Database Management 101

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An important tool for organizations to ensure their data is stored, validated, and protected is database management. It is used to develop and deploy processes that end users are comfortable with. They can feel confident that the data is easy to work with, reliable, and accessible. It is important to make sure your organization’s data is processed quickly and efficiently. 

What Is Database Management?

To maximize the value of their data, businesses rely on database management. Managing data requires planning, designing, and organizing the data that is stored in the database. A database management system (DBMS) is used to control the database and process its data. The database management system and the associated software are often referred to as a “database system,” and sometimes is simply abbreviated to “database.”

What Is a Database?

Data is organized in the database. A database server is the hardware that supports the software needed for a fully functional database. The database itself is a collection of data that is designed to be accessed and managed easily. Computer databases normally store collections of data containing information such as sales transactions, customer identification, and product details.

A database provides a data storage system and basic instructions on how to handle the data.

Small databases can offer storage using a file system, but large databases typically use multiple computers (computer clusters) or cloud storage. There are a wide variety of databases available to choose from – SQL, NoSQL, graph, MySQL, etc. The process of choosing a database system includes both practical considerations and formal techniques.

What Is a Database Management System?

A database management system (DBMS) is based on the software that is used to coordinate user commands and applications with the database. It allows users to gather, process, and analyze the data. The DBMS software also coordinates with the core instructions of the database. A DBMS can be built around a custom-designed multitasking kernel (this had been a common solution), but generally modern DBMSs use a standard operating system.

DBMSs use fairly complex software that can send the data to secondary storage devices, and can be used to manipulate or alter databases.

What Tools Are Used with Database Management?

There are several different kinds of databases and management systems. Each unique DBMS can be supported by a variety of useful tools. The commonly used DMBSs are listed below, along with a few of the many database tools currently available.

MySQL DBMS: This is an open-source relational database, and very easy to use. It has been described as an extremely useful database and is used by high-volume websites such as YouTube, Google, LinkedIn, Twitter, and PayPal. 

The MySQL DMBS organizes data into one or more data tables. These relations help in structuring the data. MySQL is an operating system that can be used with different programs to support a database, manage users, and network access. (Many tools are not open source.)

  • Toad Edge for MySQL: This is a useful toolset designed for database administration and development. It provides an SQL query monitor, schema comparison and synchronization, database snapshot creation, and an advanced JSON editor. Toad Edge can be used with Mac OS X and Windows.
  • dbForge Studio for MySQL: A multi-purpose tool that supports a broad range of features and functions. dbForge Studio for MySQL will support queries, develop and improve stored routines, and automate database object management. 
  • MySQL Workbench: A unified visual tool for database developers and database administrators. MySQL workbench supports designing, developing, and managing databases. There are three options: Community, Standard, and Enterprise. The Community version is open-source and free, and the other two are paid-for commercial versions with more functionality. MySQL Workbench works with Windows, Mac OS X.

SQL DBMS: This is the most popular DBMS available since the late 1970s. It stores data in columns and tables, and links between the tables are called “relationships.” Its primary strengths are that it is very familiar, easy to use, and reasonably efficient. It is a mature model and works with an ever-increasing number of apps useful for doing business.  

SQL DMBSs will normally accept only a few types of data formats and have limited storage. Some tools used for SQL database management are: 

  • Idera DM: This platform offers several management and analysis tools and can help with auditing sensitive data, as well identifying existing vulnerabilities. (Idera DM also comes with a standalone mobile app.)
  • SQL Power Tools: Provides tools for auditing sensitive data and identifying vulnerabilities in SQL servers. It claims to use a minimum of computer processing power. SQL Power Tools provides a dashboard and can be used to track the performance of all SQL statements.
  • Solarwinds DPA: Supports a strong focus on detecting bottlenecks in a database and places an emphasis on analyzing how long it takes the database to respond to queries. It comes with a user-friendly interface and will pinpoint the cause of database slowdowns. The Solarwinds DPA includes a machine learning algorithm that supports anomaly detection and allows you to understand disruptions.

