The Developer Toolkit serves as an in-depth resource for technical professionals focused on building applications with real-time data streams. This guide offers a detailed look at how to integrate various real-time data sources, messaging platforms, and APIs.

Here are the 5 key takeaways from the article:

  • Developer Toolkit: Designed for professionals building applications that require real-time data stream integration.
  • Solution to Manual Integration Challenges: allows the creation of APIs with minimal coding and flexible deployment, addressing challenges such as inflexible data processing pipelines and scalability limitations faced during manual data integration.
  • Real-Time Data: Essential for sectors like healthcare, logistics, manufacturing and tech, providing real-time insights and enabling quick decision-making.
  • Challenges in Manual Integration: Developers may face issues such as inflexible data processing pipelines and scalability limitations when integrating data sources manually.
  • Event-Driven Architecture: facilitates the implementation of event-driven architecture, which uses events to initiate communication between microservices, improving app performance in the context of real-time data integration.

In this article, we cover essential topics like event-driven architecture, data streaming pipelines, and real-time analytics. With that in mind, this guide aims to provide developers with the technical know-how required for effective real-time data integration. 

Table of Contents

The Rise of Real-Time Data in Application Ecosystems

Real-time data pipelines have had a huge impact on organizations across the world, particularly those that operate in sectors such as healthcare and tech where real-world insights and quick decision making is essential. The advantages of real-time data are numerous, giving businesses a competitive edge and helping them to grow. 

This is especially the case for businesses that rely on apps to serve their clients and rely on data to empower their developers to deliver optimal performance and a user-friendly experience. 

Below are nine key advantages of real-time data in application ecosystems. 

  1. Real-time insights enable organizations to make strategic decisions based on the latest market data or industry changes.
  2. Speed up decision-making by discovering new opportunities as soon as possible, or possibilities to optimize applications based on the latest user behaviors and trends. 
  3. Address issues quickly such as problems that impact the user experience or affect customer service response times. 
  4. Data analysis is scalable as huge amounts of streams (hundreds of thousands) can be supported by stream processing with no loss in performance. 
  5. Accuracy is improved as real-time stream processing means large amounts of data can be analyzed in smaller intervals, ensuring valuable insights aren’t missed.
  6. Avoid delays as real-time data streaming enables immediate responses to an event or market change, delivering a better user experience. 
  7. Enhance the user experience by accessing data across all verticals within an organization, helping to build more relevant, interactive, and intuitive applications that meet the needs of the user.
  8. Reduce server costs thanks to the way real-time data streaming is distributed, making it less resource-heavy when compared to traditional methods. 
  9. Added security due to native encryption features to help protect sensitive data and customer information. Developer Toolkit - At a Glance: is a cloud-based ETL (extract, transform, and load data) solution that allows for the creation of visualized data pipelines for automated data flows that can work across a plethora of sources. 

The Developer Toolkit makes it possible to create APIs (application programming interfaces) and integrate the platform’s data streaming capabilities with minimal coding while offering unrivaled levels of speed and security. This makes it ideal for developers looking to build scalable projects that require accurate real-time data and support all the major data connectors such as SQL, MySQL, and BigQuery. 

When working with's Developer Toolkit, you'll find that your data streaming pipelines can handle various file formats just as efficiently as any specialized document processing software. Removing the need for excess third-party tools also simplifies workflows.  

Popular for its user-friendly ETL features and ETL drag-and-drop interfaces, the simplicity offered by allows developers to automate tasks such as data warehousing and observability. This level of functionality facilitates effortless sharing of data across multiple applications, users, and data pipelines. 

Key Features

  • Create an unlimited amount of APIs with no restriction on calls or data transfer.
  • Build APIs with minimal/ no code.
  • Flexible deployment of APIs (cloud, on-premises, or hybrid)
  • Implement security features like OAuth 2.0 and API keys.
  • Data can be shared across a range of apps and services.
  • Compatible with all the top databases.
  • Comprehensive documentation to assist developers.
  • Excellent customer support. 

Connecting Real-Time Data Sources and Messaging Platforms

When connecting real-time data sources and messaging platforms, a developer may face several challenges that need to be overcome. These challenges may include inflexible data processing pipelines, limitations on scalability, and network performance issues due to a lack of control, particularly when it comes to cloud environments. 

