11 Database Design Tips to Ensure High Performance of Big Data Handling

Choose between Referential, No-SQL, or Hybrid Data model

There are three common types of data model – referential, No-SQL, and Hybrid. Each of them excels in handling different data types, provides different standards of data organization and storage, and serves specific purposes of using – and these are major factors to consider when choosing between them. Let’s examine each type in more detail:.

• The referential (or relational) data model organizes data into tables with rows and columns, establishing relationships between them. It employs a structured schema with predefined data types and enforces integrity constraints. This model is widely used in business applications, where structured data relationships are essential.
• The NoSQL data model, on the other hand, is schema-less and flexible, accommodating unstructured and varied data types. They excel in distributed, scalable, and high-performance scenarios, making them ideal for applications with large volumes of data, rapid growth, and dynamic data structures, such as social media platforms, real-time analytics, and content management systems.
• Hybrid data model combines features of both traditional relational and NoSQL databases, providing a balance between structured and unstructured data needs.

Helpful tip: Whatever data model you use, regularly review and its update together with indexing strategies  ensures they remain aligned with the changing requirements of your applications. Leverage various tools for diagnostics and optimization as well as stay informed about updates and best practices for your chosen database technology to benefit from improvements and security patches. 

Choose between Column-based and Row-based Databases 

Column-based and row-based databases differ in their data storage and retrieval approaches. In row-based databases, data is stored and retrieved by rows, making them efficient for transactional and OLAP workloads where full records of data are frequently accessed. Column-based databases store data by columns, enabling faster analytics and aggregation queries on specific attributes. They excel in OLAP scenarios where analytical queries involve only a subset of row columns. While row-based databases are suitable for transactional processing, column-based databases optimize analytical processing, offering improved performance for read-heavy operations like big data analytics, data warehousing, and business intelligence.

Helpful tip: Optimize column-based database by carefully selecting and indexing columns based on the most frequent query patterns. When implementing row-based databases, focus on transactional consistency. Design efficient indexes and ensure proper normalization for optimal performance in transactional workloads.

Design independently Cold storage from Hot storage

Hot storage is tailored for frequent and rapid access to critical data, and is usually more expensive. In contrast, cold storage is more cost-effective and focuses on long-term archival of infrequently accessed data. By separating these storage layers, you ensure that the more expensive and responsive hot storage is reserved for mission-critical and frequently accessed data, while cost-effective cold storage accommodates less frequently accessed data, contributing to overall performance, scalability, and cost savings.

Helpful tip: Implement automated data lifecycle policies to seamlessly manage data migration between the Cold and Hot storages.

Build a Distributed Database Architecture

Distributed database architecture involves spreading data across multiple servers or nodes, enabling horizontal scaling and parallel processing. In this setup, each node manages a subset of the data, improving both storage and processing efficiency. The combined processing power of multiple nodes makes distributed architectures well-suited for the demands of big data environments, allowing the system to distribute workloads, balance resources, and scale seamlessly as data volumes grow. 

Helpful tip: Prioritize simplicity and ease of maintenance in your distributed database architecture. While the inherent complexity of distributed systems is inevitable, keeping the design as straightforward as possible will contribute to easier troubleshooting, debugging, and system maintenance. Clear documentation and well-defined processes for scaling, updating, and handling failures will help to keep the distributed architecture robust, manageable, and efficient over time.

When applying data partitioning and sharding, you have to choose between Master-slave and Cluster architecture. Master-slave involves a single master node handling write operations and replicating data to slave nodes, handling read operations. In contrast, Cluster distributes tasks across interconnected nodes for further parallel processing and improved fault tolerance. Clusters provide higher scalability and better handling of heavy workloads compared to the Master-slave architecture, distributing all types of operations across all available nodes.

Helpful tip: When implementing data partitioning and sharding, consider the distribution of data based on access patterns and workload characteristics. Make sure that your queries read a minimum amount of partitions, or distributed well across shards to reach the right performance and balance. Additionally, plan for future growth enabling the addition of new shards without disrupting the existing system. 

