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Big Data Analytics – Operational Database

Learn what is operational database, why operational database needed, functions of operational databases, advantages and disadvantages of operational databases.
Submitted by IncludeHelp, on February 24, 2022

1. What is Operational Database?

OLTP (On-Line Transaction Processing) is an acronym for Operational Database, which refers to a database management system in which data is stored and processed in real-time. CDP Operational Database is an operational database-as-a-service that enhances the ease of use and flexibility of Apache HBase. It enables developers to quickly build future-proof applications that are designed to handle data evolution.
They also offer real-time analytics, which is highly sought after, and have the ability to manage both SQL and NoSQL databases, which is another plus. They are also expanding their support for distributed databases in order to improve their scalability, availability, and fault tolerance characteristics. Microsoft SQL Server, AWS Dynamo, Apache Cassandra, MongoDB, and other operational databases are examples of this type of database.

In recent years, operational databases have increasingly supported distributed database architecture, which can take advantage of distribution to provide high availability and fault tolerance through replication and the ability to scale out. When it comes to the growing role of operational databases in the IT industry, the transition from legacy databases to real-time operational databases that can handle distributed web and mobile demand, as well as address big data challenges, is happening quickly.

2. Why Operational Database needed?

Consider the possibility of a global shoe-selling enterprise based in India. The warehouse data, which contains all of the details of inventory such as raw materials, WIP goods, and finished products, allows the Purchase team, which is located in a different part of the world, to know what to order next and in what quantities to meet the company's requirements.

Operational Database management system manages everything from the company's financial data to its production and manufacturing data, sales and marketing data, customer relationship management and supply chain management data, and so on. This enables them to analyze data and information in real-time and to use advanced complex analytical tools, which they would not otherwise have access to.

As a result, an operational database management system has become increasingly important for the following reasons:

  • Analytical Capabilities: It has the ability to provide real-time analytical capabilities to assist in any decision-making process. It can incorporate a variety of applications to enhance the analytical abilities of the database according to the needs of the user without changing the state of the database in any way.
  • Internet of Things (IoT): It assists in realizing the full potential of IoT by conducting real-time monitoring, reviewing, and recommending appropriate solutions on its own.
  • Fault Tolerant: Incorporating Distributed databases can result in the creation of a fault-tolerant system in which if one of the components goes offline or malfunctions, it will not have an impact on the rest of the system.
  • Scalability: Modern operational database systems are always ready to scale on demand, and they have the added advantage of supporting a high number of concurrent users and operating at a low latency. Businesses can benefit from the "Use as you grow" philosophy.
  • Big Data: Operational Databases with distributed systems and NoSQL-based systems can harness the true potential of big data using technologies such as Hadoop in conjunction with operational database management systems such as Cassandra or MongoDB.

3. Functions of Operational Databases

The following are the functions of an operational database:

3.1. Instant updates

Continuous data updates via "micro-batches" or streamed singleton updates throughout the day ensure that the most up-to-date information is available for analytics-based decision-making at all times.

3.2. Indexing and Cataloguing

The primary function of a database system is indexing, which refers to the ability to efficiently store data so that it can be retrieved when needed with minimal effort. In this case, it is divided into three categories: Primary, Secondary, and Clustered Indexes. Cataloguing is the process of assigning key attributes to a file, which can then be used to recall information from that file, such as a book or a journal.

3.3. Fast 

Unlike traditional row-based databases, column data blocks that retain their min-max value metadata eliminate the overhead of creating indexes that must be updated with each change, as is the case with traditional row-based databases. It is possible to reap a variety of data warehouse benefits by being able to make better business decisions more quickly.

3.4. Consistent

Some databases make a trade-off between query integrity and speed. A good Operational Data Warehouse must be able to provide row-level locking and full read consistency for running queries, even when the underlying data changes, in order for them to be effective.

3.5. Replication

Simply put, it refers to the process of creating a copy of a file or data in its entirety and storing it in multiple locations to make retrieval and storage easier. When it comes to distributed systems, this is especially important because the same data can be accessed more easily and consistently when it is stored in multiple locations.

3.6. Robust

A modern data warehouse's ability to provide enterprise-level resiliency and manageability is one of its most significant advantages. The equivalent of this is having an Operational Data Warehouse that has reliable backup and recovery, failover, and replication capabilities.

3.7. File Storage and Organization

File storage is another important function of the operational DMS, and because the use cases for the operational DMS are complex, so is the system for storing the files that is used to store them. The system must be reliable enough to sort and store files in the appropriate locations. The file organization system can organize files in a number of different ways, including heap, sequential, hash, and clustered file organization.

