| databricks coding interview questions: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow |
| databricks coding interview questions: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| databricks coding interview questions: Interview Questions and Answers Richard McMunn, 2013-05 |
| databricks coding interview questions: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| databricks coding interview questions: Java/J2EE Job Interview Companion Arulkumaran Kumaraswamipillai, A. Sivayini, 2007 400+ Java/J2EE Interview questions with clear and concise answers for: job seekers (junior/senior developers, architects, team/technical leads), promotion seekers, pro-active learners and interviewers. Lulu top 100 best seller. Increase your earning potential by learning, applying and succeeding. Learn the fundamentals relating to Java/J2EE in an easy to understand questions and answers approach. Covers 400+ popular interview Q&A with lots of diagrams, examples, code snippets, cross referencing and comparisons. This is not only an interview guide but also a quick reference guide, a refresher material and a roadmap covering a wide range of Java/J2EE related topics. More Java J2EE interview questions and answers & resume resources at http: //www.lulu.com/java-succes |
| databricks coding interview questions: A Practical Guide To Quantitative Finance Interviews Xinfeng Zhou, 2020-05-05 This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews: brain teasers, calculus, linear algebra, probability, stochastic processes and stochastic calculus, finance and programming. |
| databricks coding interview questions: Beginning Apache Spark Using Azure Databricks Robert Ilijason, 2020-06-11 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 about configuring 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 cloudGet started with Databricks using SQL and Python in either Microsoft Azure or AWSUnderstand 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. |
| databricks coding interview questions: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| databricks coding interview questions: Tcl/Tk in a Nutshell Paul Raines, Jeff Tranter, 1999-03-25 The Tcl language and Tk graphical toolkit are simple and powerful building blocks for custom applications. The Tcl/Tk combination is increasingly popular because it lets you produce sophisticated graphical interfaces with a few easy commands, develop and change scripts quickly, and conveniently tie together existing utilities or programming libraries.One of the attractive features of Tcl/Tk is the wide variety of commands, many offering a wealth of options. Most of the things you'd like to do have been anticipated by the language's creator, John Ousterhout, or one of the developers of Tcl/Tk's many powerful extensions. Thus, you'll find that a command or option probably exists to provide just what you need.And that's why it's valuable to have a quick reference that briefly describes every command and option in the core Tcl/Tk distribution as well as the most popular extensions. Keep this book on your desk as you write scripts, and you'll be able to find almost instantly the particular option you need.Most chapters consist of alphabetical listings. Since Tk and mega-widget packages break down commands by widget, the chapters on these topics are organized by widget along with a section of core commands where appropriate. Contents include: Core Tcl and Tk commands and Tk widgets C interface (prototypes) Expect [incr Tcl] and [incr Tk] Tix TclX BLT Oratcl, SybTcl, and Tclodbc |
| databricks coding interview questions: Optimized C++ Kurt Guntheroth, 2016-04-27 In today’s fast and competitive world, a program’s performance is just as important to customers as the features it provides. This practical guide teaches developers performance-tuning principles that enable optimization in C++. You’ll learn how to make code that already embodies best practices of C++ design run faster and consume fewer resources on any computer—whether it’s a watch, phone, workstation, supercomputer, or globe-spanning network of servers. Author Kurt Guntheroth provides several running examples that demonstrate how to apply these principles incrementally to improve existing code so it meets customer requirements for responsiveness and throughput. The advice in this book will prove itself the first time you hear a colleague exclaim, “Wow, that was fast. Who fixed something?” Locate performance hot spots using the profiler and software timers Learn to perform repeatable experiments to measure performance of code changes Optimize use of dynamically allocated variables Improve performance of hot loops and functions Speed up string handling functions Recognize efficient algorithms and optimization patterns Learn the strengths—and weaknesses—of C++ container classes View searching and sorting through an optimizer’s eye Make efficient use of C++ streaming I/O functions Use C++ thread-based concurrency features effectively |