NoSQL DBMS: These DMBSs do not support a relational system. As a consequence, they do not support linked relationships within their storage systems. However, NoSQL has the ability to store massive amounts of data for research purposes, and can accept a broad range of formats for data storage.

There are a variety of NoSQL databases (for example, key-value, wide-column, document, and graph). They offer flexible schemas and will scale easily with large amounts of data. Some tools used to identify and locate data files are: 

  • Data Catalogs: These are structured collections, and often used with NoSQL data systems. In essence, data catalogs are well-organized indexes of stored data. Generally, data catalogs provide information about data usage, data lineage, relationships between entities, and the source. 
  • Data Dictionaries: They are basically a centralized repository of metadata. The metadata in data dictionaries includes attributes and definitions and names references data, the assets creator, and owners. They can create aliases, which can help in locating data. 
  • Business Glossaries: A collection of terms and definitions used by a business. The definitions are logically linked to entities within the database. Business glossaries support structured communication. When combined with search and Data Management tools, researchers can find the needed data.

DevOps Database: DevOps is a philosophy used for developing software quickly and efficiently. Applying the DevOps principles to a database requires tools that automate the processes promoted by them. A DevOps database is often a converted SQL or MySQL database. With the addition of tools, this modified database improves software development and helps reduce developer burnout.

A DevOps database helps apps/software development teams identify problems and bottlenecks, and streamlines the development and release process. Listed below are some samples of DevOps Database Management tools:

  • Datadog is software designed for monitoring and analytics. It is available as a SaaS service. Datadog combines the data taken from apps, servers, databases, and other sources to support cloud-based applications that will function at scale.
  • AppDynamics monitors performance and is known for providing excellent real-time business processing and code performance visibility. AppDynamics provides transaction flow monitoring, deep diagnostics, and end-user monitoring.
  • Grafana is open source. It supports dashboards and queries and promotes understanding of the data. Grafana works well with Docker, Kubernetes, and Evolven.

Graph DBMS: These databases are schemaless, and do not store data in rows and columns. Graph databases are extremely flexible and place a strong emphasis on relationships. Additionally, they are rapidly becoming popular as a method for developing AI.

There’s not much in the way of graph DBMS tools, but there are a wide variety of graph databases. Most are interesting, and useful for certain types of work. Three are listed below:

  • Amazon Neptune: This graph database is user-friendly and ACID-compliant. It is a solution for graph applications requiring a maximized throughput with minimal latency. Amazon Neptune is capable of storing billions of relationships and can process queries within milliseconds. Amazon Neptune has a pay-for-time-used pricing model.
  • NebulaGraph: This is an open-source and easily scalable database. NebulaGraph comes with several data visualization tools, such as the NebulaGraph Dashboard and the NebulaGraph Studio. It also includes tools like NebulaGraph Analytics and NebulaGraph Algorithm. Nebula Graph is especially useful for experimenting with, and developing, artificial intelligence.   
  • Neo4j AuraDB: There is a free, open-source version of the Neo4j database. However, the Neo4j AuraDB is a more advanced model. It offers a free version (though not open sourced), along with paid-for versions. It is a fully managed cloud-based graph database service that supports fast responses for real-time analytics.

How to Select an Appropriate Database?

Consider the volume of data that will be moving through your system. Will large amounts of research be done, or will it be more about sales and communication? Will you be shifting your business to the cloud, or developing an on-premise database? 

Relational databases are good for small amounts of transactional data (for example, SQL and MySQL). Larger datasets (generally for research purposes) are often handled by NoSQL databases. Graph databases focus primarily on relationships and can be used for marketing, detecting fraud, and even manufacturing.

Cost is also an issue. While there are many open-source databases, the paid-for versions tend to be more efficient and user-friendly. Many startups prefer to move to the cloud immediately, avoiding the cost of the hardware needed to support a database. 

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