You will get an end-to-end Data Engineering solution in Microsoft Azure.
Project details
Explore the full potential of Microsoft Azure with our tailored data engineering solution:
š Transform Data: Seamlessly ingest, clean, and transform diverse datasets into valuable insights.
š Azure Tools Integration: Harness the capabilities of Azure Data Factory for orchestrating data workflows, Azure Databricks for advanced analytics and big data processing, Azure Synapse Analytics for data warehousing and analytics at scale, and Azure Power BI for interactive data visualization.
š Scalable Pipelines: Architect scalable and resilient data pipelines to handle large volumes of data efficiently, ensuring optimal performance and reliability.
š Real-time Processing: Implement real-time data processing solutions using Azure Stream Analytics and Azure Event Hubs, enabling instant insights and faster decision-making.
š Performance Optimization: Optimize data engineering workflows for performance and cost-effectiveness, leveraging Azure's autoscaling capabilities and cost management tools.
š Collaboration & Documentation: Provide comprehensive documentation and training to empower teams in managing and utilizing the data engineering solution effectively.
š Transform Data: Seamlessly ingest, clean, and transform diverse datasets into valuable insights.
š Azure Tools Integration: Harness the capabilities of Azure Data Factory for orchestrating data workflows, Azure Databricks for advanced analytics and big data processing, Azure Synapse Analytics for data warehousing and analytics at scale, and Azure Power BI for interactive data visualization.
š Scalable Pipelines: Architect scalable and resilient data pipelines to handle large volumes of data efficiently, ensuring optimal performance and reliability.
š Real-time Processing: Implement real-time data processing solutions using Azure Stream Analytics and Azure Event Hubs, enabling instant insights and faster decision-making.
š Performance Optimization: Optimize data engineering workflows for performance and cost-effectiveness, leveraging Azure's autoscaling capabilities and cost management tools.
š Collaboration & Documentation: Provide comprehensive documentation and training to empower teams in managing and utilizing the data engineering solution effectively.
Database Type
MySQL, MS SQL, Oracle, SQLite, PostgreSQL, MongoDB, Teradata, Azure Cosmos DBWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 3 days
- Number of Revisions 2
- Source Code
Optional add-ons
You can add these on the next page.
Fast 1 Day Delivery
+$100About Muhammad
Data Engineer
Rawalpindi, PakistanĀ - 9:31 pm local time
Aws Data engineering :-
ā Designing end-to-end Aws serverless data pipeline and data warehouse using aws component.
ā Responsible for maintaining quality data in data warehouse by performing operations such as cleaning, transformation and ensuring Integrity by working closely with the stakeholders and solution architect
ā Implementing different data pipeline layers like building ingestion layer, transformation layer and consumption layer.
ā Implementing delta lake for different business use case
ā Developed reusable framework to be leveraged for automated ETL from RDBMS systems or s3 file path to the Data Lake utilizing Spark Data Sources and config file.
ā Implemented AWS Step Functions to automate and orchestrate the end-to-end pipeline
TECHNICAL SKILLS
āØ Data Visualization Tools: Power BI, Tableau, AWS QuickSight
āØ Scripting-Languages: Shell Scripting, Python, Spark
āØ Aws component: Aws S3, Lambda, EC2, RDS, Athena, glue, QuickSight, cloudwatch, DMS etc.
āØ Database: Oracle 11g, postgres
Get in touch today to let me know what you need. Iām happy to outline how to get there.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
equirement Gathering
Gather client requirements, including data sources, objectives, and timelines.
Implementation:
Develop and deploy data pipelines, storage solutions, and analytics tools on Azure.