| databricks coding interview questions: SQL Queries for Mere Mortals John L. Viescas, Michael James Hernandez, 2014 The #1 Easy, Common-Sense Guide to SQL Queries--Updated for Today's Databases, Standards, and Challenges SQL Queries for Mere Mortals ® has earned worldwide praise as the clearest, simplest tutorial on writing effective SQL queries. The authors have updated this hands-on classic to reflect new SQL standards and database applications and teach valuable new techniques. Step by step, John L. Viescas and Michael J. Hernandez guide you through creating reliable queries for virtually any modern SQL-based database. They demystify all aspects of SQL query writing, from simple data selection and filtering to joining multiple tables and modifying sets of data. Three brand-new chapters teach you how to solve a wide range of challenging SQL problems. You'll learn how to write queries that apply multiple complex conditions on one table, perform sophisticated logical evaluations, and think outside the box using unlinked tables. Coverage includes -- Getting started: understanding what relational databases are, and ensuring that your database structures are sound -- SQL basics: using SELECT statements, creating expressions, sorting information with ORDER BY, and filtering data using WHERE -- Summarizing and grouping data with GROUP BY and HAVING clauses -- Drawing data from multiple tables: using INNER JOIN, OUTER JOIN, and UNION operators, and working with subqueries -- Modifying data sets with UPDATE, INSERT, and DELETE statements Advanced queries: complex NOT and AND, conditions, if-then-else using CASE, unlinked tables, driver tables, and more Practice all you want with downloadable sample databases for today's versions of Microsoft Office Access, Microsoft SQL Server, and the open source MySQL database. Whether you're a DBA, developer, user, or student, there's no better way to master SQL. informit.com/aw forMereMortals.com |
| databricks coding interview questions: Kubernetes in Action Marko Luksa, 2017-12-14 Summary Kubernetes in Action is a comprehensive guide to effectively developing and running applications in a Kubernetes environment. Before diving into Kubernetes, the book gives an overview of container technologies like Docker, including how to build containers, so that even readers who haven't used these technologies before can get up and running. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Kubernetes is Greek for helmsman, your guide through unknown waters. The Kubernetes container orchestration system safely manages the structure and flow of a distributed application, organizing containers and services for maximum efficiency. Kubernetes serves as an operating system for your clusters, eliminating the need to factor the underlying network and server infrastructure into your designs. About the Book Kubernetes in Action teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of Docker and Kubernetes before building your first Kubernetes cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll explore high-value topics like monitoring, tuning, and scaling. What's Inside Kubernetes' internals Deploying containers across a cluster Securing clusters Updating applications with zero downtime About the Reader Written for intermediate software developers with little or no familiarity with Docker or container orchestration systems. About the Author Marko Luksa is an engineer at Red Hat working on Kubernetes and OpenShift. Table of Contents PART 1 - OVERVIEW Introducing Kubernetes First steps with Docker and Kubernetes PART 2 - CORE CONCEPTS Pods: running containers in Kubernetes Replication and other controllers: deploying managed pods Services: enabling clients to discover and talk to pods Volumes: attaching disk storage to containers ConfigMaps and Secrets: configuring applications Accessing pod metadata and other resources from applications Deployments: updating applications declaratively StatefulSets: deploying replicated stateful applications PART 3 - BEYOND THE BASICS Understanding Kubernetes internals Securing the Kubernetes API server Securing cluster nodes and the network Managing pods' computational resources Automatic scaling of pods and cluster nodes Advanced scheduling Best practices for developing apps Extending Kubernetes |
| databricks coding interview questions: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| databricks coding interview questions: Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies Reid Hoffman, Chris Yeh, 2018-10-09 Foreword by Bill Gates From the authors of New York Times bestsellers, The Alliance and The Start-up of You, comes a smart and accessible must-have guide for budding entrepreneurs everywhere. |
