• Regeln für den Dokumente-Bereich:

    In den Börsenbereich gehören nur Angebote die bereits den Allgemeinen Regeln entsprechen.

    Allgemeines:

    Nicht erlaubt im Dokumente-Bereich sind:

    - indizierte Titel (inkl. Comics)
    - extremistische Werke, Zeitschriften und Comics (egal, welche Richtung)
    - jegliche Art von Pornographie
    - Anleitungen zu kriminellen Handlungen, gleich welcher Art
    - sadistische, menschenverachtende oder ähnliche Werke

    Nutzt den "Bedanken"-Button, bei Sammelthreads führen jegliche Kommentare, positiv wie negativ, sehr schnell zu einer Unübersichtlichkeit des Threads. Downmeldungen sind an den Uploader zu richten

    Vor dem Einstellen zu beachten:

    - Suchfunktion

    Vergewissert euch, dass es euer Dokument noch nicht im Board gibt, Doppelposts werden kommentarlos gelöscht. Ist es schon vorhanden, tragt es als Mirror im bestehenden Post ein.

    - Threadtitel

    Idealerweise ist sofort zu erkennen um was es sich handelt. Verseht euren Titel mit den relevanten Informationen, das hilft euch und damit auch uns und allen Suchenden erheblich weiter.

    Beispiel: [Thriller] Dan Brown - Inferno oder bei Magazinen:

    Computerbild - 14/2014 (es muss ersichtlich sein, um welche Ausgabe und welches Magazin es sich handelt)

    Folgende Präfixe stehen im Unterforum "Unterhaltung" zur Verfügung:

    [Humor]
    [Drama]
    [Erotik]
    [Fantasy]
    [Krimi]
    [Roman]
    [Thriller]
    [Horror]
    [Science Fiction]

    Inhalt des Beitrags:

    Folgende Pflichtangaben gilt es einzuhalten:

    - Autor
    - Titel
    - Präfix
    - Cover
    - Genre
    - Inhaltsbeschreibung
    - enthaltene Formate
    - Gesamtgröße des Downloads
    - Hoster
    - ggf. Passwort

    Nicht erlaubt sind alle Dateien, die den Download unnötig aufblähen um eine Affiliategrenze zu erreichen, wie zB. mp3-files, übergroße Bilder, etc.

    Ebenso nicht erlaubt sind sämtliche Dateien mit DRM, persönlichen Daten, etc., diese werden kommentarlos zu eurem eigenem Schutz gelöscht.

    Achtet bitte bei der Konvertierung der Formate auf die Lesbarkeit, ein epub, was nur einfach durch Calibre gejagt wird um ein PDF zu erhalten, ist zu 99% eben nicht lesbar. Wenn ihr es nicht könnt, dann lasst es besser oder lest euch ein, wie man es richtig macht.


    Unterforum Comics:

    Threadtitel:

    Ähnlich, wie bei Unterhaltung und Magazinen, sollte der Titel alle relevanten Informationen enthalten, hier bitte

    - den Titel des Comics
    - den Verlag (einige Comics sind in verschiedenen Verlagen erschienen)
    - das Erscheinungsjahr

    Erlaubt sind folgende Formate:

    - CBR
    - CBZ

    Grundsätzlich gilt: jede Version eines Comics erhält einen eigenen Thread, Ersteller eines Comics können ihre Bände gerne mit dem Zusatz (Original-Release) versehen.

    Bei Unsicherheiten zur korrekten Benennung bitte die Informationen von www.comicguide.de nutzen.

    Inhalt des Beitrags:

    Pflichtangaben hier sind:

    - Titel des Bandes und ggf. Nummer
    - Cover
    - falls bekannt technische Daten (DPI, Breite, Speicherqualität)
    - Größe des Downloads
    - Hoster
    - ggf. Passwort
    - falls bekannt Releasenamen
  • Bitte registriere dich zunächst um Beiträge zu verfassen und externe Links aufzurufen.

