Database Management: Improve Data-Driven Decision Making

Data is the new currency. Yes, you read it right.

It’s an asset, and, like everything else, it has its value - particularly if we look at the world from how we perceive it today and the trends that are shaping our lives. Data has been a hot topic for years, but it's now a part of pretty much everything we do. People generate data every day, whether they realize it or not.

Like sharks, data is very much in a constant state of motion. It's created, stored, examined, analyzed, and manipulated. As a result, it has its own life cycle. The data lifecycle is essentially the journey that data takes through an organization's systems. This journey includes the processes and procedures you put into practice to ensure that the dataset you work with is protected and data quality is maintained throughout.

When it comes to managing real-time organizational data, various factors will affect your data set and the integrity of its usefulness from end to end. Database management ensures that your enterprise databases remain stable for effective business operations and decision-making.

Businesses rely on vast amounts of data collected from various sources, including paper documents, emails, and web-based connections. This ever-growing amount of data drives the need for stringent database management. But before we dive deeper into database management, we need to understand what a database is.

A database is the heart of a computerized business system. It contains vital data to run the business, such as customer records, addresses, and financial information. Simply put, a database is a collection of organized data.

The function of a database is simple: it allows a collection of data to be stored efficiently and organized in a useful and manageable way. This means that a database system is helpful for various purposes, from recording basic statistics to supporting a wide variety of complex business accounting and human resources functions. Companies use databases to retrieve and analyze information in a way that can help them achieve their data-driven business goals.

Why do businesses need database management?

A quality database is the building foundation of most enterprise applications. While few would argue against this simple truism, there are a wide range of views on achieving the objective of having a quality and useful database. This is where data management comes in. Data management is a complex process that requires a solid understanding of database management.

The data flood shows no signs of ebbing. Hence, organizations are investing in database management activities to:

• Maintain application and database performance
• Maintain records of consumers, data inventories, and employees
• Automate database operations and procedures
• Store and manage a wide range of data types

Database management involves knowing how to implement and store data in a database, how to maintain and clean it, and how to retrieve it at any time. All the information must be stored in a database when a business wants to make sense of it.

If data is the new oil, then database management is the oil rig.

Database management provides support to an organization's data management strategy. Along with business intelligence (BI) solutions, database management is a critical component of a company's informational infrastructure, enabling informed decision-making and corporate growth.

Key terminologies used in database management

A database stores all your essential information for quick and easy access. Through databases, businesses can store, manage, manipulate, and distribute information to make a more efficient workflow.

Database management is critical and requires a great degree of intellect, experience, and foresight to carry out the many tasks associated with it. Nonetheless, knowing the terminology related to databases is critical to ensure that a business is adequately informed and has a good grip on the concepts of database management during the database design phase.

Here are the definitions of some frequently-used database management terms.

CAP theorem

According to the CAP theorem, a distributed computer system (which includes a distributed database management software and its hosted data) cannot satisfy all of the following commitments at the same time:

1. Consistency: Consistency implies that all the computer nodes (devices or data points in a larger network) will contain the exact copies of a replicated data item for different transactions in real-time.
2. Availability: Availability means that every read or write request for a database object will either be executed successfully or will result in a message indicating the incompleteness of the operation.
3. Partition tolerance: Partition tolerance indicates that a database system can operate even if the nodes' network fails, resulting in two or more partitions, with nodes in each partition only communicating with each other. Once the partition heals, distributed systems that guarantee partition tolerance can gracefully recover from it.

At most, modern databases can provide only two of these assurances at the same time. Most database models differ in the way they deal with this limitation.

ACID

ACID is the acronym for attributes provided by typical database management systems standing for atomicity, consistency, isolation, and durability. According to the CAP theorem, the ACID database transaction model provides a consistent system. It's a suitable match for enterprises that deal with online transaction processing (for example, financial institutions) or online analytical processing (for example, consulting firms that deal with data warehousing).