| databricks coding interview questions: SAS Certified Specialist Prep Guide SAS Institute, 2019-02-11 The SAS® Certified Specialist Prep Guide: Base Programming Using SAS® 9.4 prepares you to take the new SAS 9.4 Base Programming -- Performance-Based Exam. This is the official guide by the SAS Global Certification Program. This prep guide is for both new and experienced SAS users, and it covers all the objectives that are tested on the exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers for the chapter quizzes and solutions for the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS® glossary and a list of practice data sets. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting both data syntax and programming logic errors. All exam topics are covered in these chapters: Setting Up Practice Data Basic Concepts Accessing Your Data Creating SAS Data Sets Identifying and Correcting SAS Language Errors Creating Reports Understanding DATA Step Processing BY-Group Processing Creating and Managing Variables Combining SAS Data Sets Processing Data with DO Loops SAS Formats and Informats SAS Date, Time, and Datetime Values Using Functions to Manipulate Data Producing Descriptive Statistics Creating Output Practice Programming Scenarios (Workbook) |
| databricks coding interview questions: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized |
| databricks coding interview questions: Coding Interview Questions and Answers Chinmoy Mukherjee, 2017-03-10 Have you ever wondered, what is stopping you to get a better IT job? It is just your lack of time to prepare for interview. Many interview materials are available in internet in scattered form, gathering them together and preparing for interview is a humongous task. I wrote this “Coding Interview Questions and Answers” book to solve this problem We present 240 challenging data structures, algorithm, code optimization, java, database and C programming interview questions and answers for IT professionals to practice. The reader is encouraged to solve the problem himself/herself before checking the answers. Sample “Coding Interview Questions and Answers” can be downloaded from the website http://crackingthecodinginterview.in/ |
| databricks coding interview questions: Parallel and Concurrent Programming in Haskell Simon Marlow, 2013-07-12 If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval monad and Evaluation Strategies Parallelize ordinary Haskell code with the Par monad Build parallel array-based computations, using the Repa library Use the Accelerate library to run computations directly on the GPU Work with basic interfaces for writing concurrent code Build trees of threads for larger and more complex programs Learn how to build high-speed concurrent network servers Write distributed programs that run on multiple machines in a network |
| databricks coding interview questions: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| databricks coding interview questions: T-SQL Window Functions Itzik Ben-Gan, 2019-10-18 Use window functions to write simpler, better, more efficient T-SQL queries Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You’ll find a detailed section on optimization, plus an extensive collection of business solutions — including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: • Use window functions to improve queries you previously built with predicates • Master essential SQL windowing concepts, and efficiently design window functions • Effectively utilize partitioning, ordering, and framing • Gain practical in-depth insight into window aggregate, ranking, offset, and statistical functions • Understand how the SQL standard supports ordered set functions, and find working solutions for functions not yet available in the language • Preview advanced Row Pattern Recognition (RPR) data analysis techniques • Optimize window functions in SQL Server and Azure SQL Database, making the most of indexing, parallelism, and more • Discover a full library of window function solutions for common business problems About This Book • For developers, DBAs, data analysts, data scientists, BI professionals, and power users familiar with T-SQL queries • Addresses any edition of the SQL Server 2019 database engine or later, as well as Azure SQL Database Get all code samples at: MicrosoftPressStore.com/TSQLWindowFunctions/downloads |
| databricks coding interview questions: OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide Jeanne Boyarsky, Scott Selikoff, 2019-11-19 This OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide: Exam 1Z0-815 and the Programmer II Study Guide: Exam 1Z0-816 were published before Oracle announced major changes to its OCP certification program and the release of the new Developer 1Z0-819 exam. No matter the changes, rest assured both of the Programmer I and II Study Guides cover everything you need to prepare for and take Exam 1Z0-819. If you’ve purchased one of the Programmer Study Guides, purchase the other one and you’ll be all set. NOTE: The OCP Java SE 11 Programmer I Exam 1Z0-815 and Programmer II Exam 1Z0-816 have been retired (as of October 1, 2020), and Oracle has released a new Developer Exam 1Z0-819 to replace the previous exams. The Upgrade Exam 1Z0-817 remains the same. The comprehensive study aide for those preparing for the new Oracle Certified