++ Premium IPTV ++ 7000 Live Sender ++ UHD Qualität ++ Mediathek mit 18.000 Inhalten ++


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Collection Download Ebooks Best Seller Updated Daily

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Data Engineering for Cybersecurity: Build Secure Data Pipelines with Free and Open-Source Tools
English | 2025 | ISBN: 1718504020 | 340 Pages | EPUB | 4 MB
Security teams rely on telemetry-the continuous stream of logs, events, metrics, and signals that reveal what's happening across systems, endpoints, and cloud services. But that data doesn't organize itself. It has to be collected, normalized, enriched, and secured before it becomes useful. That's where data engineering comes in.




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Data Engineering with Databricks Cookbook: Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake
English | May 31, 2024 | ASIN: B0C5MTLMMC | 696 pages | EPUB (True) | 21.22 MB
Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your data

Key Features
Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake
Gain practical guidance on using Delta Lake tables and orchestrating data pipelines
Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Written by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark.
What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You'll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You'll also go through the full process of setup and configuration of the Unity Catalog for data governance.
By the end of this book, you'll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.
What you will learn
Perform data loading, ingestion, and processing with Apache Spark
Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark
Manage and optimize Delta tables with Apache Spark and Delta Lake APIs
Use Spark Structured Streaming for real-time data processing
Optimize Apache Spark application and Delta table query performance
Implement DataOps and DevOps practices on Databricks
Orchestrate data pipelines with Delta Live Tables and Databricks Workflows
Implement data governance policies with Unity Catalog
Who this book is for
This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming.
Table of Contents
Data Ingestion and Data Extraction with Apache Spark
Data Transformation and Data Manipulation with Apache Spark
Data Management with Delta Lake
Ingesting Streaming Data
Processing Streaming Data
Performance Tuning with Apache Spark
Performance Tuning in Delta Lake
Orchestration and Scheduling Data Pipeline with Databricks Workflows
Building Data Pipelines with Delta Live Tables
Data Governance with Unity Catalog
Implementing DataOps and DevOps on Databricks



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Data Science: Concepts, Strategies, and Applications
English | November 4, 2023 | ASIN: B0CMRWYDNS | 130 pages | EPUB (True) | 718.44 KB
Dive into the heart of data-driven discovery with 'Data Science: Concepts, Strategies, and Applications.' This comprehensive guide takes you on a transformative journey through the intricate world of data science, unraveling its core concepts, machine learning algorithms, and practical applications. Explore the ethical considerations woven into data analysis, ensuring responsible practices in every decision made. From preprocessing techniques and feature engineering to model evaluation and deployment strategies, this book equips both beginners and experts with essential skills. Delve into the cutting-edge realms of advanced AI, including reinforcement learning, generative adversarial networks, and quantum computing applications. Fusing theoretical knowledge with real-world insights, this book is your go-to resource for mastering data science. Whether you're a novice aspiring to enter the field or a seasoned professional, this guide empowers you to navigate the complexities of data science and harness its transformative potential.




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Data Science Essentials: Machine Learning and Natural Language Processing
English | June 17, 2023 | ASIN: B0C8GVJL71 | 70 pages | EPUB (True) | 564.61 KB
Are you ready to dive into the exciting world of Data Science? In today's data-driven society, understanding and harnessing the power of information has become essential for success in any field.

This comprehensive guide takes you on a journey through the core principles, methodologies, and applications of Data Science.
Topics:
Data Visualization
Machine Learning
Data Mining
Natural Language Processing
Artificial Intelligence
Deep Learning
Neural Networks



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Data Science with Oracle Miner and R Enterprise
English | 2023 | ASIN: None | 592 pages | Epub | 10.31 MB
Data science involves a wide array of technologies and statistical algorithms. This makes it difficult to automate each and every aspect of it. However, there are some areas in data science that can be automated using scripts and workflows.

