Guide For Database Selection And Management In 2024

In the fast-paced digital era, transformation in technology happens as never before, and the data generated by applications is growing by leaps and bounds, nearly always. Businesses and developers are seeking a formidable choice: a robust and efficient database solution to harness the true potential of data.

Let’s unlock the power of data and fuel the critical decision-making strategy by picking the right databases and providing effective database management and optimization techniques.

Factors Influencing Database Choice

The choice of the right database ensures a significant impact on the performance, scalability, and overall success of an application. Let’s explore the essential factors that influence database choice.

• Data model and query pattern
• CAP Theorem
• Scale, performance, and interoperability
• Security and Compliance
• Development, Operational Overhead, and Cost

1. Data model and Query Pattern

Data modeling is the foundation of the database design process. Designing a logical structure that meticulously represents real-world entities, attributes, and their relationships ensures data integrity and optimal storage efficiency. The data modeling is heavily influenced by the nature of the data. It can be structured or unstructured, or it represents a domain entity and relationships among the domain entities. The data storage approach can vary from being a simple key-value to storing a document against a key.

Additionally, you should consider the query pattern because application read and write patterns are a key factor in choosing the right database. For instance, if the application needs to fetch data by key, then all you need is a key-value database, or if it needs to query by many different fields, you can choose a relational database, and so on.

2. CAP Theorem

The CAP theorem refers to the three crucial distributed system characteristics: consistency, availability, and partition tolerance. It states that any data store can guarantee only two of the three characteristics. Both the ACID and BASE models are governed by the CAP theorem.

Suppose the use case demands consistency and partition tolerance, then the master needs to block the transaction for a particular key until the slaves get updated. Here, any updates against the key will be blocked during the synchronization process, thereby trading off its availability. Examples include the banking system (highly consistent) and Youtube (highly available).

The ACID model is mostly used in RDBMS because it needs to be highly consistent, and the BASE model is mostly used in NoSQL because it demands availability.

3. Scalibility, Performance, and interoperability

Based on the volume of data and requests to the database, the scale requirements can be identified. A system can be scaled vertically (increasing the power of a single server) or horizontally (adding additional nodes to share the load).

Before implementing scaling techniques, it is important to understand the application’s performance. By understanding read-heavy and write-heavy queries, developers can opt for appropriate optimization techniques and determine the database latency requirements.

Interoperability is an important aspect if there is a need to access and process data from multiple sources. In such cases, the database should integrate well with adjacent technologies in your architecture. For example, consider the healthcare industry. It depends on multiple sources of information from multiple systems.

4. Security and Compliance

Basic security mechanisms like access control, authentication, authorization, encryption, backup, and recovery can minimize security risks. The system should protect sensitive data from unauthorized access.

In today’s digital age, compliance protocols are no longer just recommendations and can vary based on industry. It includes handling sensitive data, privileged users, the type and frequency of audit reports, and ensuring the database complies with regulations such as GDPR or CCPA.

5. Development, Operational Overhead, and Cost

Every database requires maintenance to ensure optimal performance. For instance, moving data from a transactional database to a data lake will reduce storage stress on the transactional database and optimize computation-heavy analytics queries. Some databases are serverless, and for others, we need to create and manage our own cluster.

Developers should take care of optimization techniques such as purging deleted rows, resequencing, compressing, managing index paths, defragmentation, etc. Also, evaluate how steep the learning curve will be for a newbie.

Related to cost, It’s not just about the upfront cost but also the long-term cost associated with scaling, maintenance, and support.

Types of databases based on data modeling

To pick the right database for your choice, it is important to understand the types of databases based on data modeling.

• Relational/row-based database
• Columnar-based database
• Document-oriented databases
• Key-value databases
• Graph databases
• Time series databases
• In-memory databases
• Ledger Database
• Spatial Database
• Vector Database

1. Relational or Row-based Database

Relational or row-based databases organize data in the form of tables (rows and columns) with a predefined schema. They are also known as SQL-based databases. These databases are designed for handling complex querying, transactional support (commits and rollbacks), and data integrity (ACID properties).

Some of the relational databases are MySQL, Postgres, MariaDB, Oracle, AWS Aurora, and RDS. It is generally used in applications such as finance and banking (data consistency or integrity is of utmost importance), ERP, CRM, etc. Relational databases are the worst choice for data where structure cannot be finalized or changed (non-structural data).

2. Columnar-based Database

Columnar databases store data as columns instead of rows. They are optimized for heavy reading loads and can efficiently query specific columns of data. Here, the read cost increases based on the number of columns included in the query. Another benefit of a columnar database is self-indexing. They are the right choice for large-scale data warehousing and analytics. Examples are Apache Cassandra, Hbase, and AWS Redshift.

Columnar databases are not a good choice for transactional workloads (frequent data requests and changes to data from multiple users) that demand high concurrency. It is also not suitable for incremental data loading or for small sets of data.

3. Document-oriented Databases

In document-oriented databases, the structure of documents can be effortlessly extended based on need. Here, each key has its own structure in the document. They store data in self-describing documents in JSON, BSON, or any custom form that is flexible in schema.

A document-oriented database allows complex and hierarchical data and can be queried efficiently. It has the ability to perform ad-hoc querying and is generally used for storing various attributes against a domain entity. Some of the examples are MongoDB, CouchDB, and AWS DynamoDB. They are not optimal for multi-document ACID transactions.

4. Key-value Databases

A key-value database is a type of non-relational simple database that stores value against a key. The key spaces are highly partitionable and provide horizontal scaling at a greater level when compared to other databases. They are suited for high-throughput, low-latency read and write use cases.