Professional Java SE Programmer I Exam 1Z0-815 Used primarily in mobile and desktop application development, Java is a platform-independent, object-oriented programming language. It is the principal language used in Android application development as well as a popular language for client-side cloud applications. Oracle has updated its Java Programmer certification tracks for Oracle Certified Professional. OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide covers 100% of the exam objectives, ensuring that you are thoroughly prepared for this challenging certification exam. This comprehensive, in-depth study guide helps you develop the functional-programming knowledge required to pass the exam and earn certification. All vital topics are covered, including Java building blocks, operators and loops, String and StringBuilder, Array and ArrayList, and more. Included is access to Sybex's superior online interactive learning environment and test bank—containing self-assessment tests, chapter tests, bonus practice exam questions, electronic flashcards, and a searchable glossary of important terms. This indispensable guide: Clarifies complex material and strengthens your comprehension and retention of key topics Covers all exam objectives such as methods and encapsulation, exceptions, inheriting abstract classes and interfaces, and Java 8 Dates and Lambda Expressions Explains object-oriented design principles and patterns Helps you master the fundamentals of functional programming Enables you to create Java solutions applicable to real-world scenarios There are over 9 millions developers using Java around the world, yet hiring managers face challenges filling open positions with qualified candidates. The OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide will help you take the next step in your career. |
| databricks coding interview questions: Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, 2015-04-02 In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder |
| databricks coding interview questions: Database Internals Alex Petrov, 2019-09-13 When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals. Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed. This book examines: Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency |
| databricks coding interview questions: Artificial Intelligence with Python Cookbook Ben Auffarth, 2020-10-30 Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book. |
| databricks coding interview questions: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| databricks coding interview questions: System Design Interview - An Insider's Guide Alex Xu, 2020-06-12 The system design interview is considered to be the most complex and most difficult technical job interview by many. Those questions are intimidating, but don't worry. It's just that nobody has taken the time to prepare you systematically. We take the time. We go slow. We draw lots of diagrams and use lots of examples. You'll learn step-by-step, one question at a time.Don't miss out.What's inside?- An insider's take on what interviewers really look for and why.- A 4-step framework for solving any system design interview question.- 16 real system design interview questions with detailed solutions.- 188 diagrams to visually explain how different systems work. |
| databricks coding interview questions: The Site Reliability Workbook Betsy Beyer, Niall Richard Murphy, David K. Rensin, Kent Kawahara, Stephen Thorne, 2018-07-25 In 2016, Googleâ??s Site Reliability Engineering book ignited an industry discussion on what it means to run production services todayâ??and why reliability considerations are fundamental to service design. Now, Google engineers who worked on that bestseller introduce The Site Reliability Workbook, a hands-on companion that uses concrete examples to show you how to put SRE principles and practices to work in your environment. This new workbook not only combines practical examples from Googleâ??s experiences, but also provides case studies from Googleâ??s Cloud Platform customers who underwent this journey. Evernote, The Home Depot, The New York Times, and other companies outline hard-won experiences of what worked for them and what didnâ??t. Dive into this workbook and learn how to flesh out your own SRE practice, no matter what size your company is. Youâ??ll learn: How to run reliable services in environments you donâ??t completely controlâ??like cloud Practical applications of how to create, monitor, and run your services via Service Level Objectives How to convert existing ops teams to SREâ??including how to dig out of operational overload Methods for starting SRE from either greenfield or brownfield |
| databricks coding interview questions: Learning Spark Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, 2015-01-28 Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables |
| databricks coding interview questions: Machine Learning for Time-Series with Python Ben Auffarth, 2021-10-29 Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is for This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book. |
