Best Database books
Best Database books
1-Database System Concepts 6th Edition1-Database System Concepts 6th Edition1-Database System Concepts 6th Edition
Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 6th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.
The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.
https://www.amazon.com/Database-System-Concepts-Abraham-Silberschatz/dp/0073523321/
2-Readings in Database Systems (MIT Press)
Lessons from database research have been applied in academic fields ranging from bioinformatics to next-generation Internet architecture and in industrial uses including Web-based e-commerce and search engines. The core ideas in the field have become increasingly influential. This text provides both students and professionals with a grounding in database research and a technical context for understanding recent innovations in the field. The readings included treat the most important issues in the database area -- the basic material for any DBMS professional.This fourth edition has been substantially updated and revised, with 21 of the 48 papers new to the edition, four of them published for the first time. Many of the sections have been newly organized, and each section includes a new or substantially revised introduction that discusses the context, motivation, and controversies in a particular area, placing it in the broader perspective of database research. Two introductory articles, never before published, provide an organized, current introduction to basic knowledge of the field; one discusses the history of data models and query languages and the other offers an architectural overview of a database system. The remaining articles range from the classical literature on database research to treatments of current hot topics, including a paper on search engine architecture and a paper on application servers, both written expressly for this edition. The result is a collection of papers that are seminal and also accessible to a reader who has a basic familiarity with database systems.
https://www.amazon.com/Readings-Database-Systems-Joseph-Hellerstein/dp/0262693143
3-Database Management Systems
Database Management Systems provides comprehensive and up-to-date coverage of the fundamentals of database systems. Coherent explanations and practical examples have made this one of the leading texts in the field. The third edition continues in this tradition, enhancing it with more practical material.
The new edition has been reorganized to allow more flexibi
lity in the way the course is taught. Now, instructors can easily choose whether they would like to teach a course which emphasizes database application development or a course that emphasizes database systems issues. New overview chapters at the beginning of parts make it possible to skip other chapters in the part if you don't want the detail.
More applications and examples have been added throughout the book, including SQL and Oracle examples. The applied flavor is further enhanced by the two new database applications chapters.
https://www.amazon.com/Database-Management-Systems-3rd-Edition/dp/0072465638/
4-Fundamentals of Database Systems
Fundamentals of Database Systems has become the world-wide leading textbook because it combines clear explanations of theory and design, broad coverage of models and real systems, and excellent examples with up-to-date introductions and modern database technologies. This book has been revised and updated to reflect the latest trends in technological and application development. This fourth edition expands on many of the most popular database topics, including SQL, security, and data mining along with an introduction to UML modeling and an entirely new chapter on XML and Internet databases.
https://www.amazon.com/dp/0321122267/?tag=stackoverfl08-20
5-An Introduction to Database Systems
The Seventh Edition continues to focus on the hallmark feature of its previous editions: providing a solid grounding in the foundations of database technology and shedding some light on how the field is likely to develop in the future. This comprehensive introduction to databases has been thoroughly revised to reflect the latest developments and advances in the field of database systems. Emphasizing insight and understanding rather than formalism, Chris Date has divided the book into six parts: Basic Concepts, The Relational Model, Database Design, Transaction Management, Further Topics, and Object and Object/Relational Databases. Throughout the book, there are numerous worked examples and exercises for the reader--with selected answers--as well as an extensive set of annotated references. The release of this new edition of An Introduction to Database Systems coincides with the 25th Anniversary of its initial publication.
