Published at Dec 27, 2024
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Written by Junaid Ahmed
Real-time analytics is key to extracting meaningful insights from live data streams, turning them into actionable ones for decision-making, trend forecasting, predictive maintenance, or alerting and monitoring.
However, choosing a proper database for real-time analytics takes work. With ever-increasing solutions tailored to ever-finer-grained needs and workloads, identifying the features that matter the most and understanding the compromises involved can be challenging.
Some databases are optimized for fast data processing and querying, while others are more flexible and easy to use, facilitating integration with other tools. As usual, your solution will ultimately depend on your real-time analytics use case. This article aims to simplify that choice. We will look at the different options, their features, and how to choose the best real-time analytics database to help you make an informed decision.
Let’s start by defining real-time analytics: simply put, real-time analytics is about processing active data streams in real time. The streams can originate from any activity within an organization, ranging from user interactions with a digital platform to transactions in a store or sensor readings from IoT devices. Traditional analytics depends on batch processing of historical data, while real-time analytics ensures that the insights are derived as data is analyzed.
For this, the underlying database should sustain high insert rates while making new data immediately queryable. It also needs to provide fast, pointed queries of recent data with low-latency responses that enable time-sensitive analytics.
Real-world scenarios often require updating data, introducing late-arriving records, and making changes in real time. Furthermore, as data grows, efficient techniques such as data compression, rollups, and retention policies will help enhance query performance and reduce operation costs. All these abilities will let you process active streams and quickly generate timely, actionable insights.
These systems provide a robust framework for connecting, managing, and analyzing live data streams. With seamless integration and real-time updating, you can build instant alerts, real-time notifications, customized user experiences, and actionable business insights. Let’s look at some of the characteristics of real-time databases and see how they impact a number of industries.
Real-time databases act as frameworks that dynamically manage the incoming data to ensure the consistency and responsiveness of the systems in real time. They help organizations manage high-velocity data and make decisions based on the insights provided.
Consequently, real-time databases allow applications to adaptably respond to internal changes within the environment and at the user's requests or interests. They’re a powerful tool to enable highly responsive systems.
The standout feature of real-time databases is their ability to update data continuously. Many systems update data using batch-processing technologies. Such updates can generate outdated data and delayed responsiveness within a system, often hindering the customers’ experience.
Real-time databases take extra steps to ensure data updates continuously. This constant updating mechanism results in second-by-second accuracy with a much-enhanced immediacy that can answer where, how, and when events have occurred. This is why these databases are the backbone of modern digital systems.
In online applications, real-time databases continuously ingest, process, and analyze vast amounts of information to power critical features like instant fraud detection and personalized recommendations. When users interact with an e-commerce platform, real-time analytics engines immediately process their behavior to tailor product suggestions and trigger relevant notifications. Beyond individual user experiences, these databases provide organizations with an always-on pulse of their operations, offering instantaneous insights into system performance, user engagement patterns, and business metrics.
Traditional relational databases, such as PostgreSQL, are very efficient at handling historical data analysis and complex query execution. While these databases can theoretically handle streaming data, their architecture wasn’t designed with real-time processing as the primary goal.
This means that traditional relational databases often fail to deliver responsiveness for modern applications when meeting the increasing demands of real-time data streams. Their reliance on batch processing implies that frequent updates can create bottlenecks, leading to increased latency and computational overhead that can degrade system performance.
Unlike traditional systems, real-time databases process data the moment it arrives, thus avoiding delays and maintaining data integrity across all integrated systems. Their architecture fundamentally differs by prioritizing write efficiency and rapid data ingestion.
This architectural difference means they can handle millions of events per second without the performance degradation that would typically occur in traditional databases. They often include built-in tools specifically designed for stream processing, such as windowing functions, stream joins, and real-time aggregations, making them naturally suited for applications requiring immediate data processing and analysis.
In sum, real-time databases offer the following advantages:
Writing efficiency: They are designed to cope with fast and continuous data input with minimum delays, processing updates in real-time, even at peak periods. This ensures continuous reliability for high-demand applications such as financial trading or e-commerce platforms.
