Data in Database

Start Date: 02/16/2020

Course Type: Common Course

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About Course

Big Data analytics tools are increasingly critical for providing meaningful information for making better business decisions. Big data technologies bring significant cost advantages when it comes to storing and managing large amounts of data. Understanding how to query a database to extract data will empower better analysis of large, complex datasets. Knowledge of Indexing mechanisms makes possible high-speed, selective retrieval of large amounts of information.

Course Syllabus

Big Data and Data Processing
Entity Relationship Model to Relational Model
Relational Model and Relational Algebra
Data Storage and Indexing

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Course Introduction

Data in Database Systems This course introduces the vast majority of the techniques used to design, develop, troubleshoot, and extend the life of a computer system. A very high-level view of what's happening in your computer system will get you up and running with a variety of tools to deal with any kind of data problem you may have. We'll cover everything from basic data structures and query optimization to writing more powerful SQL statements and how to avoid repetition in your code. You'll learn the basic techniques used to build secure and reliable systems. We'll also look at the specialized topics in database design, including object-relational mapping (ORM), data federation, and transactional memory (TTL). Learning Outcomes By taking this course, you'll come to be familiar with a broad range of data structures, query optimizers, and SQL statements, and will be able to write more complex queries. You'll also come to be familiar with techniques for troubleshooting queries, including writing more advanced queries, and will be able to write more sophisticated code for queries. Recommended background You should be comfortable writing query optimizers and have some familiarity with C++. You should be comfortable reading and understanding machine language, and have a basic understanding of Python. You should also have a good understanding of data types and object-relational mapping (ORM).Introduction to Query Optimization Object-Relational Mapping (ORM) Query Optimization Data Types and

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Article Example
Database Having produced a conceptual data model that users are happy with, the next stage is to translate this into a schema that implements the relevant data structures within the database. This process is often called logical database design, and the output is a logical data model expressed in the form of a schema. Whereas the conceptual data model is (in theory at least) independent of the choice of database technology, the logical data model will be expressed in terms of a particular database model supported by the chosen DBMS. (The terms "data model" and "database model" are often used interchangeably, but in this article we use "data model" for the design of a specific database, and "database model" for the modelling notation used to express that design.)
Database A database management system provides three views of the database data:
Database When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.
Oracle Database Every Oracle database has one or more physical datafiles, which contain all the database data. The data of logical database structures, such as tables and indexes, is physically stored in the datafiles allocated for a database.
Database Database storage is the container of the physical materialization of a database. It comprises the "internal" (physical) "level" in the database architecture. It also contains all the information needed (e.g., metadata, "data about the data", and internal data structures) to reconstruct the "conceptual level" and "external level" from the internal level when needed. Putting data into permanent storage is generally the responsibility of the database engine a.k.a. "storage engine". Though typically accessed by a DBMS through the underlying operating system (and often utilizing the operating systems' file systems as intermediates for storage layout), storage properties and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators. A DBMS, while in operation, always has its database residing in several types of storage (e.g., memory and external storage). The database data and the additional needed information, possibly in very large amounts, are coded into bits. Data typically reside in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that attempt to optimize (the best possible) these levels' reconstruction when needed by users and programs, as well as for computing additional types of needed information from the data (e.g., when querying the database).
Database Producing the conceptual data model sometimes involves input from business processes, or the analysis of workflow in the organization. This can help to establish what information is needed in the database, and what can be left out. For example, it can help when deciding whether the database needs to hold historic data as well as current data.
Database A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated. The most popular example of a database model is the relational model (or the SQL approximation of relational), which uses a table-based format.
In-database processing Traditional approaches to data analysis require data to be moved out of the database into a separate analytics environment for processing, and then back to the database. (SPSS from IBM are examples of tools that still do this today). Doing the analysis in the database, where the data resides, eliminates the costs, time and security issues associated with the old approach by doing the processing in the data warehouse itself.
Database administration and automation "Every database requires a database owner account that can perform all schema management operations. This account is specific to the database and cannot log in to Data Director. You can add database owner accounts after database creation. Data Director users must log in with their database-specific credentials to view the database, its entities, and its data or to perform database management tasks.
NoorderSoft Waterways Database The NoorderSoft Waterways Database is a georelational database that contains all data on inland waterways in Europe, the United States and many other parts of the world. The database is maintained in order to allow logistic calculations and planning processes for inland waterways transport, inland shipping and leisure boating. The database contains dimension data, distance data, communication data and operational data.
Negative database A negative database is a kind of database that contains huge amount of data consisting of simulating data.
Oracle Database Data in a datafile is read, as needed, during normal database operation and stored in the memory cache of Oracle Database. For example, if a user wants to access some data in a table of a database, and if the requested information is not already in the memory cache for the database, then it is read from the appropriate datafiles and stored in memory.
In-memory database An in-memory database (IMDB, also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. Main memory databases are faster than disk-optimized databases because disk access is slower than memory access, the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk.
Data model Data modeling in software engineering is the process of creating a data model by applying formal data model descriptions using data modeling techniques. Data modeling is a technique for defining business requirements for a database. It is sometimes called "database modeling" because a data model is eventually implemented in a database.
Voter database Personal data frequently included in a voter database:
Cost database The cost database may be stored in a relational database management system, which may be in either an open or proprietary format, serving the data to the cost estimating software. The cost database may be hosted in the cloud. Estimators use a cost database to store data in structured way which is easy to manage and retrieve.
Database Database transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).
Database design Database design is the process of producing a detailed data model of database. This data model contains all the needed logical and physical design choices and physical storage parameters needed to generate a design in a data definition language, which can then be used to create a database. A fully attributed data model contains detailed attributes for each entity.
Database Database languages are specific to a particular data model.Notable examples include:
Database Occasionally a database employs storage redundancy by database objects replication (with one or more copies) to increase data availability (both to improve performance of simultaneous multiple end-user accesses to a same database object, and to provide resiliency in a case of partial failure of a distributed database). Updates of a replicated object need to be synchronized across the object copies. In many cases, the entire database is replicated.