The low cost of getting started with cloud services can easily evolve into a significant expense down the road. That's challenging for teams developing data pipelines, particularly when rapid changes in technology and workload require a constant cycle of redesign. How do you deliver scalable, highly available products while keeping costs in check? With this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage. You'll also pick up best practices for code development, testing, and monitoring. By focusing on the entire design process, you'll be able to deliver cost-effective, high-quality products. This book helps you: Reduce cloud spend with lower cost cloud service offerings and smart design strategies Minimize waste without sacrificing performance by rightsizing compute resources Drive pipeline evolution, head off performance issues, and quickly debug with effective monitoring Set up development and test environments that minimize cloud service dependencies Create data pipeline code bases that are testable and extensible, fostering rapid development and evolution Improve data quality and pipeline operation through validation and testing

The low cost of getting started with cloud services can easily evolve into a significant expense down the road. That's challenging for teams developing data pipelines, particularly when rapid changes in technology and workload require a constant cycle of redesign. How do you deliver scalable, highly available products while keeping costs in check? With this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage. You'll also pick up best practices for code development, testing, and monitoring. By focusing on the entire design process, you'll be able to deliver cost-effective, high-quality products. This book helps you: Reduce cloud spend with lower cost cloud service offerings and smart design strategies Minimize waste without sacrificing performance by rightsizing compute resources Drive pipeline evolution, head off performance issues, and quickly debug with effective monitoring Set up development and test environments that minimize cloud service dependencies Create data pipeline code bases that are testable and extensible, fostering rapid development and evolution Improve data quality and pipeline operation through validation and testing

This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. --

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting

Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index

AWS Certification Guide - AWS Certified Data Analytics – Specialty Unlock the Power of AWS Data Analytics Dive into the evolving world of AWS data analytics with this comprehensive guide, tailored for those pursuing the AWS Certified Data Analytics – Specialty certification. This book is an essential resource for professionals seeking to validate their expertise in extracting meaningful insights from data using AWS analytics services. Inside, You'll Discover: Comprehensive Analytics Concepts: Thorough exploration of AWS data analytics services and tools, including Kinesis, Redshift, Glue, and more. Real-World Scenarios: Practical examples and case studies that demonstrate how to effectively use AWS services for data analysis, processing, and visualization. Targeted Exam Preparation: Insights into the certification exam format, with chapters aligned to the exam domains, complete with detailed explanations and practice questions. Latest Trends and Best Practices: Up-to-date information on the newest AWS features and data analytics best practices, ensuring your skills remain at the cutting edge. Authored by a Data Analytics Expert Written by a professional with extensive experience in AWS data analytics, this guide melds practical application with theoretical knowledge, providing a rich learning experience. Your Comprehensive Analytics Resource Whether you are deepening your existing skills or embarking on a new specialty in data analytics, this book is your definitive companion, offering a deep dive into AWS analytics services and preparing you for the Specialty certification exam. Advance Your Data Analytics Career Go beyond the fundamentals and master the complexities of AWS data analytics. This guide is not just about passing the exam; it's about developing expertise that can be applied in real-world scenarios, propelling your career forward in this exciting domain. Start Your Specialized Analytics Journey Today Embark on your path to becoming an AWS Certified Data Analytics specialist. This guide is your first step towards mastering AWS analytics and unlocking new career opportunities in the field of data. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines