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Ahmed Ayed
Lead Data Engineer chez France Télévisions
Ahmed Ayed is a skilled Cloud Data Engineer with expertise in AWS and GCP, specializing in designing, developing, and deploying Data Science platforms in the Cloud.
His technical abilities span across various tools and technologies including Spark, Kafka, Beam, Airflow, and the Elastic Stack, with proficiency in Scala, Python, and SQL.
With a background in ingesting data, designing Data Warehouses/Data Lakes, creating ETL pipelines, and deploying Machine Learning models, Ahmed Ayed brings a wealth of experience to the table.
His educational journey includes studying Data Engineering Nanodegree at Udacity and receiving a degree in Computer Engineering from ENSI - Ecole Nationale des Sciences de l'Informatique.
Fluent in multiple languages, Ahmed is known for his creativity, strong teamwork skills, and proactive approach to problem-solving.
Having held key positions such as Lead Data Engineer at France Télévisions, Cloud Data Engineer at Teolia and France Télévisions, and Data Engineer at Orange and Datavora, Ahmed Ayed has a proven track record in the industry.
Highlights
Data Engineering with Google Cloud Specialization Done ✅ Last three courses in the Data Engineering Specialization covers three key topics:
1- Building Resilient Streaming Analytics Systems on GCP. 2- Smart Analytics, Machine Learning, and AI on GCP. 3-Preparing for the Google Cloud Professional Data Engineer Exam.
6 out of 6 high-quality courses that enhanced my knowledge of the Google Cloud Platform and I truly believe that they are a serious contender to lead the Cloud market especially when it comes to Data and Analytics offerings.
Next steps to prepare the Professional Data Engineer Certification would be the highly recommended Linux Academy course by Matthew Ulasien and the Official Google Cloud Certified Study Guide recently published by Dan Sullivan.
#gcp #dataengineering #bigdata #serverless #dataprocessing #analytics #machinelearning #streaming #pipelines
More Google Cloud Platform learning! Step 3 and 4 in the Data Engineering Specialization cover two key topics: 1- Modernizing Data Lakes and Data Warehouses with GCP. 2- Building Batch Data Pipelines on GCP. More specifically how to:
• First topic:
- Describe what Data Lake is and how to use Google Cloud Storage as your Data Lake on GCP.
- Use Cloud SQL for a relational Data lake.
- Work with BigQuery as a scalable Data Warehousing option on GCP.
- Use Nested/Repeated Fields and Partitioninig/Clusterting for optimizations.
• Second topic:
- Describe different methods of loading Data into your Data Lakes and Warehouses: EL, ELT and ETL.
- Learn to lift and shift your existing Hadoop workloads to the cloud using Cloud Dataproc and how to optimize your Dataproc jobs.
- Manage your Data Pipelines with Data Fusion and Cloud Composer.
- Decide between Cloud Dataflow and Cloud Dataproc for processing Data pipelines.
- Understand Dataflow core concepts, templates and SQL.
#gcp #dataengineering #bigdata #serverless #dataprocessing #analytics #datalake #datawarehouse #batch #pipelines