Consistency: Real-time systems ensure consistency in data integrity across distributed systems without any delay and guarantee smooth synchronization. This becomes crucial in a critical environment like healthcare monitoring, where lives are saved with consistent and accurate data.
Scalability: These databases have been designed for high-velocity data streams. Performance remains consistent and predictable as data volume and velocity start increasing exponentially. This scalability feature supports a business in case of sudden, rapid growth or spurts in user activity related to an event.
Resilience: Most real-time databases are fault-tolerant, allowing recovery mechanisms that ensure integrity and availability of data in case of hardware or network failure; hence, they are reliable for mission-critical applications. These attributes render real-time databases indispensable for various time-bound applications.
This option offers unparalleled control over an organization's data architecture on top of an already existing database. These systems are central to custom real-time analytics setups and allow developers to modify pipelines depending on their needs. In this approach, the design and implementation of various data pipelines are developed using strong tools like Apache Kafka and Debezium.
Apache Kafka is a distributed platform for streaming events with a reputation for high throughput and low latency. It allows for the aggregation and disaggregation of data streams in real time, paving the way for frictionless integrations between data producers and users. Its scalability and bulk volume handling make it a good choice for those with difficult data requirements.
Debezium is a CDC tool that tracks and streams database changes as events into Kafka. This allows for the immediate reflection of updates within the pipeline for real-time data synchronization between transactional systems and analytics tools.
While this option allows for maximum customization, it demands significant resource utilization and operational costs to maintain reliable pipelines and address performance bottlenecks for scalability.
Specialized databases are designed natively for real-time analytics, delivering high performance and scalability for high-velocity data environments. Tools like ClickHouse, a column-store database capable of processing billions of rows with sub-second latency, are optimized for operational dashboards and log analytics.
These databases’ optimized features ensure low latency, high throughput, and efficiency for demanding real-time applications, while their connectors allow you to hook your preferred tools into your system.
Database extensions enhance traditional relational databases for real-time streaming without infrastructure overhauls. PostgreSQL extensions like TimescaleDB transform traditional relational databases into real-time streaming with minimum infrastructure changes. Built on PostgreSQL, TimescaleDB inherits PostgreSQL’s reliability and comprehensive ecosystem of connectors and tools. It’s also equipped with features like continuous aggregates and hybrid-row columnar storage that are capable of taming demanding real-time analytics workloads.
Unlike traditional materialized views, continuous aggregates refresh incrementally with the arrival of new data automatically, reducing computation costs while keeping insights up to date. This feature enables high-performance queries for real-time analytics, especially in cases where data streams occur frequently.
However, the center of TimescaleDB's real-time data capabilities is its hybrid-row columnar storage engine, a hybrid system that supports both rowstore and columnstore formats. New data is ingested into the rowstore for fast inserts, updates, and mutability. Over time, data automatically moves to the columnstore for efficient data compression for large-scale querying and analytics.
Let’s now dive into each option’s features.
Some of the core features of the DIY approach are:
Custom pipelines: You can implement custom features for data transformation, fault tolerance, and message routing for specific use cases.
High resource consumption: This approach requires substantial experience with streaming technologies, architecture design, and operational maintenance.
Scalability through tools: Provides advanced scaling strategies using the latest tooling, from Kafka in distributed messaging to capturing changed data with Debezium.
Higher monitoring requirements: Robust monitoring and alerting mechanisms are necessary to avoid bottlenecks or failures in high-frequency environments.
Let’s cover some of the core features of specialized databases:
Real-time analytics optimization: These databases are capable of handling high-velocity data ingestion with minimal analysis latency.
Built-in connectors: These connectors simplify integration with the existing ecosystem, though they may not support niche tools or systems.
Scalability by design: You can scale quickly in large data environments, using distributed architecture for fault tolerance, multi-region deployments, and ease of operation.
Risks of vendor lock-in: Proprietary systems can reduce flexibility and higher long-term costs.
Some of the key features of database extensions include the following:
Familiar syntax: It enables easier development and adoption by reusing existing knowledge in teams with database systems like PostgreSQL.
Seamless integration: These extensions are compatible with the existing tools and connectors to minimize disruption within the workflow.
