Making data work for you: Challenges, innovations, and lessons learned

By Clive Dsouza

It’s becoming increasingly essential for enterprises to analyze and process large amounts of data in real time in order to survive. This, however, implies that the conventional data management strategies are inadequate, leading to delayed decisions, ineffectiveness, and, consequently, lost opportunities.  

New strategies and best practices are needed for engineering teams to create high-performance data platforms that address these issues. 

Managing Real-Time Data Engineering Challenges    

First, let’s discuss the challenges unique to real-time data engineering.  A major challenge is the large amount of data that modern applications produce. Many traditional databases are not able to maintain the necessary performance, which results in slow responses and poor user experience. To solve this problem,  a company may want to implement a distributed database system that is able to handle large amounts of data in an efficient manner without compromising on speed or reliability. By Implementing a distributed database like AWS Aurora DSQL or GCP Cloud BigTable, your system will not only be scalable but highly fault-tolerant as failure in one region won’t affect the data if it is deployed in multiple regions.

Enterprises also acquire data from different sources, such as IoT devices, social media, and CRM systems. Each source can have a different data format, structure, and update frequency which makes the integration process challenging. To end this, companies can employ an ingestion layer API which acts as a single source of entry that can format the data as per a strict schema using Apache Avro, streaming platforms like Apache Kafka and integrations to effectively collect and process data in real time.

Poor data quality can also result in wrong decisions being made and poor performance. Erroneous or tolerant data can distort the analyses and affect business plans. To reduce this risk, real-time workflows should be created that are able to monitor, clean, and validate the data being received before it is used for reporting and analysis. This can be implemented by creating a rules engine to filter out the irrelevant data, perform any deduplication of the data. Integrating a stream-aware and reactive integration pipeline like Alpakka will ensure that the pipeline is robust and adjusts to the data changes.

Best Practices for Creating a  Scalable Data Platform    

As businesses use real-time data more frequently, they require a sustainable and effective data platform to support performance, reliability, and accuracy. A good platform ensures that data is integrated without any problems, is easily modifiable to the business, and produces accurate insights. These best practices are focused on the strategies for a strong and scalable data infrastructure. 

• Selecting the Proper Database Solutions: It is important to select a scalable database solution to ensure that large amounts of data can be handled without deteriorating performance. At my current company, we focus on distributed databases like Google Cloud Storage (GCS)  to guarantee data availability and processing speed as the amount of data grows.

• Developing a Flexible Data Architecture: An effective data pipeline should be able to adapt to changes in the business. We can design a flexible pipeline architecture that will enable us to incorporate new data sources, modify workflows, and increase the processing capacity as and when needed. If Google Cloud is used as a solution for storing and retrieving your data, then an effective data pipeline can be built using either Apacha Kafka along with Akka or by using Google Cloud DataFlow along with Apache Beam to do batch and stream data processing.

• Applying Real-Time Data Quality Controls: Having clean real-time data is extremely important. We can prevent errors from propagating through the system by embedding automatic quality controls into the pipeline, thus reducing the risk of making the wrong decisions based on faulty analytics.

The Impact: A High-Performance Real-Time Data Platform

The efforts we have made in real-time data engineering have led to the development of a robust platform that supports our marketing automation efforts. We can collect, process and analyze data from various sources in real time, which provides us with accurate and useful information that can be used to develop effective marketing campaigns.

The following are the lessons we have learned from this process:

• Invest in Scalable Databases: Real-time data processing can be quite demanding and thus suitable infrastructure is crucial to avoid performance bottlenecks.
• Design Flexible Pipelines from the Start: A fixed pipeline is expensive to rewrite; a flexible one can quickly adapt to new data and requirements.
• Emphasize Data Quality: Real-time quality control of the data fed into the system helps minimize the effects of data errors that can otherwise misguide business decisions.   In the future, we plan to expand our data platform by integrating advanced technologies. One potentially helpful area is the application of ML for automated data cleaning and quality control using AI to correct errors in real-time. Also, we intend to integrate natural language processing and predictive analytics to uncover more patterns and enhance the decision-making process.

To build a real-time data platform for large enterprises, overcoming challenges while still embracing innovation is necessary. By adopting the best practices, building solutions, and refining your approach,  your organization can leverage data to achieve its goals and increase your business value.

About the author

Clive Dsouza is a seasoned technology professional with over a decade of experience spanning retail, insurance, banking, education, and IT. He specializes in developing scalable, high-performance software solutions using React, TypeScript, GraphQL, and cloud-based architectures, mainly front-end and back-end development. With a strong background in real-time data tracking and microservices, Clive has contributed to significant projects at CreditKarma, Lowe’s, Target, and CitiusTech, where he has led initiatives in digital transformation, performance optimization, and AI-driven financial solutions.