Optimize Indexing

Index optimization involves strategically designing and maintaining indexes in a database to enhance query performance without introducing unnecessary overhead. Efficient indexing reduces the time and resources needed for query processing, which accelerates data retrieval and analysis. By carefully selecting and managing indexes, developers can make it significantly easier to handle the large and complex datasets typical in big data scenarios.

Helpful tip: Strike a balance between having too few and too many indexes.Tailor your indexing strategy to match the most common and performance-critical queries. Consider leveraging database tools and advisors to help identify underused or redundant indexes. Strive for simplicity and avoid over-indexing, as excessive indexes can impact overall performance. 

Optimize Query Processing

Query processing optimization is refining the execution of database queries to improve performance. In the realm of big data, by fine-tuning queries and employing techniques like caching, pre-aggregation, and reducing the amount of data operation, developers can significantly cut processing times, ensuring that analytical tasks on massive datasets are executed more swiftly. By continually optimizing query processing, you ensure that your big data analytics system delivers timely insights while maximizing the utilization of computational resources.

Helpful tip: Start with the query plan analysis, understand your query patterns, and optimize accordingly. Analyze the most frequent and resource-intensive queries (or parts of the query), focusing on indexing, query structure, and data retrieval strategies. Leverage caching mechanisms for repetitive queries, reduce the amount of data operations while retrieving data, and explore parallel processing techniques to distribute workloads efficiently. You may also opt for data engineering as a service to analyze query patterns and optimize processing time and resource allocation.

Implement Data Compression Techniques

Data compression stands for reducing the size of data to save storage space and enhance transfer efficiency. In the context of big data, efficient data compression is crucial for optimizing storage costs and improving overall system performance. Smaller data footprints lead to quicker data transfers, reducing the time needed for processing and analysis, ensuring a more efficient and cost-effective big data infrastructure. 

Helpful tip: Choose data compression techniques based on your specific use case and data characteristics. Experiment with different compression algorithms and settings to find the optimal balance between compression ratios and processing overhead. Prioritize the compression of large and infrequently accessed data while considering the impact on frequently queried data. Regularly evaluate and adjust compression strategies as data volumes and patterns change over time. 

Create Scalability Plan

Scalability planning involves anticipating and preparing for the growth of a system, ensuring it can handle increased data volumes and user demands. In the realm of big data, scalability planning is crucial for accommodating expanding datasets and processing requirements. It allows for seamless horizontal scaling, adding more resources or nodes to the system. This proactive approach ensures that the infrastructure can grow dynamically, maintaining performance as the scale of big data applications increases over time.

Helpful tip: Build your scalability plan by designing for both vertical and horizontal scaling. Choose technologies that easily allow adding resources or nodes to handle increased loads. Implement monitoring, alerting and automated scaling triggers to adapt dynamically to changing demands. Regularly test scalability through simulations and performance benchmarks, identifying potential bottlenecks before they become issues. 

Implement Fault Tolerance and Redundancy

Fault tolerance and redundancy are mechanisms that ensure system reliability and data availability in the face of failures. In big data environments, where data volumes are massive, they mitigate the impact of hardware malfunctions, network issues, or other disruptions, preventing data loss and downtime. A well-executed fault-tolerant and redundant strategies help organizations maintain the integrity of their data, ensure continuous availability, and handle the challenges of large-scale data processing with greater resilience.

Helpful tip: Implement fault tolerance and redundancy with a comprehensive approach that includes data replication, clustering, and automated failover mechanisms. Implement monitoring and alerting to know and react to failures without delays. Regularly simulate and test failure scenarios to ensure the system’s resilience. Leverage cloud-based solutions that inherently provide redundancy and availability features. 

Final remarks

Major reasons that hamper performance of big data databases include inefficient data storage and retrieval methods, inadequate hardware resources, poor data partitioning and distribution strategies, suboptimal query optimization, and bottlenecks in data processing pipelines. We’ve explored eleven best practices in database design that collectively help in crafting databases capable of seamlessly navigating massive data sets. By prioritizing performance in the design phase, organizations pave the way for resilient big data systems that deliver timely and accurate insights. Regular maintenance, simplicity in design, and continuous monitoring will keep the whole infrastructure capable of efficiently managing and analyzing versatile big data sets.

Contact Lightpoint team if you need to develop a custom database to tackle massive datasets of any type.