At a high level, we can classify data science automation into the following categories:
Repetitive tasks automation: Repetitive tasks are those that have to be done every time while building models. Data extraction, data cleaning, and basic data
transformations such as imputing null values and algorithm-specific transformations are some tasks that fall into this category. These are to be done even for the same set of data every time. Automating these tasks would take some of the burdens off of a data scientist so they can concentrate more on solving business problems.
Automated statistician: This is an area of data science automation where statistical routines and machine learning are automated. The system executes the best algorithm based on the provided data set. It hides the intricacies and mathematical complexity of algorithms from the user. The user needs to provide automated statisticians with data. It understands the data, creates different mathematical models, and returns the result based on the model that best explains the data. It is still at a nascent stage and an active area of research.
Problem-specific automation: This involves automating a data science process based on the problem at hand. This eases a user of carrying out the same activity for a specific problem several times. For example, the data scientist of an organization develops a model for predicting future sales of an organization. Based on an organization's requirement, this activity has to be carried out at different time frames, say monthly or quarterly. Each time, a wide array of tasks have to be performed, such as extracting the data, preprocessing it, creating statistical models, and returning the results back to the business operation's database. If this whole process is automated, it drives agile business decisions, better customer focus, and operationally efficient and managed resources.



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Data Science with SQL Server Quick Start Guide: Integrate SQL Server with data science
English | 2018 | ASIN: B07H2WJFWW | 196 Pages | PDF EPUB (True) | 15 MB
SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you.




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Data Sharing für KMU: Voraussetzungen und Instrumente für die gemeinsame Nutzung von Daten
Deutsch | 2025 | ISBN: 3662712083 | 289 Pages | PDF (True) | 15 MB
Ein bewusster Umgang mit Daten ist für Unternehmen wichtiger denn je: Sie fördern Innovation in Geschäftsmodellen, erfordern aber auch einen effizienten und nachhaltigen Datenumgang. Unternehmen, die Daten teilen und nutzen, wirtschaften effizienter durch Data Sharing. In diesem Fachbuch werden verschiedene Aspekte des Data Sharings aus unternehmensübergreifender und -interner Perspektive, vorwiegend mit Blick auf kleine und mittelständische Unternehmen (KMU), betrachtet. Die Autor:innen untersuchen, wie Unternehmen Anreize schaffen können, um erfolgreich an der Datenökonomie teilzunehmen, aber auch welche externen Bedingungen gegeben sein sollten, um Unternehmen in die Lage zu versetzen, ihre ökonomischen Potenziale in Bezug auf Daten zu maximieren.




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Data-centric Approaches to Industrial Decisions: Technology, Digitisation and Business Decisions (Asset Analytics) by P. K. Kapur, Omar Alhazmi, Shakshi Singhal
English | August 31, 2025 | ISBN: 9819675553 | 485 pages | Epub PDF (True) | 56 Mb
This book explores mathematical modeling, predictive analytics, and interdisciplinary tools in formulating, implementing, and evaluating industrial strategies.This book presents cutting-edge research that intersects advanced computational techniques with practical decision-making across industries. This edited book showcases a curated collection of peer-reviewed contributions from international scholars and practitioners, addressing the growing complexity of business decisions in the era of big data, AI, and digital transformation.

From cyber-physical systems to sustainability analytics and business intelligence, each chapter provides a robust academic foundation while offering real-world implications for policymakers, managers, and researchers.
With a focus on actionable insights, the book is a vital reference for operations research, analytics, and digital strategy professionals and an enriching resource for postgraduate and doctoral scholars across business and engineering disciplines.



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Data-Driven Reproductive Health: Role of Bioinformatics and Machine Learning Methods by Abhishek Sengupta, Priyanka Narad, Gaurav Majumdar
English | October 18, 2024 | ISBN: 9819774500 | 242 pages | MOBI | 4.27 Mb
This book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine.

This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.



