In today's big data era, every business is looking to become data-driven. After all, organizations that can act on real-time data can unequivocally achieve unprecedented success and differentiate themselves from their competitors.
But collecting data in real-time and building robust data pipelines is easier said than done. There are complexities in building (and managing) real-time data pipelines that must be duly considered.
The Significance of Real-Time Data
It's no secret that more and more organizations are relying on data to make personalized and customer-centric business decisions. With global data volumes expected to grow to 180 zettabytes by 2025, there's a lot that companies can achieve by analyzing this data and acting on the insight it provides.
But with market trends, business dynamics, and customer needs constantly evolving, there is an urgent and critical need to act on real-time data. Real-time data allows enterprises to:
- Make faster decisions using the most updated information about processes, employees, people, trends, and more, and stay agile in a dynamic and rapidly changing business environment.
- Process critical data immediately with minimal delay and add significant value to the business.
- Stay updated on the current state of business operations and respond promptly to critical situations or changing circumstances.
- Enhance the customer experience by enabling personalized and timely interactions and improving customer satisfaction and loyalty.
- Optimize resource allocation based on past utilization patterns, current demand, and future needs.
- Monitor processes in real-time to identify bottlenecks, inefficiencies, or deviations and take corrective actions promptly.
- Examine systems in real-time to detect anomalies and ensure the safety and security of people and processes.
The Top 6 Challenges of Real-Time Data Pipelines and Possible Solutions
As we've seen above, real-time data offers numerous benefits. But building and managing real-time data pipelines comes with various complexities, such as:
1. Data Volume and Velocity
Real-time data pipelines deal with high volumes of data that are coming in continuously and at high velocities. Processing and analyzing these distributed data pipelines in real time requires robust and scalable infrastructure.
Adopting frameworks like Apache Hadoop is a great way to enable the distributed processing of large data sets. Designed to scale up from single servers to thousands of machines, you can use Hadoop to gather and process data from varied data sources, ensure required levels of availability, and ensure high throughput.
Tools like Apache Spark can help to build faster and more reliable pipelines. By rapidly querying, analyzing, and transforming data at scale, data engineers can operate with enormous datasets and process massive volumes of data quickly.
2. Data Quality
Ensuring data quality is critical in real-time data pipelines. Errors or inconsistencies in incoming data can quickly propagate downstream, leading to inaccurate and unreliable results.
To maintain quality levels, you must invest in tools and processes for data cleansing. Tools like Databricks can help process, clean, and analyze datasets and build and deploy workflows, models, and dashboards. By unifying all data on one platform, it can ensure consistent governance and analytics and speed up innovation. These tools can ensure only correct, complete, and accurate data is fed into the pipeline. In the long run, they can help produce better results and improve business decision-making.
3. Latency
In real-time data pipelines, data must be processed and delivered with minimal latency. Achieving low-latency processing is challenging, especially when sources are numerous and formats are complex and dissimilar.
To overcome the latency issue in real-time data pipelines, microservices architectures can help a great deal. By running microservices at the edge, you can enable modular and scalable data pipelines and facilitate seamless integration of data in real-time.
Data teams can also invest in tools like Apache Kafka to enable high-performance data pipelines. Kafka can help in delivering high throughput and availability via distributed, durable, and fault-tolerant clusters.
4. Integration
Real-time data pipelines often need to integrate data from various sources, including databases, APIs, IoT devices, and streaming platforms. Integrating and harmonizing data from different sources can be complex and time-consuming – especially in the presence of departmental or data silos.
Investing in cloud data warehouses is a plausible way to integrate growing volumes of data from various sources. These warehouses can ensure the effective and efficient management of the entire data pipeline. You could also build a robust data fabric to make data available to all computing entities within your enterprise via a common interface.
5. Security and Fault Tolerance
Ensuring data reliability and consistency is challenging in distributed and real-time environments. Real-time data pipelines must be fault-tolerant to handle system failures, data spikes, or network interruptions. They must also comply with the required compliance regulations, especially when dealing with sensitive data.
To overcome the issue of fault tolerance, you can invest in load-balancing solutions. By allowing applications to run on multiple network loads, these solutions minimize the chances of a single point of failure – thus making the data pipeline more resilient to spikes that would otherwise cause slowdowns. You must also establish a strong data security policy and update your data governance policies.
6. Scalability
As data volume and velocity increase, real-time data pipelines must be designed to scale horizontally to handle the growing workload efficiently. But rigid, on-premises systems often restrict businesses from achieving the required levels of scalability.
Building a serverless real-time data pipeline can help you manage growing volumes of data in the cloud – without writing a single line of code. With data needs constantly changing, serverless computing can improve your ability to swiftly aggregate data from different sources and scale it without interruptions.
In a Nutshell
Real-time data pipelines are becoming increasingly popular with businesses that are aiming to become data-driven. But building real-time data pipelines is a complex and time-consuming process. If you want to maintain data consistency and coherency and ensure your data pipelines adapt to evolving business needs, you must be cognizant of the challenges and possible solutions.
Develop robust real-time data pipelines with Wissen and efficiently deal with growing volumes of data with consistency, security, and availability.