| databricks coding interview questions: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 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. -- |
| databricks coding interview questions: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| databricks coding interview questions: Hadoop Operations Eric Sammer, 2012-09-26 If you’ve been asked to maintain large and complex Hadoop clusters, this book is a must. Demand for operations-specific material has skyrocketed now that Hadoop is becoming the de facto standard for truly large-scale data processing in the data center. Eric Sammer, Principal Solution Architect at Cloudera, shows you the particulars of running Hadoop in production, from planning, installing, and configuring the system to providing ongoing maintenance. Rather than run through all possible scenarios, this pragmatic operations guide calls out what works, as demonstrated in critical deployments. Get a high-level overview of HDFS and MapReduce: why they exist and how they work Plan a Hadoop deployment, from hardware and OS selection to network requirements Learn setup and configuration details with a list of critical properties Manage resources by sharing a cluster across multiple groups Get a runbook of the most common cluster maintenance tasks Monitor Hadoop clusters—and learn troubleshooting with the help of real-world war stories Use basic tools and techniques to handle backup and catastrophic failure |
| databricks coding interview questions: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| databricks coding interview questions: Terraform: Up & Running Yevgeniy Brikman, 2019-09-06 Terraform has become a key player in the DevOps world for defining, launching, and managing infrastructure as code (IaC) across a variety of cloud and virtualization platforms, including AWS, Google Cloud, Azure, and more. This hands-on second edition, expanded and thoroughly updated for Terraform version 0.12 and beyond, shows you the fastest way to get up and running. Gruntwork cofounder Yevgeniy (Jim) Brikman walks you through code examples that demonstrate Terraform’s simple, declarative programming language for deploying and managing infrastructure with a few commands. Veteran sysadmins, DevOps engineers, and novice developers will quickly go from Terraform basics to running a full stack that can support a massive amount of traffic and a large team of developers. Explore changes from Terraform 0.9 through 0.12, including backends, workspaces, and first-class expressions Learn how to write production-grade Terraform modules Dive into manual and automated testing for Terraform code Compare Terraform to Chef, Puppet, Ansible, CloudFormation, and Salt Stack Deploy server clusters, load balancers, and databases Use Terraform to manage the state of your infrastructure Create reusable infrastructure with Terraform modules Use advanced Terraform syntax to achieve zero-downtime deployment |
| databricks coding interview questions: SAS Certified Professional Prep Guide SAS Institute, 2019-10-18 The official guide by the SAS Global Certification Program, SAS Certified Professional Prep Guide: Advanced Programming Using SAS 9.4 prepares you to take the new SAS 9.4 Advanced Programming Performance-Based Exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers to the chapter quizzes and solutions to the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS Glossary and a list of practice data sets. Major topics include SQL processing, SAS macro language processing, and advanced SAS programming techniques. All exam topics are covered in the following chapters: SQL Processing with SAS PROC SQL Fundamentals Creating and Managing Tables Joining Tables Using PROC SQL Joining Tables Using Set Operators Using Subqueries Advanced SQL Techniques SAS Macro Language Processing Creating and Using Macro Variables Storing and Processing Text Working with Macro Programs Advanced Macro Techniques Advanced SAS Programming Techniques Defining and Processing Arrays Processing Data Using Hash Objects Using SAS Utility Procedures Using Advanced Functions Practice Programming Scenarios (Workbook) |
| databricks coding interview questions: Creating a Self-Directed Learning Environment Greg Mullen, 2019-11-14 Educate the whole child—improve the whole school. Implementing evidence-based and innovative teaching practices can feel like juggling: If you have standards-based learning in one hand and social-emotional learning in the other, what do you do with cognitive development? This book shows you how to balance all 3, combining these concepts into manageable, realistic plans for success. In clear, easy-to-follow language, master teacher and educational expert Greg Mullen introduces a flexible, three-tiered, visual framework designed for schoolwide collaboration. He also offers: • An integrated philosophy focused on self-directed learning and the whole child • Research sourced from CASEL and state programs and initiatives • Attention to academic inclusion, behavior intervention, and classroom management • Numerous illustrations, tables, and graphics • Tools and supplemental resources for implementation Make innovation work for your school. With this guide, you and your colleagues will build on your strengths, discover the potential of your existing programs, and implement smart changes that make a real difference for students. |