General use cases are caching, session management, metadata storage engines, shopping carts, etc. Some of the popular key-value databases are Redis, Riak DB, and AWS Dynamo DB. However, key-value databases are not a good choice for complex queries and tight schemas.

5. Graph Databases

Graph databases store nodes and edges (relationships) instead of tables or documents. Nodes and edges can have properties that are relevant for modeling highly connected data, multi-hop traversals, reverse lookups, and relationship analysis. Some of the popular graph databases are Neo4j, AWS Neptune, and Azure Cosmos DB. They are widely used for network management, supply chain efficiency, knowledge graphs for AI, fraud detection, etc.

6. Time Series Databases

Time series databases are designed to store and retrieve timestamp-based information like sensor data, system metrics, intraday stock prices, etc. It permits the storage of a large volume of timestamp data in a format that facilitates efficient storage, retrieval, and analysis of data. Examples of time series databases include Influx DB and Open TSDB. Relational and NoSQL databases are alternatives, but the I/O cost is comparatively higher when compared to time-series databases.

7. In-memory Databases

An in-memory database is a purpose-built database where data is stored and accessed from the primary memory (RAM) rather than disk I/O. It is ideal for applications that require microseconds of response time. These databases ensure high throughput, low latency, and high scalability. Two of the popular in-memory databases are SAP HANA and Redis. Usecases include caching, real-time bidding, gaming leaderboards, etc. The downside of the in-memory database is its higher cost when compared to other databases (storing data in primary memory or RAM is more expensive than disk storage).

8. Ledger Databases

The ledger database provides a transparent, secure, and immutable record of transactions using cryptographic techniques. Since it is immutable, any change in the existing data is considered a new version of the data (never overwrite the existing data). Here we have the ability to review the changes (transparent), and the entire set of recorded data is hashed (cryptographically verified).

Examples are the Amazon Quantum Ledger Database (QLDB) and ImmuDB. Usecases include storing financial transactions, reconciling supply chain systems, maintaining claim histories, centralizing digital records, etc.

9. Spatial Databases

A spatial database stores geometric objects (points, lines, and polygons), 3D objects, topological coverages, and triangulated irregular networks. It allows spatial indexing and provides an efficient algorithm for spatial join. Some common spatial databases are PostgreSQL (with the PostGIS extension), ESRI Geodatabase, Snowflake, Oracle, etc. Usecases include mapping, urban planning, network planning, risk assessment, and so on.

10. Vector Databases

A vector database provides efficient means to store, retrieve, and perform operations on high-dimensional vector data. It is best known for organizing and searching large amounts of embedded data (numerical representation of subject, word, image, etc.), which is beneficial for information retrieval, document similarity, clustering, and so on. They are optimized for nearest neighbor search and make use of specialized indexing and querying algorithms (ANN, kNN, k-d tree, ball tree, etc.). A few of the vector databases include Weavite, PgVector (a PostgreSQL extension), ChromaDB, etc.

Database optimization techniques

Exploring the key strategies and best practices for effective database management and optimization helps organizations have control over the data, thereby driving growth in the competitive world.

With proper optimization techniques, organizations can gain faster response times, enhanced application performance, and better resource utilization. Let’s delve into the techniques.

1. Indexing, Query Optimization, and Denormalization

Efficient indexing significantly contributes to faster data retrieval operations. Understanding the different index types (clustered and non-clustered), choosing the right column to index (high selectivity, columns in search conditions, etc.), and considering composite index are critical for an efficient data retrieval process. However, indexing incurs additional overhead during the write operation. So it is important to consider the impact on writing performance as well.

Query optimization techniques such as query rewriting, join optimization, and the use of query hints can greatly enhance the performance of SQL queries.

The denormalization process can reduce the number of joins and amplify query performance. However, it is important to balance the denormalization benefits, data integrity, and maintenance complexity.

2. Caching and Partitioning

Implementing a caching mechanism can reduce the amount of time an application takes to access repetitive or resource-intensive data. This mechanism can boost system responsiveness.

Partitioning is the process of dividing a large dataset (tables or indexes) into small partitions. It facilitates query performance and cuts down on data management efforts for massive data volumes.

3. Lazy Loading

Lazy loading is an optimization technique that loads data from a database only when it is explicitly requested. It offers efficient resource utilization, improved responsiveness, and memory optimization.

4. Garbage Collection

Garbage collection is an automatic background database process that removes expired and unwanted data. VACCUM is a garbage collector for PostgreSQL.

5. Distributed System Pattern and Dynamic Cluster

In developing distributed systems, it is essential to make use of distributed system patterns for application performance. Command and Query Responsibility Seggregation (CQRS) is one of the patterns that separates the read and write operations of a distributed system. It is best for data-intensive applications like SQL or NoSQL database management systems and for data-heavy micro-service architectures. Other patterns include 2PC, Saga, RLBS, and so on.

Dynamic clusters consist of server instances that can be dynamically scaled up to meet resource needs.

6. Regular Maintenance and Hardware Setup

Regular monitoring and maintenance can identify unused or redundant indexes and address index fragmentation (rebuilding or reorganizing indexes), statistics gathering, and disk fragmentation.

Choosing the appropriate hardware configuration (disk type, memory size, and network bandwidth) is crucial for database performance. Factors to keep into account are data size and growth, work load and concurrency, network and security, scalability and availability, budget, and maintenance.

Conclusion

Whether you are constructing a high-traffic e-commerce website or a mobile application, deriving insights from the massive volume of data generated by IoT devices, or harnessing the potential of embedding data, understanding the concepts that shape the database can help you build a robust and efficient application.