Proven scalability: Database extensions can scale effortlessly, enhancing functionality for diverse workloads. Notably, TimescaleDB has grown from 350 TB and 10 billion records daily to petabyte scale, handling 800 billion metrics daily with tiered storage, continuous aggregates, and optimized ingestion.
Hybrid workloads: It supports both real-time streaming and batch processing, making it very versatile in hybrid use cases.
The right approach to implementing data streaming depends on your use case, expertise, and goals. Below, we summarize three key options: DIY solutions, specialized systems, and database extensions. Each has distinct benefits and trade-offs.
A DIY approach gives complete control, which is appropriate for organizations with highly customized data streaming needs. Consider this option when:
Confidentiality management: DIY solutions can establish special security protocols and regulatory standards regarding security in the finance or healthcare sector. A financial institution can create a custom streaming platform to build encryption protocols in real-time transactions to protect data and adhere to regulations.
Custom hardware integration: The DIY model supports seamless integration for infrastructures involving proprietary components. An IoT company using custom sensors can use a DIY approach to build pipelines for processing data from such devices with hardware specifications.
Building and maintaining such systems requires substantial expertise and resources. This means the organization must devote enough time and expertise to scaling up and making it reliable. DIY works when the business wants customization over simplicity.
Specialized systems are designed for performance at a large scale. You should select this if:
Operating at scale: The specialized tool can effectively work with huge volumes and real-time data complexities. For instance, an international e-commerce platform processes millions of transactions per second. When a customer places an order, it instantly updates using these specialized tools; it may also show recommendations in real time.
Advanced capability requirements: Such systems may apply deep customization to optimize data pipelines, including dynamic pricing algorithms or real-time fraud detection in financial systems.
While powerful, they often involve a steep learning curve and significant implementation effort. Specialized systems are suited for high-stakes environments where performance is critical.
Database extensions, which add real-time functionality to existing databases without the complete replacement of the latter, offer a practical middle ground. These are ideal for:
Current system enhancement: Extensions like TimescaleDB can integrate flawlessly, extending real-time processing capabilities into existing systems. For instance, a retail company can use the extensions to get timely point-of-sale insights, adjust inventory, and modify the pricing strategy dynamically.
Minimal disruption: Extensions enable upgrading without rebuilding the infrastructure and balance performance with ease of adoption. For example, a logistics company can use extensions with its existing database, providing real-time tracking of shipments without changing its entire IT system.
While these extensions are unlikely to compete with much more specialized tools in tackling the most complex workloads, they may be a highly scalable and efficient solution for many organizations.
Assess your organization's needs to identify the best option:
DIY solutions: These provide unparalleled flexibility for customized needs but require significant expertise and resources.
Specialized tools: These provide outstanding performance in complex and extensive operations; however, their maintenance requires specific effort and expertise.
Database extensions: These easily extend existing infrastructure into real-time capabilities with minimal disruption.
Each path has unique advantages and trade-offs. Align your selection with your team's competency, operational scale, and performance goals to implement the solution that best supports your business.
Real-time analytics databases transform how organizations extract value from data streams, enabling insights, personalized user experiences, and instant alerts. Among these solutions, specialized tools offer optimized performance for specific workloads, while standard databases provide versatility.
Extended relational databases bridge this gap, combining advanced real-time analytics capabilities with scalability, flexibility, and operational efficiency. TimescaleDB, built on PostgreSQL, exemplifies this blend, adding features like continuous aggregates and a hybrid-row columnar storage engine.
Hybrid-row columnar storage engine enhances TimescaleDB with efficient data ingestion, compression, and large-scale analytics. Optimizations like chunk micro-partitions, SIMD vectorization, and skip indexes boost query performance, reduce storage costs, and enable real-time data processing at scale.
If you need more scale (including infinite low-cost storage for your infrequently accessed data while still being able to query it) and you’d like to experience the full benefits of a managed PostgreSQL platform, Timescale Cloud offers features like query performance insights, an integrated SQL editor, and more. To see it in action, check out this article, where we build an IoT pipeline for real-time analytics using PostgreSQL/Timescale and Kafka.