Data analytics has changed – batch processing is on its way out. Real-time data processing adoption has risen in importance, and rightly so. It is essential for modern applications that require fast and accurate responses. Be it social media, online gaming, fraud detection, or IoT, real-time data processing drives today’s data-driven world.
However, such processing needs to be powered with low-latency data pipelines that maintain data freshness and allow high user concurrency while processing rapidly increasing volumes of big data.
The Challenges in Building Low-Latency Pipelines
Building low-latency data pipelines is not an easy task. This is primarily because of the complexity of real-time data itself. Real-time data is:
- Always fresh and has to be made available downstream within milliseconds of its creation
- Fast and has only millisecond latency in query responses, irrespective of the complexity
- Highly concurrent and accessed by many users all at once
Real-time data processing needs to filter, aggregate, clean, and transform the streaming data and send it downstream to the users almost as immediately as it is ingested. It involves data processing with large states over unbounded timeframes.
Building low-latency data pipelines that allow high-frequency ingestion and output becomes challenging because:
- Large volumes of data need concurrent processing. Processing and analyzing these distributed data pipelines in real-time require scalable infrastructure.
- Errors, inconsistencies, constant changes in the structure of source data, and poor data quality can trickle downstream and deliver unreliable or inaccurate results. Maintaining data quality levels needs tools for data cleansing and enhancement.
- Numerous sources and data formats make low latency challenging with monolithic data pipelines. The right architecture and design need modularity and scalability to facilitate seamless integration.
Best Practices in Building Low-Latency Data Pipelines
Inefficiently designed or unsupported pipelines cannot facilitate real-time processing. As such, building low-latency data pipelines needs careful consideration. To that end, some of the best practices to follow when building these pipelines are:
Identify the Business Case
An in-depth analysis of the business processes and operations and how the data needs to work to generate value is essential. This will determine all the other decisions, such as the architecture, modularity, data sources, technology stack, etc., needed to build the data pipeline.
Building a data pipeline to analyze streaming data from IoT sensors in logistics, for example, will have separate data ingestion, architecture, and data sources compared to a pipeline created for real-time data processing for anomaly detection in server management.
Data Collection, Storage, and Persistence
Real-time data processing provides immediate insights. To create data pipelines that serve this purpose, having clarity on specific data sources that contribute to the pipeline is vital. It helps make the appropriate data parsers and storage choices. Further considerations need to be given to the data collection methods to make the right technology considerations for the same.
Understanding the characteristics of the data is a necessary consideration as it drives the selection of the storage technology. Structured data, for example, can be considered for traditional relational databases. For unstructured or semi-structured data, data lakes or NoSQL databases are more suitable.
It is also essential to look at data partitioning and sharding strategies to build low-latency data pipelines. These strategies ensure scalability and avoid data bottlenecks that create latency.
Distributed systems and parallel compute
Employing distributed systems become pivotal to create data pipelines for real-time data processing. Distributed systems split workloads across multiple nodes or clusters and enable parallel processing of large datasets.
Parallel processing breaks down large datasets into smaller chunks and can be independently processed by individual nodes within the cluster. Parallel processing and distributed systems provide greater fault tolerance and greater scalability and are integral to building low-latency data pipelines for real-time data processing.
Data Caching and Compressing
Data caching and compressing are essential for ensuring high-performing data pipelines. Data compression enables real-time processing by reducing the size of the data being transferred. This also reduces network latency and ensures faster data transfer rates. Data caching temporarily stores data in the memory that facilitates fast real-time data processing.
Data compression and caching help data pipelines become more performance-driven by helping them avoid repeated processing and freeing up computational resources.
Evaluating the trade-offs between the benefits of compression and caching and incorporating these techniques in the data pipeline architecture ensure that it can manage the large data volumes needed for real-time processing.
Address Data Integration Challenges
Real-time data pipelines need to integrate data from various sources. These include databases, IoT devices, APIs, streaming platforms, etc. Low-latency data pipelines for real-time processing ideally ensure that they can smoothly integrate large volumes of data of different types generated from various sources.
Cloud data warehouses become critical to integrate the growing data volume, variety, and velocity and eliminate the complexities that come from data silos. These data warehouses easily make data available to compute entities and solve the integration challenge. However, addressing the data warehouse design needs assumes importance here for the smooth management and running of the entire data pipeline.
Ensure Fault Tolerance
Low-latency data pipelines need to be designed to be highly fault-tolerant. They must handle data spikes, system failures, or network interruptions while adhering to compliance regulations.
Identifying the right load-balancing solutions helps enterprises leverage low-latency data pipelines that can run on multiple network loads.
Scalability and Performance
Low-latency data pipelines have to be built for horizontal scalability to handle larger amounts of incoming traffic without impacting performance. Choosing a microservices architecture allows enterprises to break down the data pipeline into loosely coupled, independent services.
Each service focuses on a specific task, such as data ingestion, processing, or storage. This adds to the scalability, flexibility, performance, and ease of maintenance of the data pipeline as it makes it easier to update or replace components individually.
Distributed processing and data parallelism, as used in distributed architectures, also make for efficient resource usage and allow data pipelines to achieve fault tolerance and low latency. It is further important to pay attention to access controls and use the right encryption mechanisms for data at rest and in transit. This is to keep sensitive data protected from unauthorized access or tampering.
Monitoring and Alerts
Low-latency data pipelines need robust monitoring and alert mechanisms. Centralized logging to gather and analyze logs from different components of the data pipeline to track system health, data flow, and performance metrics contributes to high performance and low downtime.
Businesses must also configure alerts to proactively notify administrators of any anomalies, processing delays, or system failures. Creating real-time alert mechanisms ensures timely intervention, minimizes downtime, and keeps the pipeline running smoothly.
The Bottom Line
Building low-latency data pipelines needs thoughtful, tailored, and strategic architectural choices to meet the needs of the targeted use case. A well-designed latency data pipeline is a valuable asset that helps enterprises stay ahead of the curve in a dynamic, disruptive market.
Read More:
- Understanding the Complexities of Real-time Data Pipelines
- The Technology Behind Efficient Data Pipelines