| databricks coding interview questions: The Handbook of Research Synthesis and Meta-Analysis Harris Cooper, Larry V. Hedges, Jeffrey C. Valentine, 2019-06-14 Research synthesis is the practice of systematically distilling and integrating data from many studies in order to draw more reliable conclusions about a given research issue. When the first edition of The Handbook of Research Synthesis and Meta-Analysis was published in 1994, it quickly became the definitive reference for conducting meta-analyses in both the social and behavioral sciences. In the third edition, editors Harris Cooper, Larry Hedges, and Jeff Valentine present updated versions of classic chapters and add new sections that evaluate cutting-edge developments in the field. The Handbook of Research Synthesis and Meta-Analysis draws upon groundbreaking advances that have transformed research synthesis from a narrative craft into an important scientific process in its own right. The editors and leading scholars guide the reader through every stage of the research synthesis process—problem formulation, literature search and evaluation, statistical integration, and report preparation. The Handbook incorporates state-of-the-art techniques from all quantitative synthesis traditions and distills a vast literature to explain the most effective solutions to the problems of quantitative data integration. Among the statistical issues addressed are the synthesis of non-independent data sets, fixed and random effects methods, the performance of sensitivity analyses and model assessments, the development of machine-based abstract screening, the increased use of meta-regression and the problems of missing data. The Handbook also addresses the non-statistical aspects of research synthesis, including searching the literature and developing schemes for gathering information from study reports. Those engaged in research synthesis will find useful advice on how tables, graphs, and narration can foster communication of the results of research syntheses. The third edition of the Handbook provides comprehensive instruction in the skills necessary to conduct research syntheses and represents the premier text on research synthesis. Praise for the first edition: The Handbook is a comprehensive treatment of literature synthesis and provides practical advice for anyone deep in the throes of, just teetering on the brink of, or attempting to decipher a meta-analysis. Given the expanding application and importance of literature synthesis, understanding both its strengths and weaknesses is essential for its practitioners and consumers. This volume is a good beginning for those who wish to gain that understanding. —Chance Meta-analysis, as the statistical analysis of a large collection of results from individual studies is called, has now achieved a status of respectability in medicine. This respectability, when combined with the slight hint of mystique that sometimes surrounds meta-analysis, ensures that results of studies that use it are treated with the respect they deserve....The Handbook of Research Synthesis is one of the most important publications in this subject both as a definitive reference book and a practical manual.—British Medical Journal When the first edition of The Handbook of Research Synthesis was published in 1994, it quickly became the definitive reference for researchers conducting meta-analyses of existing research in both the social and biological sciences. In this fully revised second edition, editors Harris Cooper, Larry Hedges, and Jeff Valentine present updated versions of the Handbook's classic chapters, as well as entirely new sections reporting on the most recent, cutting-edge developments in the field. Research synthesis is the practice of systematically distilling and integrating data from a variety of sources in order to draw more reliable conclusions about a given question or topic. The Handbook of Research Synthesis and Meta-Analysis draws upon years of groundbreaking advances that have transformed research synthesis from a narrative craft into an important scientific process in its own right. Cooper, Hedges, and Valentine have assembled leading authorities in the field to guide the reader through every stage of the research synthesis process—problem formulation, literature search and evaluation, statistical integration, and report preparation. The Handbook of Research Synthesis and Meta-Analysis incorporates state-of-the-art techniques from all quantitative synthesis traditions. Distilling a vast technical literature and many informal