Big Data Technologies in the Cloud Training
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedBig Data Technologies in the Cloud Training
The Big Data Technologies in the Cloud training is an intensive, practical workshop designed for engineers, system administrators, and IT professionals who want to learn how to effectively build and manage modern Big Data infrastructure in cloud environments (AWS, Azure, Google Cloud). Participants will become familiar with key technologies, tools, and architectural patterns used for storing, processing, and analyzing large volumes of data in the cloud. The course combines up-to-date theoretical knowledge with a dominant practical component based on real project scenarios.
What You Will Learn
- You will learn the architectures and capabilities of major Big Data and cloud technologies
- You will master effective techniques for storing, securing, and processing large datasets using tools like S3, Hadoop, Spark, and NoSQL
- You will gain skills in automating data integration and analysis using dedicated cloud services
- You will acquire practical experience in designing, deploying, and optimizing Big Data solutions in cloud environments
Who is this training for?
Software engineers and system administrators implementing or maintaining Big Data solutions in the cloud-
Data analysts and Data Science professionals who want to increase their competencies in data processing and analysis
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Individuals planning to migrate existing systems or deploy new projects based on Big Data and public or hybrid cloud environments
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IT solution architects who want to implement modern, scalable data platforms
Training Program
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Day 1: Big Data Fundamentals in the Cloud
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Module 1: Basics of Big Data and Cloud
- Introduction to the Big Data concept and the 5Vs (volume, velocity, variety, veracity, value)
- Overview of cloud types: IaaS, PaaS, SaaS
- Main cloud service providers
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Fundamental AWS services
- Compute: EC2, Lambda
- Storage: S3, EBS, Glacier
- Networking: VPC, Internet Gateways
- Monitoring: CloudWatch, CloudTrail
- Security and identity management: IAM
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Module 2: Data Lake Architecture and Data Storage
- Building a Data Lake in the cloud
- Amazon S3, Azure Blob Storage, Google Cloud Storage
- Managing permissions, data versioning, and data security
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Day 2: Data Processing and Analysis
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Module 3: Processing Large Data Sets
- Distributed file systems in the cloud (HDFS, S3 integration)
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Core processing engines
- Hadoop
- Spark (AWS EMR, Azure Databricks)
- MapReduce
- YARN
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Module 4: NoSQL Databases and Data Warehouses
- NoSQL technologies: HBase, Cassandra, MongoDB in the cloud
- Data warehouses: Amazon Redshift, Google BigQuery, Azure Synapse Analytics
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Day 3: Advanced Technologies and Case Studies
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Module 5: Data Integration, Orchestration, and Automation
- Data integration services: AWS Glue, Azure Data Factory, Google Dataflow
- Workflow orchestration and automation: Oozie, AWS Step Functions
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Module 6: Analysis, Visualization, and Security
- Cloud data analysis: Athena, BigQuery, Spark SQL
- Data visualization: Jupyter Notebook, Zeppelin, BI tools
- Security: secret storage, access auditing, compliance (IAM, Azure Key Vault)
