NoSQL Database Use Cases

NoSQL is a good option for organizations with data workloads directed toward rapid processing and analyzing of massive quantities of unstructured data—coined “big data” back in the 1990s. A flexible data model, continuous application availability, optimized database architecture, and modern transaction support are all important for processing big data. In contrast to relational databases, NoSQL databases are flexible because they are not confined by a fixed schema model.

In action, organizations that can process and act on fresh data rapidly achieve greater bottom-line value, business agility, and operational efficiency from it. A typical approach to real-time big data processing ingests new data with stream processing, analyzes historical data, and integrates both with a NoSQL database. For example, big data stored in a NoSQL database can be used for customer segmentation, delivering personalized ads to customers, data mining, and fraud detection.

Examples of NoSQL for Big Data Use Cases

Tens of billions of IoT devices – such as mobile devices, smart vehicles, home appliances, factory sensors, and healthcare systems–are now online. These devices continuously generate a massive amount of diverse, semi-structured data—approximately 847 zettabytes—that NoSQL databases are better equipped to ingest and analyze than their relational cousins. There are three ways to consider this:

Scalability. Scalability is difficult for SQL databases because IoT use cases often experience unpredictable traffic bursts and are frequently write-heavy to start with. NoSQL databases are a good option for managing system load when easy scaling of write capacity is a priority.

Consistency. Although relational databases deliver strong consistency guarantees, IoT applications are often well-suited for eventual consistency models.

Flexibility. While validation and schema capabilities are built into SQL databases, IoT data often needs more flexibility. NoSQL databases allow users to push schema enforcement logic to application code.

Examples of NoSQL for IoT Use Cases

NoSQL may offer better affordability, availability, performance, scalability, and flexibility for ecommerce applications compared to relational databases. Most ecommerce applications are characterized by frequent queries, massive, rapidly updating and expanding online catalogs, and huge amounts of inventory.

NoSQL databases often respond more rapidly to queries and are known for their cost-effective, predictable, horizontal scalability and high availability. With NoSQL databases, organizations can:

• Handle massive volumes of data and traffic growth
• Scale easily for a good price
• Analyze inventory and catalog in real time
• Provide catalog refreshes more rapidly
• Expand online catalog and product offerings

Examples of NoSQL for Ecommerce Use Cases

Except for very small datasets, extracting high performance for time series data with an SQL database demands significant customization and configuration—and any such configuration is nontrivial.

Time series data is unique in that it is generally monitoring data gathered to assess the health of a host, system, patient, environment, etc. To optimize for time series use cases, NoSQL databases typically add data in time-ascending order and delete against a large range of old data. This ensures high query and write performance.

SQL databases are generally equally focused on creating, reading, updating, and deleting data, while NoSQL is less so. In addition, in contrast to the more loosely structured NoSQL database, SQL databases are typically designed with the ideas of atomicity, consistency, isolation, durability (ACID principles) in mind.

Time series databases typically accommodate time series data: they collect data in time order in real-time, and accommodate for extremely high volumes of data by holding it as immutable and append-only. Relational databases accommodate only lower ingest volumes, and are optimized for transactions. Overall, NoSQL databases trade ACID principles for the basic availability, soft state and eventual consistency (BASE) model, depending on their particular use case. In other words, the important notion for the time series data is the aggregate trend, not a single point in a time series, generally speaking.

Examples of NoSQL for Time Series Data Use Cases

To create differentiating, engaging digital customer experiences, it is essential to build on time-critical, data-intensive capabilities such as user profile management, personalization, and a unified customer view across touch points. This massive load of behavioral, demographic, and logistical data taxes RDBMS infrastructure that is designed to scale-up in different ways.

Distributed NoSQL databases allow users to manage increasing attributes with less work, scale more cost-effectively, and enjoy reduced latency—the essence of satisfying online interactions for users in real-time. A personalized, high-quality, fast, consistent experience is no longer a standout feature; it’s what customers demand, across all devices.

