Course Content
Prerequisites for a Data Engineering
Preparing for a Data Engineering boot-camp can enhance your experience and success. Here are the core prerequisites:
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Preparing for a Data Engineering boot-camp
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Roadmap to become Data Engineer
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How is Data Engineer different from other Roles
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The Difference
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Data engineer and Data architect same or different???
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Data Ingestion, Storage & Processing
Introduction to Data Engineering Overview of Data Engineering in modern architectures. Data lifecycle and pipelines. Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming). Activity: Discuss a real-world data pipeline use case.
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Overview of Data Engineering in modern architectures.
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Data lifecycle and pipelines
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Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming).
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Activity: Discuss a real-world data pipeline use case.
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Assignment 1
Data Ingestion Techniques
Understanding structured, semi-structured, and unstructured data. Batch ingestion: Using Apache Sqoop, Talend. Streaming ingestion: Using Apache Kafka.
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Understanding structured, semi-structured, and unstructured data.
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Batch vs Stream
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Batch ingestion: Using Apache Sqoop, Talend.
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Streaming ingestion: Using Apache Kafka.
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Activity: Set up a simple data ingestion pipeline using Kafka.
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Data Storage Solutions
Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra). Cloud-based data storage (AWS S3, Azure Blob Storage). Choosing the right storage based on use cases.
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Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra).
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Cloud-based data storage (AWS S3, Azure Blob Storage).
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Choosing the right storage based on use cases.
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Activity: Hands-On Setup for Storing Ingested Data Using AWS S3 or Azure Blob Storage
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Batch Processing with Apache Spark
Understanding Spark architecture. Loading and transforming data using Spark. Difference between RDDs, DataFrames, and Datasets. Activity: Run a sample batch processing job using Spark on a dataset.
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Understanding Spark Architecture
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Loading and Transforming Data Using Apache Spark
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Difference between RDDs, DataFrames, and Datasets.
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Activity: Run a sample batch processing job using Spark on a dataset.
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Data Transformation, Orchestration & Monitoring
Data Transformation & ETL Tools Understanding ETL vs ELT. Using ETL tools: Talend, Apache Nifi, or Airflow. Data cleansing and transformation concepts. Activity: Create a data pipeline with Talend/Airflow for a simple ETL process.
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Understanding ETL vs ELT.
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Using ETL tools: Talend, Apache Nifi, or Airflow.
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Data cleansing and transformation concepts.
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Create a data pipeline with Talend/Airflow for a simple ETL process.
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Data Orchestration
Introduction to orchestration tools: Apache Airflow, AWS Step Functions. Creating workflows to manage complex pipelines. Managing dependencies and retries in workflows.
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Data orchestration process and benifits
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Data Engineering
About Lesson

Choosing the right cloud storage solution—whether AWS S3 or Azure Blob Storage—depends on the specific use case and the needs of your data workflows. Below, I’ll break down common use cases and provide guidance on selecting the best storage solution for each.

1. Data Lake for Analytics

Use Case:

A data lake stores raw, structured, semi-structured, and unstructured data, which can be analyzed using big data tools. The storage must scale, provide cost-effective tiering, and integrate well with analytics and machine learning tools.

  • Best Choice:
    AWS S3 is often a top choice for data lakes due to its native integration with analytics and machine learning services such as Amazon Redshift, Athena, AWS Glue, and EMR. Additionally, S3’s Intelligent-Tiering storage class makes it easier to manage cost for infrequently accessed data without manual intervention.

    Azure Blob Storage is also a strong option, especially if you’re leveraging Azure’s analytics services like Azure Synapse Analytics and Databricks. Azure Blob integrates well with Azure Data Lake for big data analytics.

  • Recommendation:

    • Use AWS S3 if you’re already in the AWS ecosystem and need a highly scalable, cost-efficient data lake with seamless integration into AWS analytics and AI/ML services.
    • Use Azure Blob Storage if your data analytics workflows are built around Azure Synapse, Databricks, or Azure ML.

2. Backup and Disaster Recovery

Use Case:

Long-term backup storage with redundancy and availability for data recovery in case of hardware or system failures. Key considerations include durability, redundancy options, and cost.

  • Best Choice:
    Azure Blob Storage with Geo-Redundant Storage (GRS) or Read-Access Geo-Redundant Storage (RA-GRS) provides robust options for disaster recovery, ensuring that data is replicated across geographically distributed data centers. S3 Glacier and S3 Glacier Deep Archive in AWS S3 are ideal for long-term, cost-efficient archival storage with occasional access needs.

  • Recommendation:

    • Use Azure Blob Storage if you require geo-redundancy for disaster recovery with faster access to replicas in secondary regions (RA-GRS).
    • Use AWS S3 Glacier if you’re focused on long-term archival storage and cost-efficiency, but don’t need frequent access to the data.

