4 Processing Large Data Sets:
  • Used in transportation networks for analyzing traffic patterns and optimizing route planning for delivery vehicles.
• 1.5 Data Visualization:
  • Employed in marketing agencies for creating interactive dashboards to visualize campaign performance metrics and customer engagement data.‍

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Unlock the power of deploying big data solutions on cloud platforms, understanding cloud application architecture and scalability considerations. Learn how to deploy web services within and outside a cloud architecture, ensuring seamless integration and performance optimization.

• 1.1 Deploying Big Data Solutions on Cloud:
  • ‍Cloud-based big data solutions are deployed in manufacturing industries for monitoring and optimizing production processes, reducing downtime, and improving efficiency.
• 1.2 Cloud Application Architecture:
  • Implemented in financial institutions for developing secure and scalable banking applications, facilitating online transactions and customer account management.
• 1.3 Deploying a Web Service from Inside and Outside a Cloud Architecture:
  • Utilized in e-learning platforms for deploying web services to deliver educational content and track student progress.
• 1.4 Data Scalability & Cloud Services:
  • Scalable cloud services are utilized in telecommunications companies for processing and analyzing vast amounts of network data to optimize network performance and customer experience.

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Gain insights into retrieving, integrating, and processing data on cloud platforms, exploring fundamental concepts of data management and mining. Dive deep into Apache Hive for querying and processing large datasets, and automate data processing workflows with Oozie and Zookeeper.

• 1.1 Fundamentals of Data on Cloud:
  • ‍Cloud-based data services are employed in retail chains for centralized inventory management and sales analytics across multiple store locations.
• 1.2 Retrieval and Integration:
  • Used in logistics companies for integrating shipment tracking data from multiple carriers and warehouses to provide real-time visibility to customers.
• 1.3 Mining and Processing Data:
  • Data mining techniques are applied in healthcare organizations for analyzing patient records to identify disease patterns and improve diagnosis and treatment protocols.

Master the fundamentals of data warehousing operations, including ETL operations, data storage, and querying with Hive. Explore advanced data transfer techniques using Sqoop and Flume, enabling efficient data movement across disparate systems.

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Understand the critical aspects of data security and governance, including enterprise security, infrastructure security, and compliance mechanisms. Learn how to design robust security architectures and implement vulnerability assessment and penetration testing (VA & PT) mechanisms

• 1.1 Data Security and Privacy:
  • ‍Data security measures are implemented in government agencies for protecting sensitive citizen information stored in databases and preventing unauthorized access.
• 1.2 Enterprise Security:
  • Enterprise security practices are employed in banking institutions for securing customer financial data and preventing cyber-attacks and fraud.
• 1.3 Infrastructure Security:
  • Infrastructure security protocols are implemented in telecommunications companies for securing network infrastructure and preventing data breaches and service disruptions.
• 1.4 Network, OS, Database & Mobile Security:
  • ‍Network, OS, database, and mobile security measures are implemented in technology companies for protecting corporate data and intellectual property from cyber threats and data leaks.
• 1.5 Security Architecture and VA & PT Mechanism:
  • Security architecture and vulnerability assessment mechanisms are employed in defense organizations for securing military networks and systems from cyber threats and attacks.

Dive into the world of distributed data processing with PySpark, exploring SparkContext, SparkSession, and DataFrame operations. Learn advanced techniques for optimizing performance, working with streaming data, and deploying scalable machine learning models.

• 1.1 Spark Context and SparkSession:
  • ‍Understand the foundational components of PySpark for distributed data processing and management.
• 1.2 Data Loading and Storage:
  • Learn methods for loading data into PySpark and storing it in various formats for efficient processing.
• 1.3 DataFrame Operations and Transformations:
  • Dive into DataFrame operations and transformations to manipulate and prepare data for analysis and modeling.
• 1.4 Optimizing Performance:
  • ‍Explore techniques for optimizing PySpark performance, including caching, partitioning, and leveraging cluster resources effectively.
• 1.5 Working with Streaming Data:
  • Gain insights into processing real-time streaming data with PySpark, enabling timely analysis and decision-making.
• 1.6 Machine Learning with PySpark:
  • Harness the power of PySpark for machine learning tasks, including model training, evaluation, and deployment.
• 1.7 Deployment and Scalability:
  • Learn strategies for deploying PySpark applications in production environments and scaling them to handle large volumes of data efficiently.

Explore Kafka fundamentals, including setup, configuration, and producing/consuming data streams. Discover stream processing with Kafka Streams, ensuring fault tolerance, scalability, and compliance with security standards.

• 1.1 Setup and Configuration:
  • ‍Establish and configure Kafka clusters to facilitate real-time data processing and messaging in enterprise environments.
• 1.2 Producing and Consuming Data:
  •  Implement data producers and consumers to ingest and distribute streaming data across distributed Kafka topics for real-time analytics.
• 1.3 Stream Processing with Kafka Streams:
  • Utilize Kafka Streams for real-time data processing, enabling applications such as fraud detection, monitoring, and anomaly detection.
• 1.4 Fault Tolerance and Scalability:
  • ‍Ensure fault tolerance and scalability in data processing pipelines by leveraging Kafka's distributed architecture and replication mechanisms.
• 1.5 Monitoring and Operations:
  • Monitor Kafka clusters and data pipelines to ensure smooth operation, detect performance bottlenecks, and maintain high availability.
• 1.6 Security and Compliance:
  • Implement security measures and compliance mechanisms to protect sensitive data and ensure regulatory compliance in Kafka deployments, particularly in industries such as finance and healthcare.