Time Series Analysis With Spark
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Time Series Analysis with Spark
Author: Yoni Ramaswami
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-03-28
Master the fundamentals of time series analysis with Apache Spark and Databricks and uncover actionable insights at scale Key Features Quickly get started with your first models and explore the potential of Generative AI Learn how to use Apache Spark and Databricks for scalable time series solutions Establish best practices to ensure success from development to production and beyond Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by Databricks Senior Solutions Architect Yoni Ramaswami, whose expertise in Data and AI has shaped innovative digital transformations across industries, this comprehensive guide bridges foundational concepts of time series analysis with the Spark framework and Databricks, preparing you to tackle real-world challenges with confidence. From preparing and processing large-scale time series datasets to building reliable models, this book offers practical techniques that scale effortlessly for big data environments. You’ll explore advanced topics such as scaling your analyses, deploying time series models into production, Generative AI, and leveraging Spark's latest features for cutting-edge applications across industries. Packed with hands-on examples and industry-relevant use cases, this guide is perfect for data engineers, ML engineers, data scientists, and analysts looking to enhance their expertise in handling large-scale time series data. By the end of this book, you’ll have mastered the skills to design and deploy robust, scalable time series models tailored to your unique project needs—qualifying you to excel in the rapidly evolving world of big data analytics.What you will learn Understand the core concepts and architectures of Apache Spark Clean and organize time series data Choose the most suitable modeling approach for your use case Gain expertise in building and training a variety of time series models Explore ways to leverage Apache Spark and Databricks to scale your models Deploy time series models in production Integrate your time series solutions with big data tools for enhanced analytics Leverage GenAI to enhance predictions and uncover patterns Who this book is for If you are a data engineer, ML engineer, data scientist, or analyst looking to enhance your skills in time series analysis with Apache Spark and Databricks, this book is for you. Whether you’re new to time series or an experienced practitioner, this guide provides valuable insights and techniques to improve your data processing capabilities. A basic understanding of Apache Spark is helpful, but no prior experience with time series analysis is required.
Codeless Time Series Analysis with KNIME
Author: Corey Weisinger
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-08-19
Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methods Key Features • Gain a solid understanding of time series analysis and its applications using KNIME • Learn how to apply popular statistical and machine learning time series analysis techniques • Integrate other tools such as Spark, H2O, and Keras with KNIME within the same application Book Description This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques. This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There's no time series analysis book without a solution for stock price predictions and you'll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools. By the end of this time series book, you'll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases. What you will learn • Install and configure KNIME time series integration • Implement common preprocessing techniques before analyzing data • Visualize and display time series data in the form of plots and graphs • Separate time series data into trends, seasonality, and residuals • Train and deploy FFNN and LSTM to perform predictive analysis • Use multivariate analysis by enabling GPU training for neural networks • Train and deploy an ML-based forecasting model using Spark and H2O Who this book is for This book is for data analysts and data scientists who want to develop forecasting applications on time series data. While no coding skills are required thanks to the codeless implementation of the examples, basic knowledge of KNIME Analytics Platform is assumed. The first part of the book targets beginners in time series analysis, and the subsequent parts of the book challenge both beginners as well as advanced users by introducing real-world time series applications.
Big Data Processing with Apache Spark
Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.