To overcome such challenges, businesses and their developers should adhere to a number of best practices to ensure applications are optimized, future-proofed, and deliver the intended results. Furthermore, following key guidelines and carrying out the necessary testing can also ensure the accuracy and reliability of data, delivering a flawless user experience.

If you want to improve your real-time data source integration endeavors, apply the following tips. 

  1. Consider the long-term goals of the app and how it may need to adapt over time, including how real-time data may impact functionality and user experience.
  2. Use a data collection platform such as Amazon S3 to unify your data so that it can be sorted, categorized, and checked for any errors.
  3. Define an integration goal such as improving data security or centralizing data. Without such a goal, real-time data streams will not be used to their full potential and any benefit may be minimal. 
  4. Deploy a Master Data Management (MDM) system to handle big data from various sources, providing an overview of all data so it can be managed more effectively. 
  5. Establish a Strategic Data Management Plan to set clear goals and to determine how you will measure the success of the project. For a successful plan, all departments within an organization should be involved, stating what they hope to gain from real-time data. 
  6. As you integrate real-time data sources with messaging platforms, one factor that should not be overlooked is the ecosystem's security and reliability. Operating in a safe SaaS environment ensures that your data is not only securely transmitted but also reliably maintained. prioritizes your data's integrity, offering multiple layers of security protocols and advanced encryption methods to safeguard your data streams.

Case Study: Fuze Cloud Communication Software

The cloud-based communications and collaboration platform provider Fuze required a solution to facilitate business conversations, delivering voice, video, messaging, and conferencing services via mobile apps. Fuze gathers data from a large number of sources and events which is centralized in an Amazon Redshift data warehouse, and as the platform’s user base grows, so does the number of queries sent to the data warehouse.

This increase in queries required higher processing speeds and data analysis to provide query insights, which is why Fuze turned to to overcome these challenges. With, Fuze was able to achieve better disk utilization and have full visibility regarding issues that may slow queries or make a data set unavailable.

In addition, the platform enabled Fuze to optimize ETL pipelines using real-time data streams, improving the performance of the data warehouse by highlighting problems such as sudden CPU usage spikes. 

APIs - The Backbone of Dynamic Data Integration

In the past, data was often entered manually between systems, which increased the likelihood of errors. APIs solve this problem by automating data entry while checking errors in real-time and also sharing this accurate data automatically. 

Using APIs for dynamic data integration not only ensures accurate data but it is also a more cost-effective solution, as automation saves valuable time and resources. By streamlining data sharing, communication also becomes more efficient between developers and users, while connecting applications via a single platform like significantly improves the monitoring of data transfers. 

Data indexing is vital in terms of optimizing data query performance in API integrations and creating data structures that enable quicker retrieval of information from the database. However, this is just one of many techniques that improve data stream integration, others include using appropriate data types, optimizing subqueries, and regularly testing/ benchmarking queries. 

Event-Driven Architecture and its Role in Data Streaming

Event-driven architecture is used in modern applications that are made up of microservices, using events to initiate communication between each service, even if they use different programming languages or are run from different environments. 

Events represent a state change or an update and issue a response to a microservice so it can perform its intended function. 

Event-driven architecture consists of three main components:

  • Event producers: Publish the event to the router.
  • Event Routers: Filters and pushes the event to the relevant microservices. 
  • Event Consumers: The services that handle the event. 

This allows microservices to be scaled, updated, and deployed independently from each other, resulting in better app performance. This technological evolution sees event-driven architecture replace traditional request/response architecture where services would need to wait for a response before moving on to the next task. 

Three common use cases for event-driven architecture in real-time applications include: 

  • Cross-account and cross-region data replication, using an event router to transfer data to systems across multiple regions and accounts, allowing services to work independently from each other. 
  • Resource state monitoring and alert systems, informing administrators of errors, updates, and status changes.
  • Sharing information across systems that are running on different stacks so that they can remain decoupled.

How Can Help's Developer Toolkit can play a key role in building applications with real-time data streams with minimal coding while maintaining high performance and impressive levels of security, supported by its ETL features. With built-in monitoring tools, developers can have full visibility of integrated data streams which can then be automatically shared across a range of apps and services.

Interested? Then take advantage of our 14-day free trial to unleash the power of big data with Or if you're looking to address your unique business requirements, schedule an intro call with one of our experts. Our team will help identify your pain points and discuss potential solutions with you one-on-one.