ICT & Technologies

The Big Data Technologies training provides an in-depth understanding of how organizations efficiently manage, process, and analyze massive datasets. The course starts with a foundational overview of big data, exploring its characteristics through the 5Vs (Volume, Velocity, Variety, Veracity, and Value).

Participants will then delve into essential big data technologies, including the Hadoop ecosystem, distributed storage, batch and real-time data processing, and advanced analytics.

The training's key focus is the Ingest-Process-Serve complete workflow, which covers tools and techniques for data ingestion, transformation, and serving in large-scale environments.

The training combines theory, hands-on labs, and real-world projects to equip participants with practical skills for effectively managing and leveraging big data.

• Understand big data fundamentals and its impact on modern businesses.
• Define and apply the 5Vs of Big Data (Volume, Velocity, Variety, Veracity, and Value).
• Explore the Hadoop ecosystem: HDFS, YARN, MapReduce, Hive, Spark, and more.
• Implement the Ingest-Process-Serve workflow for managing big data.
• Work with data ingestion tools like Apache Kafka, Flume, and Sqoop.
• Process and analyze big data using batch processing (MapReduce, Spark) and real-time streaming (Flink, Spark Streaming).
• Store and manage large datasets with HBase, Cassandra, and NoSQL databases.
• Develop and optimize big data pipelines for scalable analytics.
• Integrate machine learning and AI with big data for advanced insights.
• Work on real-world projects, applying big data concepts to industry-specific problems.

Day 1:
- The 5Vs and their impact on big data strategies.  

- Big Data architectures and use cases.

Day 2:
- Overview of HDFS and YARN.

- Introduction to MapReduce, Hive, and Spark.

Day 3:
- Data ingestion: Using Kafka, Flume, and Sqoop for real-time and batch processing.  

- Data processing: ETL pipelines with Spark and Flink.  

- Data serving: Storing and retrieving data with HBase and Cassandra.

Day 4:
- Batch vs. real-time data processing

- Streaming data processing with Apache Flink and Spark Streaming

- Optimizing big data workflows with Apache NiFi and Airflow

Day 5:

- Applying big data concepts in real-world projects.

- Optimizing big data application performance.

- Discussing trends, AI integration, and cloud solutions in big data.

-  Introduction to Big Data and its role in modern industries.

- The 5Vs of Big Data: Understanding data characteristics and challenges.

- The Hadoop ecosystem: Components, architecture, and practical applications.

- The Ingest-Store-Train-Serve-Intelligence workflow is used to handle large-scale data efficiently.

- Batch processing with MapReduce and Spark, and real-time streaming with Flink.

- Data ingestion and storage using Kafka, Flume, HBase, and NoSQL databases.

- Building scalable data pipelines for analytics and AI integration.

- Industry use cases and hands-on projects for practical experience.

- Big Data trends: Future AI, cloud computing, and machine learning advancements.

This four-day intensive workshop is designed to provide participants with a solid foundation in Data Management.

It will focus on the principles and practices necessary for effectively organizing, maintaining, and securing data across various platforms and environments.

Participants will learn about data governance, quality control, metadata management, and data utilization in compliance with regulations and business requirements. 

• Understand the fundamentals of data governance and data stewardship.
• Learn techniques for data quality assurance, data cleaning, and maintenance.
• Gain insights into managing metadata and setting up effective data catalogs.
• Develop strategies for data privacy and security that are in line with regulations.
• Explore tools and techniques for data integration, warehousing, and lifecycle management.
• Build skills in creating and enforcing data policies and procedures that support business objectives. 

Day 1:

- Introduction to Data Management and Governance

Day 2: 

- Data Quality Control and Metadata Management.

Day 3:

- Data Security, Privacy, and Compliance.

Day 4:

- Data Integration and Warehousing; Capstone Project Planning.

- Structured lectures with detailed course materials tailored to real-world applications.

- Hands-on labs using leading data management tools and software.

- Group activities to enhance learning through practical challenges.

- In-depth discussions on Data policies, Ethics, and Compliance issues.

- Real-world case studies illustrating data management challenges and solutions.

- A final project that applies learned concepts to a practical scenario, reinforcing the workshop’s teachings. 

Data Engineering is crucial for the management and analysis of big data, supporting advanced analytics applications across various industries.

This intensive 4-day workshop delves into the core aspects of data engineering, covering data architecture, ingestion, and storage on major big data platforms like Hadoop and Spark.

Participants will engage with cloud technologies for big data, learning practical deployment techniques on both AWS and Azure platforms.

• Gain a foundational understanding of big data platforms' architecture and components.
• Acquire practical skills with data ingestion tools such as Kafka and Sqoop.
• Explore and implement large-scale storage solutions including HDFS, HBase, and various NoSQL databases.
• Develop competency in managing and analyzing data with Spark and Hadoop.
• Implement secure, scalable data solutions in diverse cloud environments.
• Address key data security, compliance, and governance aspects within big data engineering. 

Day 1:

- Introduction to Big Data Platforms

- Cloud computing technologies

Day 2: 

- Data Management and Analysis with Hadoop and Spark.

Day 3:

- Security, Compliance, and Capstone Project Design.