sources, the Handbook provides a portfolio of the most effective solutions to the problems of quantitative data integration. Among the statistical issues addressed by the authors are the synthesis of non-independent data sets, fixed and random effects methods, the performance of sensitivity analyses and model assessments, and the problem of missing data. The Handbook of Research Synthesis and Meta-Analysis also provides a rich treatment of the non-statistical aspects of research synthesis. Topics include searching the literature, and developing schemes for gathering information from study reports. Those engaged in research synthesis will also find useful advice on how tables, graphs, and narration can be used to provide the most meaningful communication of the results of research synthesis. In addition, the editors address the potentials and limitations of research synthesis, and its future directions. The past decade has been a period of enormous growth in the field of research synthesis. The second edition Handbook thoroughly revises original chapters to assure that the volume remains the most authoritative source of information for researchers undertaking meta-analysis today. In response to the increasing use of research synthesis in the formation of public policy, the second edition includes a new chapter on both the strengths and limitations of research synthesis in policy debates |
| databricks coding interview questions: Rebooting AI Gary Marcus, Ernest Davis, 2019-09-10 Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a robust artificial intelligence that can make our lives better. “Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough.” —Garry Kasparov, former world chess champion and author of Deep Thinking Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. |
| databricks coding interview questions: Cracking the PM Interview Gayle Laakmann McDowell, Jackie Bavaro, 2013 How many pizzas are delivered in Manhattan? How do you design an alarm clock for the blind? What is your favorite piece of software and why? How would you launch a video rental service in India? This book will teach you how to answer these questions and more. Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named PM (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important pitch. |
| databricks coding interview questions: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
Databricks: Leading Data and AI Solutions for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the …
What is Databricks? | Databricks Documentation
May 5, 2025 · What is Databricks? Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI …
About Databricks: The data and AI company
Headquartered in San Francisco, with offices around the world, Databricks is on a mission to simplify and democratize data and AI, helping data and AI teams solve the …
Learn Databricks - Training & Resources | Databricks
Explore Databricks resources for data and AI, including training, certification, events, and community support to enhance your skills.
データとAIの企業：未来をリードするデータインテリジェンスプラット …
Databricks のデータプラットフォームは、ETL、データの取り込み、BI、AI、ガバナンスのための現行ツールと統合します。 有効なツールはそのままで、新たなツールを採用できます。
Databricks: Leading Data and AI Solutions for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform.
What is Databricks? | Databricks Documentation
May 5, 2025 · What is Databricks? Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. …
About Databricks: The data and AI company
Headquartered in San Francisco, with offices around the world, Databricks is on a mission to simplify and democratize data and AI, helping data and AI teams solve the world’s toughest …
Learn Databricks - Training & Resources | Databricks
Explore Databricks resources for data and AI, including training, certification, events, and community support to enhance your skills.
データとAIの企業：未来をリードするデータインテリジェンスプ …
Databricks のデータプラットフォームは、ETL、データの取り込み、BI、AI、ガバナンスのための現行ツールと統合します。 有効なツールはそのままで、新たなツールを採用できます。
Databricks IQ: AI-Driven Analytics for Faster Data Insights
Databricks IQ powers AI-driven analytics to help you derive faster insights, optimize decision-making, and scale your data analytics workflows with ease.
Databricks components | Databricks Documentation
Learn fundamental Databricks components such as workspaces, data objects, clusters, machine learning models, and access.
Data Lakehouse Architecture - Databricks
The Databricks Data Intelligence Platform is built on lakehouse architecture, which combines the best elements of data lakes and data warehouses to help you reduce costs and deliver on …
Get started tutorials on Databricks
May 13, 2025 · Build a machine learning classification model using the scikit-learn library on Databricks to predict whether a wine is considered “high-quality”. This tutorial also illustrates …
Data Science with Databricks Platform | Databricks
Write code in Python, R, Scala and SQL, explore data with interactive visualizations and discover new insights with Databricks Notebooks. Confidently and securely share code with …