Schema

A database schema is a skeletal structure that reflects a database's logical structure of data. It specifies how data is arranged in different tables and how relationships between the tables are linked. Database designers create the schema to aid programmers in understanding and making use of the database.

Attribute

A database attribute is a property that identifies a specific database object. A column in a database table is an attribute. At the same time, the table itself as a whole is known as an entity.

Metadata

Metadata means more data about the given data. In a DBMS, the data stored in the columns of a table has specific features, such as type, size, or other properties that allow the DBMS to interpret the data meaningfully or enable users to comprehend it better. The easiest way of accessing the metadata of a database is through a database dictionary.

Primary key

A primary key is a single attribute or a set of multiple attributes that uniquely identifies each record in a database. There can be no duplicate primary key values in a single table, and they cannot be null. Primary keys are handy when establishing associations between entities in one database and attribute values in another. A consumer, for example, may have multiple phone numbers and email addresses. A primary key helps link a single consumer to the numerous distinct values in the same "phone number" or "email address" field.

Foreign key

A foreign key connects a record in one table to a record in another. A foreign key is a record (or set of records) in one table that refers to the primary key of another table. The table containing the foreign key becomes the child table, and the table containing the primary key becomes the parent table.

For example, if there are two tables, customer and order, a connection can be established between them by adding a foreign key to the order table that references the customer_id in the customer table. Both the customer and order tables have the customer_id field. The foreign key in the order table is the customer_id column, which refers to the primary key in the customer table. The DBA must meet the foreign key constraint to insert a record into the order table. This decreases the chance of error and increases data accuracy by eliminating repetitive data entry.

Structured query language

Structured query language (SQL) is the most commonly used programming language for retrieving data from a database. Its strength and versatility enable the creation of databases and tables and data processing and querying. SQL syntax is divided into two categories: data manipulation language and data definition language. The data manipulation language provides the most frequently used SQL commands, while the data definition language is responsible for creating new database objects such as indexes and tables. SQL databases such as MySQL and PostgreSQL follow the ACID database transaction model.

Query

A query is an inquiry made about data in the database in the form of a coded command by programmers. Queries are helpful to find and retrieve data and create ad hoc lists of entries or a sub-table of results.

Data manipulation language

Data manipulation language, also known as DML, is a set of SQL commands that deal with manipulating data in a database. For example, the INSERT command adds data to a table, and the UPDATE command updates the data in a table.

Data definition language

Data definition language, or DDL, is a set of SQL commands that define the database schema. It simply handles the database schema descriptions and is used to construct and alter the structure of database objects. For example, the CREATE command creates the database and its objects, while the DROP command deletes objects from the database.

BASE Model

The BASE model was created as an alternative to the ACID database transaction model to meet the demands of NoSQL databases whose data is not organized in the same manner that SQL databases require. According to the CAP theorem, the BASE model provides high availability. It's a suitable match for marketing and customer service enterprises that deal with sentiment analysis for social network research.

NoSQL

NoSQL is a database paradigm created to handle unstructured data such as emails, social media postings, and media. A NoSQL database uses the less-strict BASE database transaction model to maintain data integrity. Instead of tables, a NoSQL database schema can utilize a key or value architecture or graphs to store data. MongoDB, Redis, Cassandra, Amazon DynamoDB, and Couchbase are among the most popular NoSQL databases.

Evolution of the database

Before the invention of databases, everything humans documented was on paper. People used lists, notebooks, ledgers, and infinite archives of records stored in file cabinets. When accessibility to one of these documents was required, identifying and physically retrieving the record was a long and arduous procedure.

There were also frequent issues ranging from missing records to fires that destroyed whole archives and the history of society and organizations. Finally, there were additional security issues because physical access was typically simple to obtain.

Scientists developed the concept of databases in an attempt to overcome the constraints of traditional paper-based data storage. The files in a database are known as records. The individual data pieces in a record are known as fields.

Despite their beginning in the early 1960s, databases have changed considerably over the years. The first databases used to store and manage data were navigational databases such as hierarchical and network databases.