3.8. Supports Scalability

An effective enterprise data warehouse must be scalable in both the vertical and horizontal directions. Vertical scalability allows workloads to take advantage of more CPU and storage capacity on a single system by utilizing the resources of the entire system. As soon as a single system's hardware capacity is reached, the ability to scale horizontally to a cluster of systems allows the Operational Data Warehouse to be expanded to accommodate larger databases and a greater number of users. A modern data warehouse's ability to scale up in response to increased demand is a significant competitive advantage.

3.9. It is cost-effective

In the case of a specific database technology being used to support a specific business case, a number of factors can influence the total cost of ownership (TCO). One advantage is the ability to run standard servers rather than specialized appliances. Other advantages include the ability to customize deployment models to meet specific business requirements, the ability to scale up and down according to performance requirements, and the ability to use different-sized components (compute and storage) to optimize operating efficiencies.

3.10. Query Processing or Query Execution

Because of its superior query processing system, operational database systems are able to perform analytical functions. A query translation process refers to the entire process of translating a query submitted to a database system by users into simple low-level instructions, as well as optimising the query and analysing and evaluating the query. Following the process, the system would extract relevant information from the database and display it to the user. This entire process takes only a fraction of a second to complete.

3.11. Support for Transactions

In the operational database system, a transaction system can be thought of as the logical unit of operation. A steady-state operation, a concurrent operation, and the availability of recovery services are all essential for it to function properly and consistently. Transaction support takes care of this, as well as ensuring that the system is ACID-compliant, which stands for Atomicity, which distinguishes a transaction from other transactions. Consistency, which ensures that the transaction does not change the database during the process, prevents the transaction from becoming inconsistent. Finally, Durability refers to the fact that the transactions are permanently stored in the dataset along with the end result of processing, and Isolation refers to the fact that the transactions are kept independent in a concurrent state.

3.12. The ability to be adaptable

These are the days of the single computing platform, where an organisation could standardize on it. The Operational Data Warehouse must be able to be deployed on-premises (on Linux, Windows, or Hadoop Clusters) or in the cloud, and it must provide the flexibility to do so (on AWS, Microsoft Azure and beyond).

3.13. The ability to communicate with others

In order to be effective, an Operational Data Warehouse must support open application programming interfaces (APIs), such as those that support Open Database Connectivity (ODBC) and American National Standards Institute Structured Query Language (ANSI SQL), among other things (ANSI SQL). These are required in order for the data warehouse to be compatible with the wide range of query tools that an organization may employ. There are more than 20 different visualization and query tools in use by many organizations.

3.14. Secure

Because of the explosive growth of cybercrime and the increased regulation of data privacy, even "internal" systems must be protected against attack. In order to be considered effective, an Operational Data Warehouse must include built-in support for advanced encryption, auditing, role-based security, and data masking.

3.15. Connected

The ability to ingest data at a high rate is a critical requirement for an Operational Data Warehouse. If you are unable to load your data in a reasonable amount of time, you will be forced to work with summary data or, even worse, with stale data.

4. Advantages and disadvantages of Operational Databases

4.1 Advantages of Operational Databases

The following are some of the advantages of using a computer:

  • Versatile: One of the most important characteristics of operational databases is their versatility. They can accommodate distributed systems such as NoSQL, SQL, and New SQL Databases.
  • Fault-tolerant: As previously discussed, these systems are highly available, fault-tolerant, and highly scalable.
  • Highly secured: The fact that they are highly secure stems from the fact that they include built-in support for encryption, auditing, and protection from cyber attacks.
  • Highly adaptable database: They are extremely adaptable and can be used with a variety of applications without losing the state of the database. As a result, people in different locations or within a department can incorporate applications and services according to their requirements without having to worry about the state of the database.
  • Economical system: operational databases are typically more economical than other types of systems because they are based on distributed networks and systems, which reduce costs and ensure consistency.

4.2 Disadvantages of Operational Databases

The following are some of the negative aspects of the situation:

  • They usually have a learning curve, which means that personnel must be assigned to provide relevant training in order to manage such databases, which increases the costs of overhead.
  • Even the installation process of such an operational database system necessitates time and effort because the system must be configured in the most efficient manner possible in order to meet business objectives and reap the greatest possible benefit from it.
  • Security could also be an issue because the data is stored in a remote location, making it difficult to maintain overall control. We have seen recent examples of customer data being hacked from some of the most prominent technology companies.

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