metosys_backend

Tag: cloud data engineering

Embrace the scalability and flexibility of cloud data architectures to manage, store, and analyze your information in the cloud. Our deep dives cover data lakes on S3, Delta Lake patterns, and serverless compute with AWS Lambda and Azure Functions. Learn how to secure your data with IAM roles, encrypt at rest and in transit, and implement lifecycle policies to optimize costs. From ingestion with Kafka and Event Hubs to interactive querying with Athena and Synapse, we cover the end‑to‑end lifecycle. Migrate with confidence—explore our Cloud Data resources now!

  • Designing Scalable AWS Data Pipelines

    Designing Scalable AWS Data Pipelines

    Cloud-based data pipelines are essential for modern analytics and decision-making. AWS offers powerful tools like Glue, Redshift, and S3 to build pipelines that scale effortlessly with your business.

    A data pipeline collects data from sources (e.g., APIs, logs, databases), transforms it, and stores it in a data warehouse. For instance, an e-commerce platform can use a pipeline to analyze customer behavior by ingesting clickstream data into Redshift for BI tools.

    AWS Glue simplifies ETL (extract, transform, load) processes with visual workflows and job schedulers. Redshift serves as the destination for structured data, enabling fast queries and reports.

    To build a pipeline:

    Define your data sources.

    Use AWS Glue to create crawler jobs that identify schema.

    Schedule transformations using Glue Jobs (Python/Spark).

    Store final data in Redshift or Athena for reporting.

    Monitoring and alerting using CloudWatch ensures reliability. Secure the pipeline with IAM roles and encryption.

    A scalable pipeline reduces manual data handling, supports real-time analytics, and ensures consistency across the organization. Whether it’s sales data, marketing funnels, or IoT logs—cloud pipelines are the backbone of data-driven success.