The introduction of hierarchical databases solved many of the difficulties that arose with a paper-based approach. Hierarchical databases depended on a tree-like architecture and permitted only a one-to-many link. It also provided users with a comprehensive backup and recovery system, which meant that lost files due to external damages were no longer an issue.

The network model was first defined by scientists at the Conference on Data Systems Languages (CODASYL). It was able to overcome the limitations of the hierarchical model. Unlike its predecessor, the network database provided a more flexible model that permitted numerous associations.

1970-present

Although the network model outperformed the hierarchical model, designers and programmers still don’t prefer it. The fundamental reason for this is because more established products in the market, such as IBM's IMS and DL/1 kept utilizing the hierarchical model, while researchers developed the relational model. Designers found the relational model to be much easier to grasp, and it improved application programming.

The relational model organizes a database's contents into tables, each of which contains entries with fields. Each field's data type is specified, which helps guarantee that there are no discrepancies and that the output is consistent. In a relational database, tables can have relationships among them. Most relational databases use the SQL programming language to retrieve the data.

In 1998, a new term, NoSQL, was developed. It refers to a new breed of databases that store and retrieve data using query languages other than SQL. Such databases have been around since the early 1960s, but the Web 2.0 revolution brought them to the forefront of the technological world.

Traditional relational databases suffered from scalability and performance issues, whereas NoSQL was typically quicker since it used other data structures to store information. However, they’re also more versatile than traditional relational databases since the same limitations don’t bind them.

What is a database management system?

A database often requires a database management system (DBMS) or database administration system (DAS) to create and manage databases. A DBMS is comprehensive database software that acts as a bridge between the database and its end users or applications, allowing users to obtain, change, and control how information is structured and optimized. End users can create, protect, read, update, and remove data in a database using DBMS software.

The DBMS interacts with the rest of the operating system, notably the file system and the user interface, to change the database. It may be located on the same computer as the database, or it may be on a remote computer that accesses the database on another computer over a network.

The DBMS offers a centralized view of organizational data that numerous users from different places may access in a controlled way. It can also restrict what data the end-user views, allowing for several views of a single database schema. Since the DBMS handles all requests, end-users, and software applications are not required to understand where the data is physically housed or where it’s stored.

Concurrency, security, data integrity, and standardized data administration methods are all supported by the above three core features. In addition, change management, performance monitoring, and tuning, security, and backup and recovery are some of the common database administration responsibilities supported by the DBMS.

Types of databases

A database is a collection of data. More precisely, it's a collection of related data organized to store and retrieve legible information.

Databases come in a variety of shapes and sizes. The ideal database for a specific organization depends on how executives will use that data to make data-driven decisions. 

The following types of databases are available, depending on business needs.

Centralized database

A centralized database allows multiple users to simultaneously access its stored data over a computer network using a variety of applications. Larger organizations, such as a corporation or institution, generally employ centralized databases.

Relational database

A relational database uses the relational data model, which saves data in rows and columns combined to make a database table. SQL is used to store, manipulate, and manage data in a relational database. A relational database management system, also known as a relational DBMS or RDBMS, is a database software designed explicitly for relational databases. It's a tool that allows users to design, edit, and manage a relational database.

Distributed database

In contrast to a centralized database system, data in a distributed database is dispersed throughout an organization's numerous database systems. These database systems are linked together using communication connections. These connections make it easier for end-users to gain information.

Document database 

A document database follows a non-relational database model that stores and queries JSON-like documents. Document databases are more natural and flexible for programmers to maintain and query data in a database because they utilize the same document-model format as their software code. Such a model is well-suited for use cases such as catalogs, user profiles, and content management systems, in which each document is different and changes over time.

Object-oriented database

In an object-oriented database, the data is stored as an object. These are analogous to the objects used in object-oriented programming languages.

