What is Database Management?

Databases serve as the foundation for business processes, applications, and user access. Therefore, organizations need to be strategic in their approach to the database systems they have installed. Below, we share data management practices that help you maintain data quality, reduce costs, and ensure a positive user experience.

Design with scalability in mind

A scalable database allows organizations to mitigate traffic volume increase, which may push existing compute resources, such as processors and data storage, to their limits. You can scale a database horizontally and vertically.

Horizontal scaling

Horizontal scaling involves adding more data clusters instead of directing traffic to a single database. This method evens out the incoming traffic and prevents overwhelming entire database systems. Incoming requests can be redirected to the particular component that contains data, which results in fast data retrieval. To scale a database horizontally, you can implement these methods:

• Database sharding, where a database is partitioned and distributed into smaller parts.
• Load balancing, an approach that distributes network traffic to database clusters.
• Read replicas, which replicate the main database and serve it to applications that require frequent access to specific data.

Vertical scaling

Vertical scaling is an approach that increases the compute resources that your database uses. Like horizontal scaling, it allows database systems to operate reliably when traffic spikes. However, vertical scaling doesn’t involve changing the database design.

Scaling a physical database on-premises vertically is cost-intensive because organizations must invest in additional database servers and provision them accordingly. To make vertical scaling more effortless and affordable, organizations built on cloud databases. When you use AWS Cloud databases, you can:

• Deploy the database without provisioning server infrastructure.
• Scale the database to the required capacity.
• Pay only for the compute resources used.

Establish data backup strategies

When working with databases, organizations are responsible for ensuring data integrity and availability and minimising data security risks. A good backup strategy involves replicating data and storing it securely. Additionally, it includes measures to promptly restore them and prevent operational disruptions.

With an effective database backup strategy, you can recover from downtime promptly and ensure business continuity. For example, if you continuously log database changes, you can recover them to a time point before they were altered or became unusable. 

Organizations in certain industries are required by law to maintain copies of historical data for a specified period. Creating a database backup is also helpful if you want to migrate your database system from one infrastructure to another. Such tasks often involve consolidating data from various sources, such as relational databases, and restoring it in the new setup. During the process, subsequent data transformation, although carried out meticulously, may still result in data inaccuracy. In that case, you can roll back the changes with the backup.

AWS Backup simplifies data backup and protection across cloud and hybrid environments. It automates data protection, monitors data assets, and provides disaster recovery capabilities, enabling you to meet compliance guidelines. Here’s how to get started with backing up your AWS databases.

1. Sign up for AWS and launch your AWS Backup console.
2. Create a backup plan and configure backup policies, methods, reporting, and other parameters.
3. Assign databases you want to back up, including Amazon Elastic Block Store (EBS) volumes, Amazon Relational Database Service (RDS) databases, and Amazon DynamoDB tables
4. Start the backup.
5. Monitor your backups and restore them if required.

Enforce granular access controls

Organizations must exert control over who has access to the information stored in their databases. You need authentication and authorization in your access control mechanism. Authentication enables you to verify user identity requesting access to the database. Meanwhile, authorization indicates if the particular user should be granted access. Together, they form the foundation of access control methods that keep modern databases safe, such as and

• Role-based access control (RBAC) provides database access based on the principle of least privilege. With this method, you grant the user access to only the necessary data they need in their role.
• Attribute-based access control (ABAC) considers changing factors such as time, location, and users’ requirements before allowing access to specific data.

In addition to regulating access, organizations should continually monitor and audit databases to detect unauthorized attempts and enhance their security resilience.

AWS IAM allows you to set permission guardrails that enforce fine-grained access to cloud workloads. You can generate least-privileged policies, review permissions, and set conditions that further limit access. Wellola, a healthcare platform provider, uses IAM to enforce strict data governance and protection when granting developers access to its features.

Monitor and fine-tune performance

Database systems may experience performance degradation and bottlenecks over time. Left unaddressed, applications and services that rely on the databases will be negatively impacted. To ensure reliable access to stored data, you need ways to continuously monitor and fine-tune database performance. For example, a database might experience concurrency control issues, especially when multiple users work with rapidly changing data. This can result in data inconsistencies.

Conventional database monitoring relies heavily on manual interventions, which introduce a delay between identifying issues and implementing remedial measures. Modern databases benefit from automated performance monitoring, which accelerates problem resolution.

When you use Amazon Relational Database Service (Amazon RDS), you have access to a dashboard that automatically brings key indicators of your database. For example, you can see if your database is running, the resources it consumes, and the status of read replicas. Additionally, you can integrate AWS RDS with other AWS services. For example,.

• Amazon CloudWatch allows you to monitor built-in RDS metrics and trigger alarms to activate subsequent actions.
• Amazon EventBridge captures and routes events that signify changes in the Amazon RDS environment to other AWS services.
• Amazon CloudTrail logs API calls made to RDS when accessing your database.

Establish data consolidation for analytics

Each application database exists as a silo in your organization. To derive actionable insights through analytics, you need to consolidate data from multiple databases. For example, you may have a group of databases that store a mixture of structured, unstructured, and semi-structured data. However, to analyze them, you need to extract, transform, and load (ETL) the data into a storage that supports more flexible data models and complex data structures.

The best way to do so is to set up data pipelines that connect the relevant databases to a data warehouse or data lake. This way, you can transform transactional and operational data into formats that business intelligence (BI) applications can analyze effectively.

Amazon Redshift is a cloud data warehouse that helps you consolidate databases without the need for complex data pipelines. Zero-ETL features automate data ingestion from Amazon RDS, Amazon Aurora, and Amazon DynamoDB, allowing you to achieve near real-time analytics. For example, when an ecommerce application stores a transaction in your AWS-hosted structured query language (SQL) database, the data is automatically transformed and sent to Amazon Redshift.