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Databricks for Not Dummies - Master Databricks in Seven Days: Learn Databricks, Big Data, and Cloud Analytics - No Fluff, Just Real Knowledge
English | March 3, 2025 | ASIN: B0DZ942M3V | 299 pages | Epub | 1.67 MB
Databricks for Not Dummies - Skip the Fluff, Master Databricks & PySpark Fast

Forget "Databricks for Dummies" - this is Databricks for Doers. In just seven days, you'll go from beginner to confident practitioner with this no-nonsense, hands-on guide to Databricks, PySpark, and big data analytics.
Whether you're a data engineer, data analyst, or data scientist, this book cuts through the noise to give you exactly what you need: real-world skills, real code, and real results.
What You'll Learn (No Theory Overload)
* Databricks Demystified
Understand clusters, notebooks, Spark architecture, and the Databricks workspace with clarity and speed.
* PySpark Like a Pro
Work confidently with DataFrames, Spark SQL, joins, aggregations, UDFs, and machine learning workflows.
* Build Real Data Pipelines
Construct and optimize ETL pipelines using Delta Lake, Structured Streaming, and performance tuning techniques.
* Cloud Integration Made Easy
Connect Databricks with AWS, Azure, GCP, Power BI, and Tableau for end-to-end data solutions.
* Enterprise-Grade Security
Implement Unity Catalog, fine-grained access control, and best practices in data governance.
* Certification and Career Advancement
Prepare for official Databricks certifications and position yourself for high-demand data roles.
Why This Book?
* Not for dummies - built for achievers
* Not theory-heavy - 100% practical and example-driven
* Not outdated - aligned with modern, cloud-native data practices
From your first PySpark notebook to real-time streaming and secure, scalable pipelines, this book delivers a full-stack Databricks experience without wasting your time.
Who Should Read This Book?
* Beginners seeking a clear, fast path to Databricks mastery
* Professionals tired of abstract tutorials and endless documentation
* Data teams onboarding to Databricks
* Anyone who searched for "Databricks for dummies" but wants something smarter, sharper, and actually useful
Technologies Covered:
* Apache Spark
* PySpark
* Delta Lake
* Databricks SQL
* Structured Streaming
* MLlib
* Unity Catalog
* AWS, Azure, GCP Integrations
Whether you're building modern data platforms, scaling pipelines, or pursuing your next promotion, this book is your practical guide to mastering Databricks.



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Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
English | May 17, 2024 | ASIN: B0958P9PX9 | 451 pages | EPUB (True) | 31.47 MB
Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on

Key Features
Build machine learning solutions faster than peers only using documentation
Enhance or refine your expertise with tribal knowledge and concise explanations
Follow along with code projects provided in GitHub to accelerate your projects
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Discover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform.
You'll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You'll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources.
By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.
What you will learn
Set up a workspace for a data team planning to perform data science
Monitor data quality and detect drift
Use autogenerated code for ML modeling and data exploration
Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows
Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects
Communicate insights through Databricks SQL dashboards and Delta Sharing
Explore data and models through the Databricks marketplace
Who this book is for
This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.
Table of Contents
Getting Started with This Book and Lakehouse Concepts
Designing Databricks: Day One
Building Out Our Bronze Layer
Getting to Know Your Data
Feature Engineering on Databricks
Searching for a Signal
Productionizing ML on Databricks
Monitoring, Evaluating, and More



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Daughters of Batavia by Stefanie Koens
English | 29 July 2025 | ISBN: 1460766164 | True EPUB | 384 pages | 3.8 MB
A woman searching for answers in her own life finds them - and much more - in the wreckage and haunting stories of the Batavia shipwreck.

The powerfully moving historical debut from the winner of the Banjo Prize for Fiction. For all fans of Natasha Lester, Lauren Chater and Tea Cooper.
Two women. One shipwreck. And four centuries of secrets.
Shortly before Christmas in 2018, Tess McCarthy, after years as a hard-working English teacher who never did anything out of the ordinary, flies to Western Australia's remote Abrolhos Islands. She is in search of answers - both to the infamous Batavia shipwreck and her personal family crises.
Amsterdam, 1628. Saskia, an orphaned young Dutchwoman, boards Batavia with relatives, bound for a new and potentially dangerous life in the East Indies - only for her world to first collide with Aris Jansz, the ship's taciturn under surgeon.
Tess, Saskia and Aris - their lives linked by secrets that span generations - carry the baggage of past losses and the uncertainty of their futures. And, in the most unlikely circumstances, they may find qualities that echo through centuries: faith, acceptance, and love.



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