NoSQL database

A NoSQL or a not only SQL database is a non-relational database, which permits the storage and manipulation of unstructured and semistructured data.NoSQL databases gained popularity as the demand for developing contemporary applications expanded. As a result, in response to the demands, NoSQL introduced a wide range of database systems. A NoSQL DBMS differs from an RDBMS in its ability to handle both structured and unstructured data. This enables NoSQL systems to collect and analyze data without a strict tabular structure. NoSQL databases can perform queries of database entities that contain several components, such as social media postings, photos, audios, and videos, in addition to traditional text and numeric data.

Data warehouse

A data warehouse is an online analytical processing (OLAP) database specially intended for quick query and analysis. It serves as a central repository for data. It collects information from many sources inside an organization for reporting and analysis. Reports generated by complicated queries in a data warehouse help make strategic business decisions.

Open-source database 

An open-source database system is one whose source code is available to the public; these databases can be either relational or non-relational databases. The code of an open-source database is available to the public to download, modify, and reuse. A proprietary or closed source database, on the other hand, is one in which the code is secure and unavailable to the public.

Graph database

A graph database stores, maps, and queries relationships using graph theory. These types of databases are typically valuable for analyzing relationships. A graph database, for example, maybe used by a company to extract data about clients from social media.

Cloud database

A cloud database stores a collection of organized or unstructured data on a private, public, or hybrid cloud computing platform. It gives users access to the database using various cloud computing services (SaaS, PaaS, IaaS). Cloud database models are of two types: conventional and database as a service (DBaaS). A cloud DBMS helps businesses host their databases on the cloud. The DBaaS approach uses managed cloud database services to administer the systems. The cloud approach allows firms to pay only for the storage, processing, and other resources they use rather than furnish the underlying infrastructure and resources necessary to run a DBMS on-premises. The managed service automatically scales up or down in response to changes in database resource demand. Users can access data assets easily from any internet-connected device, and database workloads are safely stored in the cloud.

Multi-model database

A multi-model database supports numerous data models in conjunction with a single, integrated backend. Most databases accommodate a single data model that governs how the data is structured, stored, and modified. Multi-model databases can store multiple data models such as key-value, relational, document, and graph.

Self-driving database

Self-driving databases, also known as autonomous databases, are the newest and most innovative type of database. They are cloud-based and use machine learning to automate database operations such as tuning, data security, backups, and updates that database administrators traditionally perform.

Challenges of database management

As the rate of user interactions, the number of devices, and the quantity of data saved grows, so do the issues you encounter while maintaining your database. Here are some of the frequent difficulties you'll encounter while making your database management more efficient for your organization.

Limits on scalability

Every software application, including database servers, has scalability and resource usage constraints. You’ll not be efficient in how you work if your database doesn't match your expanding business demands. Likewise, hardware configuration, operating systems, and database architecture affect an organization's transaction processing capacity.

Relational databases are more prone to scalability issues because of their architecture. As a result, companies may have performance difficulties with their infrastructure and database technology as the size of relational database increases. When problems arise, companies generally begin by scaling up the database server. If their database continues to expand and performance declines even with the installation of several new servers that they can afford, they may consider upgrading to a better performance database technology, which is generally significantly more expensive.

Data security

Security is the most pressing problem in today's digital environment. Businesses must secure their data and keep leaks to a minimum. For example, database breaches containing very sensitive information can ruin a business' reputation. Hence, data security has received a lot of attention recently, which is natural.

The most prevalent threats to the database are:

• Access privileges that are misused when employees use database privileges that exceed their job duties
• SQL and NoSQL database injection attacks that target big-data platforms
• Data breaches due to human negligence

A defensive matrix of guidelines and internal controls is required to safeguard databases adequately. Use database security tools as part of your security guidelines to encrypt the data and restrict access to only those who need to see it.

Database performance

Databases have always had to meet stringent performance requirements. While the amount of structured and unstructured data a business collects grows, it’s also important to process this data faster to keep the consumers happy. The database must process data rapidly to satisfy users without having them jump through hoops to access information.

The database, regardless of technology, must handle enormous volumes of data without slowing down.