NoSQL platforms help deliver positive customer support experiences across multiple verticals by capturing data from massive quantities of omnichannel interactions and relating it to the accounts and service status of individual customers. NoSQL databases:

• Allow for expanding customer bases with extremely low latency and fast response times
• Handle structured and unstructured data from a range of sources
• Scale cost-effectively by design, and manage, store, query, and modify massive quantities of data while delivering personalized experiences
• Flex to enable innovations in the customer experience
• Seamlessly collect, integrate, and analyze new data in real-time
• Serve as the backbone for artificial intelligence (AI) and machine learning (ML) engine algorithms that drive personalization with recommendations

Examples of NoSQL for Retail Use Cases

The bulk of social networking platforms consist of posts, media, profiles, relationships, and APIs. Posts allow users to share thoughts, while media allows them to share videos and photos. Profiles store basic user information and relationships connect them. And through APIs the platform and users can interact with other sites and apps. These features demand that social network data is more flexible—and more difficult to process.

Massive amounts of data present social media platforms with both daily maintenance and development problems. Storing huge quantities of data in SQL databases makes it impossible to process unstructured and unpredictable information. Social media networks demand a flexible, occurrence-oriented database that operates on a schemaless-data model—something impossible for SQL databases. Also, the vertical scaling demands of SQL databases require enhancing implementation hardware, which makes processing large batches of data expensive.

NoSQL can store generic objects, such as JSON, and support huge volumes of read-write operations. This contributes to data consistency across the distributed system, making NoSQL databases a good option for processing the big, unstructured patterns of data access typical of social media platforms.

Examples of NoSQL for Social Media Use Cases

To react in real-time to a threat landscape that evolves constantly, cybersecurity demands speed and scale. To collect, store, and analyze the billions of events that reveal insight into the activities of malicious actors, cybersecurity providers are adopting cloud-native infrastructure. There are several reasons why a high performance low latency NoSQL database offers a cybersecurity advantage, most linking back to improved speed and scalability:

Intrusion detection. Greater speed supports real-time analytics and insights users can compare rapidly to events and operational data contained in a single database to detect problems.

Threat analysis. Real-time updates enable more proactive responses to security breaches and other attacks—including prevention.

Compliance and governance. A NoSQL structure can collect and store events and telemetry when deployed across diverse topologies, either on-premises or in the cloud, to ensure compliance.

Virus and malware protection. Enables machine learning and file analysis to identify malware within harmless content to defend users and endpoints from threats and intrusions.

Examples of NoSQL for Cybersecurity Use Cases

Ensuring only authentic users have access to applications and protecting sensitive personal data is a top priority that is heightened for banking, financial services, payments, and insurance.

It is sometimes possible to identify anomalies and patterns to detect fraud in real-time or even in advance. This demands real-time analysis of large quantities of both live and historic data, including environment, user profile, biometric data, geographic data, and context. For example, a $500 withdrawal may be typical until it occurs after hours in the wrong zip code.

Reputational stakes are amplified with mistakes over social media, yet excessively high false positive rates hurt the customer experience. This is why a fast and highly available NoSQL database is so important to support complex data analysis of website interactions, the CRM system, historical shopping data, and other data that fraud detection and identity authentication demand.

Examples of NoSQL for Fraud Detection Use Cases

The speed that NoSQL databases are well-suited to deliver is a critical competitive advantage for AdTech and MarTech businesses:

SLAs. To meet strict SLAs, these platforms must capture ad space during page loads—and this demands single-digit-millisecond latencies.

Real-time bidding. Consistently responsive, available databases allow users to win more available ad inventory and avoid latency spikes.

Precision ad targeting. High-volume ad service based on revenue optimization, impressions, and campaign goals can allow a team to target audiences and determine the most engaging content for individual users rapidly.

Highly scalable personalization engines. AdTech and MarTech services rely on personalization engines. These engines analyze behavioral, demographic, and geo-location data in real-time to ensure each user has a tailored experience each time they visit.

Real-time analytics. Drive real-time decision-making with actionable insights extracted from masses of data.

Mobile device metadata stores. A geographically-distributed metadata store for mobile devices can improve user conversion and retention.

User behavior and impressions. Engage in real-time capture and analysis of clickstreams to identify trends, understand sentiment, and optimize campaigns.

Machine learning. Run analytics and operational workloads at high velocity on the same infrastructure against the same datasets.

Examples of NoSQL for Fraud Detection Use Cases