• Targeted, concise lectures complemented by comprehensive digital resources.
• Hands-on labs and practical exercises using industry-standard tools and platforms.
• Collaborative group projects designed to simulate real-world problem-solving.
• Focused discussions on deploying and securing data solutions in cloud environments.
• Case studies that highlight practical challenges and solutions in data engineering.
• A capstone project that integrates the skills learned, fostering a practical understanding of course materials. 

Data is the lifeblood of the modern web. This workshop will equip you with the full-stack skills needed to collect, analyze, and present data in engaging and insightful ways for users. 

We will explore the entire web development process, covering everything from front-end design and user experience (UX) principles to back-end data handling and visualization. 

Participants will gain a comprehensive understanding of user experience principles and best practices for designing intuitive and engaging web applications. Additionally, you will develop a strong understanding of data security and privacy best practices for web applications.

• Build robust, data-driven web applications f.
• Master front-end technologies like HTML, CSS, and JavaScript (including libraries like React or Vue.js) for creating interactive and user-friendly interfaces.
• Develop back-end APIs using languages like Python (with frameworks like Django or Flask) or Node.js to handle data storage, retrieval, and processing.
• Learn to design and implement effective database structures (e.g., SQL, NoSQL) to efficiently store and manage large datasets.
• Explore data visualization libraries and techniques to effectively communicate insights through interactive charts, graphs, and dashboards.

Day 1:
- HTML, CSS, JavaScript basics
- React/Vue.js

Day 2:
- Python/Node.js

- API design, database interactions (MongoDB).

Day 3:
- Building a Data-Driven Application
- UI and UX Principles: Wireframing, prototyping, usability testing

Day 4:
- Data Visualization and Interactive Dashboards
- Deployment and Hosting

Day 5:
- Security, scalability, performance optimization, and emerging trends

- Hands-on Projects: Building real-world web applications with a focus on data integration and visualization
- Industry Best Practices: Adhering to industry standards and best practices for web development
- Personalized Feedback: Expert guidance and feedback on individual projects
- Comprehensive Learning Materials: Access to course materials, code - examples, and online resources
- Community Building: Networking opportunities with fellow learners and industry professionals 

 The Internet of Things (IoT) is transforming industries by connecting devices, collecting data, and enabling intelligent decision-making.

This workshop provides a comprehensive introduction to IoT fundamentals, covering platform design, constraints, protocols, and essential components that drive IoT solutions.

Participants will gain hands-on experience with real-world applications, exploring how IoT can optimize processes, enhance efficiency, and create new business opportunities. 

• Understand the core principles and architecture of IoT systems.
• Identify key constraints and challenges in IoT deployment.
• Learn about IoT communication protocols and data exchange mechanisms.
• Explore various IoT platforms and their integration with cloud services.
• Gain insights into IoT security and privacy considerations.
• Develop practical skills in designing and implementing IoT solutions.

Day 1:
- IoT overview and main concepts 

Day 2:
- Main architecture concepts 

Day 3:
- Design Constraints

Day 4:
- Standards and protocols 

Day 5:
- Advanced IoT concepts 

- Introduction to IoT concepts, architecture, and components.

- Key considerations for building scalable and designing an efficient IoT platforms.

-  MQTT, CoAP, HTTP, and other essential IoT communication protocols.

- Power consumption, network limitations, and device constraints.

- Processing data efficiently at the edge and in the cloud.

- Addressing cybersecurity threats and data protection in IoT.

- Real-world use cases across healthcare, smart cities, manufacturing, and more. 

In an era of increasing cyber threats, organizations must stay ahead with robust security strategies. This cybersecurity training provides a deep dive into essential security principles, focusing on real-world threats and defense mechanisms. Participants will explore reconnaissance techniques, threat modeling, securing web servers, managing permissions, and debunking common cybersecurity myths. Additionally, the workshop will introduce the power of machine learning and AI in cybersecurity, equipping attendees with cutting-edge tools to detect, prevent, and respond to cyberattacks. Whether you are an IT professional, security analyst, or business leader, this training will enhance your ability to safeguard digital assets and mitigate risks effectively. 

• Explore Security - Attack anatomy, cycles, and types
• Protect data against the latest cybersecurity threats.
• Strengthen environment with robust security frameworks.
• Detect and investigate malicious behavior in the BI system and environment. 
• Evaluate data security, analyze potential risks, and remediate. 
• Simulate real-case attacks according to the latest penetration testing methodologies. 
• Gain insights into the role of ML and AI in modern cybersecurity.

Day 1:

- Cybersecurity fundamentals & evolving threats

- Reconnaissance techniques & threat modeling

Day 2: 

- Role-Based vs. Attribute-Based Access Control

- Privilege escalation & cybersecurity myths

Day 3: AI in Cybersecurity & Threat Mitigation

- AI-driven threat detection & fraud prevention

- ML techniques for anomaly & malware analysis

- Reconnaissance & Threat Modeling: Identifying potential threats and assessing vulnerabilities.

- Web Server Security: Best practices for securing Apache, Nginx, and other web servers.

- Permissions & Common Myths: Understanding access control, privilege escalation, and security misconceptions.

- ML & AI in Cybersecurity: How machine learning and AI enhance threat detection and prevention.

- Real-World Case Studies: Examining cybersecurity incidents and mitigation strategies.