Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly. Author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The book's second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service. You will: Learn common architectures for real-time analytics Discover how event processing differs from real-time analytics Ingest event data from Apache Kafka into Apache Pinot Combine event streams with OLTP data using Debezium and Kafka Streams Write real-time queries against event data stored in Apache Pinot Build a real-time dashboard and order tracking app Learn how Uber, Stripe, and Just Eat use real-time analytics

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

This book delves into the techniques and tools essential for designing, processing, and analyzing complex datasets in real-time using advanced frameworks like Apache Spark, Storm, and Amazon Kinesis. By engaging with this thorough guide, you'll build proficiency in creating robust, efficient, and scalable real-time data processing architectures tailored to real-world scenarios. What this Book will help me do Learn the fundamentals of real-time data processing and how it differs from batch processing. Gain hands-on experience with Apache Storm for creating robust data-driven solutions. Develop real-world applications using Amazon Kinesis for cloud-based analytics. Perform complex data queries and transformations with Spark SQL and understand Spark RDDs. Master the Lambda Architecture to combine batch and real-time analytics effectively. Author(s) Shilpi Saxena is a renowned expert in big data technologies, holding extensive experience in real-time data analytics. With a career spanning years in the industry, Shilpi has provided innovative solutions for big data challenges in top-tier organizations. Her teaching approach emphasizes practical applicability, making her writings accessible and impactful for developers and architects alike. Who is it for? This book is for software professionals such as Big Data architects, developers, or programmers looking to enhance their skills in real-time big data analytics. If you are familiar with basic programming principles and seek to build solutions for processing large data streams in real-time environments, this book caters to your needs. It is also suitable for those seeking to familiarize themselves with using state-of-the-art tools like Spark SQL, Apache Storm, and Amazon Kinesis. Whether you're extending current expertise or transitioning into this field, this resource helps you achieve your objectives.