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AWS Data Engineering with Data Analytics Online Training in Hyderabad

Visualpath provides top-quality AWS Data Engineering Training in Hyderabad by real-time experts. Our training is available worldwide, and we offer daily recordings and presentations for reference. Call us at 91-9989971070 for a free demo.<br>WhatsApp: https://www.whatsapp.com/catalog/919989971070/<br>Visit blog: https://visualpathblogs.com/<br>Visit: https://www.visualpath.in/aws-data-engineering-with-data-analytics-training.html<br>

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AWS Data Engineering with Data Analytics Online Training in Hyderabad

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  1. What is data engineering architecture? & Best Tools and Technologies Data engineering architecture refers to the blueprint for designing and managing data flows, storage, and processing within an organization. It encompasses the frameworks, tools, and methodologies used to collect, transform, store, and deliver data to support analytics and decision-making processes. Here is a comprehensive guide to understanding data engineering architecture: AWS Data Engineer Training Components of Data Engineering Architecture 1.Data Sources: oStructured Data: Databases, data warehouses. oUnstructured Data: Logs, social media, IoT devices. oSemi-structured Data: JSON, XML files. 2.Data Ingestion: oBatch Ingestion: Data is collected and processed in large chunks at scheduled intervals. oStream Ingestion: Real-time data collection and processing.

  2. 3.Data Processing: oETL (Extract, Transform, Load): Traditional method where data is extracted from sources, transformed into a suitable format, and loaded into a data warehouse. AWS Data Engineering Training in Hyderabad oELT (Extract, Load, Transform): Data is extracted and loaded into a data warehouse first, then transformed. oStreaming Processing: Real-time processing of data streams. 4.Data Storage: oData Lakes: Store raw data in its native format. oData Warehouses: Structured storage optimized for querying and analysis. oData Marts: Subsets of data warehouses, tailored for specific business needs. 5.Data Transformation: oBatch Processing Tools: Apache Spark, Hadoop. oStreaming Processing Tools: Apache Kafka, Apache Flink. 6.Data Integration: oData Pipelines: Process data from source to destination automatically with data pipelines. oApache Airflow and Luigi are some orchestration tools. 7.Data Protection and Governance: oData Quality: Providing reliable, consistent, and accurate data is known as data quality. oData Lineage: Tracking the flow of data from source to destination. oAccess Control: Managing who can view and modify data. 8.Data Access and Consumption: oBI Tools: Tableau, Power BI. oData APIs: Allowing programmatic access to data.

  3. oQuery Engines: Presto, Druid. Best Practices for Data Engineering Architecture 1.Scalability: Design systems that can handle growing amounts of data and increased demand. 2.Modularity: Use modular components that can be independently developed and maintained. AWS Data Engineering Course 3.Flexibility: Ensure the architecture can adapt to changing requirements and new technologies. 4.Reliability: Implement robust error handling, monitoring, and alerting mechanisms. 5.Performance: Optimize for low latency and high throughput in data processing. 6.Security: Protect data at rest and in transit with encryption and access controls. 7.Data Quality: Regularly validate and clean data to maintain high quality. Tools and Technologies 1.Ingestion: Apache NiFi, AWS Glue, Google Cloud Dataflow. 2.Storage: Amazon S3, Google BigQuery, Azure Data Lake Storage. 3.Processing: Apache Spark, Databricks, Flink. 4.Orchestration: Apache Airflow, Google Cloud Composer. 5.BI and Analytics: Looker, Qlik, Power BI. Case Study: Example Architecture 1.Data Sources: CRM, ERP systems, weblogs, social media APIs. 2.Ingestion Layer: Using Apache Kafka for real-time data and AWS Glue for batch data. 3.Processing Layer: Transforming data with Apache Spark and storing in a data lake (Amazon S3). 4.Storage Layer: Processed data is moved to a data warehouse (Amazon Redshift).

  4. 5.Integration Layer: Data pipelines orchestrated with Apache Airflow. 6.Consumption Layer: Business analysts use Tableau for reporting and dashboards. By understanding and implementing a robust data engineering architecture, organizations can efficiently manage their data, gain valuable insights, and make data-driven decisions. AWS Data Engineering Training Institute Visualpath is the Best Software Online Training Institute in Hyderabad. Avail complete AWS Data Engineering with Data Analytics worldwide. You will get the best course at an affordable cost. Attend Free Demo Call on - +91-9989971070. WhatsApp: https://www.whatsapp.com/catalog/917032290546/ Visit blog: https://visualpathblogs.com/ Visit https://www.visualpath.in/aws-data-engineering-with-data-analytics- training.html

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