Security & Compliance: Implementing IAM, encryption, data loss prevention, and compliance measures.
Reliability & Fidelity: Data preparation, monitoring, disaster recovery, and ACID compliance.
Flexibility & Portability: Designing for multi-cloud, data governance, and migration strategies.
Data Migrations: Planning and executing migrations to Google Cloud using tools like BigQuery Data Transfer Service and Datastream.
Pipeline Planning: Defining data sources, transformation logic, and networking considerations.
Pipeline Building: Utilizing tools like Dataflow, Dataproc, Cloud Data Fusion, and BigQuery for data ingestion and transformation.
Deployment & Operationalization: Managing pipeline performance, cost, and scalability.
Storage Systems: Choosing between BigQuery, Cloud SQL, Cloud Storage, Bigtable, and Firestore based on use cases.
Data Modeling: Designing data models, normalization, and data access patterns.
Data Lakes & Mesh: Implementing data lakes with tools like Dataplex and managing distributed data systems.
Data Preparation: Optimizing data for analysis using BigQuery, including partitioning and clustering.
Data Visualization: Integrating with tools like Looker and Data Studio for data visualization.
SQL Proficiency: Writing complex queries for data analysis and reporting.
Monitoring & Troubleshooting: Using tools like Cloud Monitoring and Logging to oversee data systems.
Automation: Implementing automation for data workflows and pipeline management.
Failure Management: Designing systems for fault tolerance and data recovery.
Google Cloud SkillsBoost: Offers a structured learning path with hands-on labs.
Coursera: Provides a Professional Certificate in Data Engineering.
ProjectPro: Offers a course with practical projects on GCP data engineering.
Aakash.ai: Provides study materials and notes for the certification exam.
Whether you're a student, a working professional, or a career switcher, our training programs are tailored to your needs. Join us and master data science with one of Hyderabad's top-rated institutes.
Data engineering is the discipline of designing and building systems for collecting, storing, and analyzing data at scale. The goal is to create data pipelines that clean, transform, and organize raw data into usable formats for analytics and machine learning.
graph TD
A[Data Sources] –> B[Ingest with Pub/Sub or Storage]
B –> C[Process with Dataflow or Dataproc]
C –> D[Store in BigQuery or GCS]
D –> E[Analyze with SQL, Looker Studio]
E –> F[Serve to users / ML models]
Proficiency in SQL, Python, and Spark
Understanding of cloud architecture and data modeling
Experience with ETL, streaming, and data pipeline orchestration
Familiarity with DevOps tools (CI/CD, Terraform, CloudFormation)
Provoke Trainings is a leading training provider in Hyderabad, offering a comprehensive range of online, offline, and corporate training solutions. We specialize in IT and business technologies, delivering industry-relevant courses designed to enhance practical skills and career readiness.
"Transitioning from a Zonal Manager to a Data Scientist was a bold move, but Provoke Trainings made it seamless. The curriculum was comprehensive, and the hands-on projects were invaluable. I now apply advanced analytics daily to solve complex business problems.
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