6-Database Systems: Design, Implementation, & Management 11th Edition
Practical and easy to understand, DATABASE SYSTEMS: DESIGN, IMPLEMENTATION, AND MANAGEMENT, Eleventh Edition, gives students a solid foundation in database design and implementation. Filled with visual aids such as diagrams, illustrations, and tables, this market-leading text provides in-depth coverage of database design, demonstrating that the key to successful database implementation is in proper design of databases to fit within a larger strategic view of the data environment. Renowned for its clear, straightforward writing style, this text provides students with an outstanding balance of theory and practice. The eleventh edition has been updated to include expanded relational algebra coverage, updated business vignettes showing the impact of database tech in the real world, updated coverage of cloud data services, expanded coverage of Big Data and related Hadoop technologies, SQL coverage expanded to include MySQL databases, and many other improvements! In addition, new review questions, problem sets, and cases have been added throughout the book so that students have multiple opportunities to test their understanding and develop real and useful design skills.
Best Books in Sql
SQL For Dummies
Uncover the secrets of SQL and start building better relational databases today!
This fun and friendly guide will help you demystify database management systems so you can create more powerful databases and access information with ease. Updated for the latest SQL functionality, SQL For Dummies, 8th Edition covers the core SQL language and shows you how to use SQL to structure a DBMS, implement a database design, secure your data, and retrieve information when you need it.
- Includes new enhancements of SQL:2011, including temporal data functionality which allows you to set valid times for transactions to occur and helps prevent database corruption
- Covers creating, accessing, manipulating, maintaining, and storing information in relational database management systems like Access, Oracle, SQL Server, and MySQL
- Provides tips for keeping your data safe from theft, accidental or malicious corruption, or loss due to equipment failures and advice on eliminating errors in your work
Don't be daunted by database development anymore - get SQL For Dummies, 8th Edition, and you'll be on your way to SQL stardom.
SQL in 24 Hours, Sams Teach Yourself
In just 24 sessions of one hour or less, you’ll learn how to use SQL to build effective databases, efficiently retrieve your data, and manage everything from performance to security! Using this book’s straightforward, step-by-step approach, you’ll learn hands-on through practical examples. Each lesson builds on what you’ve already learned, giving you a strong real-world foundation for success. The authors guide you from the absolute basics to advanced techniques—including views, transactions, Web data publishing, and even powerful SQL extensions for Oracle and Microsoft SQL Server!
Step-by-step instructions carefully walk you through the most common SQL tasks.
Quizzes and Exercises at the end of each chapter help you test your knowledge.
By the Way notes present interesting information related to the discussion.
Did You Know? tips offer advice or show you easier ways to perform tasks.
Watch Out! cautions alert you to possible problems and give you advice on how to avoid them.
SQL in 10 Minutes, Sams Teach Yourself
Sams Teach Yourself SQL in 10 Minutes, Fourth Edition
New full-color code examples help you see how SQL statements are structured
Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job.
Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints.
You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete.
With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language.
10 minutes is all you need to learn how to...
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Use the major SQL statements
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Construct complex SQL statements using multiple clauses and operators
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Retrieve, sort, and format database contents
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Pinpoint the data you need using a variety of filtering techniques
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Use aggregate functions to summarize data
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Join two or more related tables
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Insert, update, and delete data
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Create and alter database tables
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Work with views, stored procedures, and more
SQL: The Ultimate Guide From Beginner To Expert - Learn And Master SQL In No Time! (2016 Edition)
Become An SQL Expert In Few Easy Steps
Learn the most useful tips by following this easy handbook!
Are you looking for more ways on how to improve your SQL skill? Do you want to see your database in an organised fashion?
Well if you answered “yes”, then read on!
Structured Query Language or SQL is famously used to interact with database. You may be working at a hotel, bank, government, or any industry, SQL is certainly a necessity to run your daily operations!
SQL: The Ultimate Guide From Beginner To Expert – Learn And Master SQL In No Time! is a book that will introduce you to a computer language that has helped so many business owners and information technologists with daily reports and database management without the fear of crashing.
