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Bid is per book.
The following eBooks are available-
Big Data on Kubernetes is your comprehensive guide to leveraging Kubernetes for scalable and efficient big data solutions. You will learn key concepts of Kubernetes architecture and explore tools like Apache Spark, Airflow, and Kafka. Gain hands-on experience building complete data pipelines to tackle real-world data challenges.
What this Book will help me do
Understand Kubernetes architecture and learn to deploy and manage clusters.
Build and orchestrate big data pipelines using Spark, Airflow, and Kafka.
Develop scalable and resilient data solutions with Docker and Kubernetes.
Integrate and optimize data tools for real-time ingestion and processing.
Apply concepts to hands-on projects addressing actual big data scenarios.
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"Cracking the Data Engineering Interview" is your essential guide to mastering the data engineering interview process. This book offers practical insights and techniques to build your resume, refine your skills in Python, SQL, data modeling, and ETL, and confidently tackle over 100 mock interview questions. Gain the knowledge and confidence to land your dream role in data engineering.
What this Book will help me do
Craft a compelling data engineering portfolio to stand out to employers.
Refresh and deepen understanding of essential topics like Python, SQL, and ETL.
Master over 100 interview questions that cover both technical and behavioral aspects.
Understand data engineering concepts such as data modeling, security, and CI/CD.
Develop negotiation, networking, and personal branding skills crucial for job applications.
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Unlock the secrets to building scalable and efficient data architectures with 'Data Engineering Best Practices.' This book provides in-depth guidance on designing, implementing, and optimizing cloud-based data pipelines. You will gain valuable insights into best practices, agile workflows, and future-proof designs.
What this Book will help me do
Effectively plan and architect scalable data solutions leveraging cloud-first strategies.
Master agile processes tailored to data engineering for improved project outcomes.
Implement secure, efficient, and reliable data pipelines optimized for analytics and AI.
Apply real-world design patterns and avoid common pitfalls in data flow and processing.
Create future-ready data engineering solutions following industry-proven frameworks.