Benefits of database management

As the volume of data grows, so does the requirement for a system to properly manage this volume of information. The success of an organization depends on the quality of data that it uses. With the growing number of applications relying on data and its role in making business decisions, it becomes even more critical to manage this data effectively.

Successful database management implementation is not an easy task, but the benefits it provides are outstanding.

Reduces data redundancy

In a file-based data management system, there are instances where files are stored in multiple locations in a system or even across multiple devices. Unfortunately, this is a frequently occurring issue in many organizations, also known as data redundancy, where the identical data is present in two or more different locations.

Data redundancy causes data anomalies and corruption and should be avoided during database design. Database normalization eliminates redundancy and maximizes storage use. The appropriate usage of foreign keys in a database can reduce data redundancy and the occurrence of harmful anomalies.

Maintains data integrity

The term data integrity refers to the accuracy and consistency of the data across the organization. Since a business can contain several databases with structured or unstructured data, ensuring the integrity of the data becomes critical.

When multiple versions of the same data exist in various parts of an organization, it causes data inconsistency. Businesses can ensure an all-encompassing picture of their data throughout their organization by using a suitable database management system along with data quality management tools to enforce data integrity.

Furthermore, data management systems give a more effective framework for enforcing data privacy and security regulations. More effective management implies more openness and a lower chance of regulatory violations.

Provides backup and recovery

Database users are not required to backup data regularly because the DBMS does this for them. Database software has robust mechanisms to enable data recovery during a failure and automated backup for all corporate data at predefined times. Furthermore, it restores the database to its former state following a crash or system failure.

Database management systems software

A database management system (DBMS) is a software system designed to facilitate the storage, retrieval, and maintenance of database records. A database management system is also sometimes considered to be synonymous with the database.

DBMS features go beyond conventional database activities and are distinguished by significantly greater management capabilities. These include enhanced metadata visibility, comprehensive and customized reporting, performance monitoring, user analytics, and regulatory checks.

A software solution must meet the following criteria to be eligible for the DBMS category:

• Comply with a database transaction model (for example, relational, object-oriented, hierarchical, distributed)
• Allow for the database development and management
• Provision for detailed reporting and analytics
• Integrate with other database-driven applications

*Below are the five leading DBMS software platforms based on G2 data collected on June 17, 2021. Some reviews may be edited for clarity.

1. SQL Developer

SQL Developer is a free DBMS that streamlines the development and maintenance of Oracle databases in both traditional and cloud platforms. In addition, Oracle SQL developer provides comprehensive end-to-end creation of SQL applications, a reporting interface, a spreadsheet for executing queries and scripts, a DBA console for database management, and a comprehensive data modeling solution.

What users like:

"SQL Developer is available on multiple platforms such as Windows, Mac, and Linux. Oracle doesn't charge a penny for it. So, SQL Developer is best suited for a big cross-functional globally based team. SQL Developer is less resource-intensive compared to other IDEs.

It can also manage and develop Oracle databases in both on-premise and cloud deployments. SQL Developer can also connect with non-oracle databases like IBM db2, MSSQL Server, and MySQL."

-  SQL Developer Review, Aritra G.

What users dislike:

"There are some limitations like you cannot search more than 1000 records at a given time etc. But overall, it's a great tool."

-  SQL Developer Review, Ashutosh G.

2. Toad for Oracle

Toad for Oracle empowers DBAs, developers, and analysts to automate operations, lower the risk and reduce project delivery times by almost half. It also lowers the impact on productivity, development cycles, performance, and availability of inefficient code with a world-class integrated development environment (IDE). The IDE also provides teams with extensive automation, code templates, and collaboration.

What users like:

"I enjoy the ease of dragging and dropping tables and the ability to do quick manual edits through SQL statements. This creates a user-friendly experience where many users can create their queries. In addition, connecting to databases is simple, and users can save various schema of tables to return to at a later time, reducing the need to configure the views upon each login."

-  Toad For Oracle Review, Alisa P.