Here’s what you’ll find inside:
- SQL Basics
- SQL and Data
- Data Functions (Aggregate, Rowset, and Ranking)
- Scalar Functions
- Basics of Building a Table Using SQL
- Recommendations
SQL Cookbook Query Solutions and Techniques for Database Developers
You know the rudiments of the SQL query language, yet you feel you aren't taking full advantage of SQL's expressive power. You'd like to learn how to do more work with SQL inside the database before pushing data across the network to your applications. You'd like to take your SQL skills to the next level.
Let's face it, SQL is a deceptively simple language to learn, and many database developers never go far beyond the simple statement: SELECT columns FROM table WHERE conditions. But there is so much more you can do with the language. In the SQL Cookbook, experienced SQL developer Anthony Molinaro shares his favorite SQL techniques and features. You'll learn about:
- Window functions, arguably the most significant enhancement to SQL in the past decade. If you're not using these, you're missing out
- Powerful, database-specific features such as SQL Server's PIVOT and UNPIVOT operators, Oracle's MODEL clause, and PostgreSQL's very useful GENERATE_SERIES function
- Pivoting rows into columns, reverse-pivoting columns into rows, using pivoting to facilitate inter-row calculations, and double-pivoting a result set
- Bucketization, and why you should never use that term in Brooklyn.
- How to create histograms, summarize data into buckets, perform aggregations over a moving range of values, generate running-totals and subtotals, and other advanced, data warehousing techniques
- The technique of walking a string, which allows you to use SQL to parse through the characters, words, or delimited elements of a string
Written in O'Reilly's popular Problem/Solution/Discussion style, the SQL Cookbook is sure to please. Anthony's credo is: "When it comes down to it, we all go to work, we all have bills to pay, and we all want to go home at a reasonable time and enjoy what's still available of our days." The SQL Cookbook moves quickly from problem to solution, saving you time each step of the way.
Learning SQL
Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals.
Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will:
- Move quickly through SQL basics and learn several advanced features
- Use SQL data statements to generate, manipulate, and retrieve data
- Create database objects, such as tables, indexes, and constraints, using SQL schema statements
- Learn how data sets interact with queries, and understand the importance of subqueries
- Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements
Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work.
Big Data
The Definition of Big Data
What exactly is big data?
To really understand big data, it’s helpful to have some historical background. Here is Gartner’s definition, circa 2001 (which is still the go-to definition): Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.
Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
The Three Vs of Big Data
Volume |
The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a webpage or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes. |
Velocity |
Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action. |
Variety |
Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata. |
The Value—and Truth—of Big Data
Two more Vs have emerged over the past few years: value and veracity.
Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?
Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.
Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.
Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.
The History of Big Data
Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started with the first data centers and the development of the relational database.
Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.
The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.
While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data.
Benefits of Big Data and Data Analytics:
- Big data makes it possible for you to gain more complete answers because you have more information.
- More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.
Big Data Use Cases
Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few. (More use cases can be found at Oracle Big Data Solutions.)
Product Development |
Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products. |
Predictive Maintenance |
Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime. |
Customer Experience |
The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively. |
Fraud and Compliance |
When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster. |
Machine Learning |
Machine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible. |
Operational Efficiency |
Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand. |
Drive Innovation |
Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities. |
Big Data Challenges
While big data holds a lot of promise, it is not without its challenges.
First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.
But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.
Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
How Big Data Works
Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:
1. Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as ETL (extract, transform, and load) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.
During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.
2. Manage
Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.
3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.
Big Data Best Practices
To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.
Align Big Data with Specific Business Goals |
More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data. |
Ease Skills Shortage with Standards and Governance |
One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms. |
Optimize Knowledge Transfer with a Center of Excellence |
Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way. |
Top Payoff Is Aligning Unstructured with Structured Data |
It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today. Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture. Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries. |
Plan Your Discovery Lab for Performance |
Discovering meaning in your data is not always straightforward. Sometimes we don’t even know what we’re looking for. That’s expected. Management and IT needs to support this “lack of direction” or “lack of clear requirement.” At the same time, it’s important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need—and are properly governed. |
Align with the Cloud Operating Model |
Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.
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