Building Modern Data Applications Using Databricks Lakehouse
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Building Modern Data Applications Using Databricks Lakehouse
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Author : Will Girten
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-21
Building Modern Data Applications Using Databricks Lakehouse written by Will Girten and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-21 with categories.
Develop, optimize, and monitor data pipelines on Databricks
Building The Data Lakehouse
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Author : Bill Inmon
language : en
Publisher: Technics Publications
Release Date : 2021-10
Building The Data Lakehouse written by Bill Inmon and has been published by Technics Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10 with categories.
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
Managing Data As A Product
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Author : Andrea Gioia
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-29
Managing Data As A Product written by Andrea Gioia and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable way Key Features Leverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architectures Learn how to identify, implement, and operate data products throughout their life cycle Design and execute a forward-thinking strategy to turn your data products into organizational assets Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTraditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets. Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions. By the end of this book, you’ll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.What you will learn Overcome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costs Discover the benefits of adopting a data-as-a-product approach for scalability and sustainability Navigate the complete data product lifecycle, from inception to decommissioning Automate data product lifecycle management using a self-serve platform Implement an incremental, value-driven strategy for transitioning to data-product-centric architectures Optimize data modeling in distributed environments to enhance GenAI-based use cases Who this book is for If you’re an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you’re a staff engineer, product manager, or a software engineering leader or executive, you’ll find this book useful. Familiarity with basic data engineering principles and practices is assumed.
Data Engineering With Apache Spark Delta Lake And Lakehouse
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Author : Manoj Kukreja
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-22
Data Engineering With Apache Spark Delta Lake And Lakehouse written by Manoj Kukreja and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Computers categories.
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Data Lakehouse In Action
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Author : Pradeep Menon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-17
Data Lakehouse In Action written by Pradeep Menon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-17 with Computers categories.
Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.
The Enterprise Big Data Lake
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Author : Alex Gorelik
language : en
Publisher: O'Reilly Media
Release Date : 2019-02-21
The Enterprise Big Data Lake written by Alex Gorelik and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries
Beginning Apache Spark Using Azure Databricks
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Author : Robert Ilijason
language : en
Publisher: Apress
Release Date : 2020-06-11
Beginning Apache Spark Using Azure Databricks written by Robert Ilijason and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-11 with Computers categories.
Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything aboutconfiguring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloud Get started with Databricks using SQL and Python in either Microsoft Azure or AWS Understand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.
Delta Lake The Definitive Guide
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Author : Denny Lee
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-10-30
Delta Lake The Definitive Guide written by Denny Lee and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-30 with Computers categories.
Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques. Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale. This book helps you: Understand key data reliability challenges and how Delta Lake solves them Explain the critical role of Delta transaction logs as a single source of truth Learn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and Trino Architect data lakehouses with the medallion architecture Optimize Delta Lake performance with features like deletion vectors and liquid clustering
Essential Pyspark For Scalable Data Analytics
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Author : Sreeram Nudurupati
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29
Essential Pyspark For Scalable Data Analytics written by Sreeram Nudurupati and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Azure Databricks Cookbook
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Author : Phani Raj
language : en
Publisher: Packt Publishing
Release Date : 2021-09-17
Azure Databricks Cookbook written by Phani Raj and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-17 with categories.
Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key Features: Integrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelines Use Databricks SQL to run ad hoc queries on your data lake and create dashboards Productionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environments Book Description: Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What You Will Learn: Understand Databricks cluster options and when to use them Read and write data from and to Azure sources such as ADLS Gen-2, EventHub, and more Build a data warehouse in Azure Databricks Perform ad hoc analysis on data lakes using Databricks SQL Analytics Integrate with Azure Key Vault to access hidden data and Log Analytics for telemetry and monitoring Integrate Databricks with Azure DevOps for version control and for deployment and to productionize the solution using CI/CD pipelines Build a data processing pipeline for near real-time data analytics Who this book is for: This recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.