3. Web and Mobile Applications

Use Case:

Storing static assets (images, videos, HTML, CSS, JavaScript) for web or mobile applications. The solution should provide fast content delivery and scalability for global access.

  • Best Choice:
    AWS S3 supports hosting static websites and integrates well with Amazon CloudFront for fast content delivery globally. Similarly, Azure Blob Storage integrates with Azure CDN to deliver static content efficiently around the world.

  • Recommendation:

    • Use AWS S3 for static websites or static file storage if you need tight integration with CloudFront or are already working in the AWS ecosystem.
    • Use Azure Blob Storage if you’re building applications in the Azure environment, especially with integration into Azure CDN.

4. Media Content Delivery (Video, Images)

Use Case:

Storing and delivering large media files like videos, images, or audio streams for applications such as video streaming, file sharing, or multimedia distribution.

  • Best Choice:
    Azure Blob Storage is optimized for media content delivery and integrates seamlessly with Azure Media Services for encoding, packaging, and streaming. AWS S3 also supports media delivery with Amazon CloudFront and Elastic Transcoder for media workflows.

  • Recommendation:

    • Use Azure Blob Storage if you need powerful media management services like Azure Media Services for video processing, or you’re delivering large multimedia content within Azure’s ecosystem.
    • Use AWS S3 if you’re already leveraging CloudFront and Elastic Transcoder or if your media streaming architecture is AWS-based.

5. Machine Learning and Artificial Intelligence Workloads

Use Case:

Storing and retrieving large datasets for machine learning and AI workloads, requiring integration with data science tools, fast retrieval times, and scalability.

  • Best Choice:
    Azure Blob Storage works well with Azure ML and Databricks, making it ideal for managing datasets in the context of machine learning workflows. AWS S3 integrates tightly with Amazon SageMaker, Athena, and other AI/ML tools, providing a complete solution for data storage and AI model development.

  • Recommendation:

    • Use AWS S3 if your ML/AI workflows are based on Amazon SageMaker, Athena, or other AWS ML tools, and you need access to large-scale datasets.
    • Use Azure Blob Storage if your machine learning or AI workloads are designed around Azure Databricks, Synapse Analytics, or Azure Machine Learning.

6. Long-Term Archival and Regulatory Compliance

Use Case:

Archiving data for legal or regulatory purposes, where data must be stored securely and remain accessible for many years with minimal cost.

  • Best Choice:
    AWS S3 Glacier and S3 Glacier Deep Archive are purpose-built for low-cost archival storage with compliance features like Object Lock for immutable storage. Azure Blob Storage offers an Archive Tier that’s similarly cost-effective for long-term storage but may not have as fine-tuned cost control for deep archival use cases.

  • Recommendation:

    • Use AWS S3 Glacier if cost is a primary concern and you need features like Object Lock for compliance with regulations such as GDPR or HIPAA.
    • Use Azure Blob Archive Tier if you’re working within Azure’s ecosystem and need low-cost storage for rarely accessed, compliant data.

7. High-Frequency Data Access (Hot Data)

Use Case:

Accessing data frequently or in real time, such as transactional data, real-time logs, or operational data, where performance and low-latency access are key.

  • Best Choice:
    Azure Blob Storage (Hot Tier) and AWS S3 Standard are optimized for high-frequency data access, both offering low-latency access to frequently used data.

  • Recommendation:

    • Use Azure Blob (Hot Tier) if you’re processing high-frequency operational data within Azure, such as for real-time analytics, IoT data, or transactional data.
    • Use AWS S3 Standard if you need fast access to operational or transactional data in AWS, especially when integrated with real-time analytics services like Athena or Redshift.

Conclusion: Key Considerations for Choosing the Right Cloud Storage

  • Ecosystem Integration: Choose the storage solution that integrates best with the cloud services and tools you’re already using (AWS vs. Azure).
  • Data Access Frequency: Hot data (frequently accessed) should go into the standard or hot tier, while cold or archival data should be moved to lower-cost tiers like AWS Glacier or Azure Archive.
  • Compliance Needs: If you have legal or regulatory data retention requirements, consider storage solutions that offer features like immutability (e.g., AWS S3 Object Lock).
  • Disaster Recovery: If redundancy and fast disaster recovery are essential, consider services with geo-replication, like Azure GRS or AWS cross-region replication.
  • Cost: Compare pricing based on the access patterns, size, and redundancy needs of your data.

By understanding these use cases and the features provided by AWS S3 and Azure Blob Storage, you can make an informed choice to align your data storage strategy with your organization’s requirements.

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