Data 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 to illustrate 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 and 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. 

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