What users dislike:

"Sometimes it's hard to navigate between the visualize, build, explore, and edit code menus. I feel like it can host many tabs, which feel like layers that I can lose things in. It's also frustrating to have to re-establish the connection to the warehouse every time I log in. Maybe this is standard with querying systems."

-  Toad For Oracle Review, Emily E.

3. Oracle Enterprise Manager

Oracle Enterprise Manager is a DBMS tool that provides a single dashboard for managing all deployments. Businesses can run it on-premises or via Oracle cloud infrastructure. Oracle Enterprise Manager offers more visibility and control over a business' IT infrastructure. In addition, it provides users with market-leading management and automation support for Oracle applications, databases, hardware, and engineered systems.

What users like:

"Oracle Enterprise Manager provides a single point of control for your applications and databases. In addition, it offers out-of-box automation in the form of monitoring templates and reporting templates. I also love the new administrative group introduced in the 13.1 version onward. It's a single point for patch control to monitor all your deployments through OEM."

-  Oracle Enterprise Manager Review, Jim R.

What users dislike:

"License costs are high. Also, it uses lots of computer resources like CPU and memory which can cause some slowness."

-  Oracle Enterprise Manager Review, Tugce K.

4. Microsoft SQL

Microsoft SQL Server is a relational database management system that extends SQL querying capabilities to users for Windows, Linux, and Docker containers. SQL Server can be installed on-premises or in the cloud for industry-leading performance. It allows developers to build intelligent applications in their favorite language and environment.

What users like:

"My favorite thing about this software is how robust of a tool it is. It has been the database engine of choice for many business systems I've used in my career: from PDM and ERP systems to design automation tools. The usage of this platform by the developers goes well beyond what I can create on my own. Yet, I've found the architecture of the interconnected tables used in these systems intuitive, giving me a shallow learning curve should I ever need to audit or retrieve data from those systems.

For a novice user such as myself, I've further found value in utilizing SQL as a leaner, faster version of Microsoft Excel. In some business roles, I've found that companies use Excel for databases, which grow slow and cumbersome as the volume of data increases. By storing data and doing basic calculations in SQL tables, I've been able to leverage SQL's significantly more efficient calculation abilities and read data in a fraction of the time. I've found this true to the extent that I've even used SQL outside of work for use in fantasy sports."

-  Microsoft SQL Review, David M.

What users dislike:

"It's for large-scale businesses, so it is probably not for you if you are looking for something lightweight, for example, for smaller websites. It also takes some learning curve and is helpful to have a dedicated admin in full IT environments. However, this can be overcome by the wealth of information available out there due to its longevity as a product."

-  Microsoft SQL Review, Jeremiah S.

5. DataGrip

DataGrip is an intelligent IDE and a database client that satisfies the unique requirements of SQL developers. Developed by JetBrains, it provides a collection of database utilities that operate on top of the IntelliJ Platform.

What users like:

"The best thing about this software is that it allows me to manage databases easily and has extensive support for handling different database engines. It's an easy-to-understand application and makes database management a much more bearable task. It also has excellent functions and resources to correctly use the data we have according to our study and productivity needs."

-  DataGrip Review, Mrunmayi G.

What users dislike:

"It consumes many resources on my computer, which makes my PC slower and its performance drastically decreases. Furthermore, the fact that it doesn't have many options to consult information is also quite negative, since it doesn't allow us to make inquiries in a much easier way without having to consult technical support."

-  DataGrip Review, Emiro N.

Data management made easy

Getting an overview of your data is challenging. There are so many ways to collect it, and if you aren't doing it right, it may be irrelevant or, even worse, misleading information for you. Higher quality data is only achievable with improved data management practices. Database management is the backbone of all modern businesses, which need to store large volumes of data subject to modifications.

Database management provides a foundation for data management activities, making it more necessary than ever to use the databases wisely and efficiently.

Data backup and recovery is an essential practice for any business. Use database backup software to protect organizational data with reserve database copies.