Today, the average employee juggles 9.39 applications, with the average company spending $343,000 per year on SaaS (a 78% increase from the previous year), seeing an average 39% change in their stack. With thousands of applications available, data growing in volume, and systems becoming more distributed, the ability to integrate data at scale allows organizations to maximize interoperability and efficiency, while reducing costs and complexity. Learn about application integration, how it works, benefits, and what to look for in an integration solution.
Application integration is the process of connecting disparate software applications in order to combine data, share workflows, and communicate in real-time.
Application integration is the process of combining real-time data, processes, and workflows between disparate applications.
Application integration and data integration are often used interchangeably, however, they represent two fundamentally different means of getting an integrated system to work.
One focuses on what's happening NOW and how you need to respond to it in the moment whereas the other shows you trends based on your historic data and what you need to do in the future to do better. One streams actionable data right into your apps while the other lets you pull the insights you need from the data you've accumulated over time.
Data integration is becoming more and more prevalent as businesses of all sizes and types understand its importance. With numerous apps and companies struggling to cope with increasing consumer demand for one-point data storage and access, data availability and data quality are just two of the benefits integrated data can deliver.
Integrated data unlocks a layer of connectivity that businesses need if they want to compete in today’s economy. By connecting systems that contain valuable data and integrating them across departments and locations, organizations are able to achieve data continuity and seamless knowledge transfer. This benefits companies as a whole, not just a team or individual.
By applying data integration, companies gain the ability to take deep dives into business processes and thus promote intersystem cooperation. For example, integrating data from multiple online stores gives a more complete understanding of customer behavior, shopping patterns and payment processing preferences than each individual store managing their own data.
Businesses that apply data integration techniques are able to eliminate errors in data sets used for business intelligence and decision making. Properly integrated data ensures that no errors from overlooked sources occur and reports can be run and accessed in real time.
Finally, one of the primary advantages of data integration is the way it saves time and resources. When the data that an organization needs for its day-to-day operations is spread across multiple systems, teams or apps, it can be very time consuming to gather all of the required data from these disparate sources. For business processes that are time sensitive, manually gathering and integrating data can mean that the data is no longer relevant or accurate by the time it is collected.
When systems are properly integrated, collecting data and converting it into its final, usable format takes less time and allows organizations to make better choices based on deeper understanding of their business data.
There are several applications for integrated data but one of the most common business uses of data integration is the creation of data warehouses.
Creating a data warehouse allows you to integrate different sources of data into a master relational database. By doing this, you can run queries across integrated data sources, compile reports drawing from all integrated data sources, and analyze and collect data in a uniform, usable format from across all integrated data sources.
When all of an organization’s critical data is collected, stored and easily available, it’s much easier to assess micro and macro processes, assess client/customer behavior/preferences, manage operations and make strategic decisions based on this business intelligence.
A medium-size managed services provider typically uses various systems to run its operations, including (but not limited to):
Each one of these systems stores its own repository of information related to the company’s operations. But because each data storage system is different, the same customer may be represented in different ways across the various data sets.
Today, the most successful retailers operate seamlessly across the nation, or even internationally. Imagine having 1,000 brick-and-mortar store locations, an online website, a mobile app, a software backend, and 3rd party resellers. Not only would they need centralized data, they would need real-time data integration across all their outlets:
Today, the most successful retailers operate seamlessly across the nation, or even internationally. Imagine having 1,000 brick-and-mortar store locations, an online website, a mobile app, a software backend, and 3rd party resellers. Not only would they need centralized data, they would need real-time data integration in order to properly ensure inventory tracking, user activity, seamlessly launch sales and specials, employees’ work hours, gain business metrics,, and optimize tracking performance across all outlets.
Therefore, for getting a 360-view of their business operations, the organization needs all the data in a single place and unified format.
In this case, data integration works by providing a cohesive and centralized look at the entirety of an organization’s information, streamlining the process of gaining business intelligence insights. To achieve this, the managed service provider would a process called ETL. ETL (Extract, Transform, Load) is the process of sending data from source systems an organization possesses to the data warehouse where this information will be viewed and used. Most data integration systems involve one or more ETL pipelines, which make data integration easier, simpler, and quicker.
There are several ways to prepare an ETL pipeline – by writing manual code, which is a complex and inefficient task or by making use of enterprise-grade data integration platforms, such as Apache Kafka. These data integration solutions offer significant benefits as they come with a variety of built-in data connectors (for data ingestion), pre-defined transformations, and built-in job scheduler for automating the ETL pipeline. Such tools make data integration easier, faster, and more cost effective by reducing the dependency on your IT team.
One way to achieve that with minimal hassle is by using Kafka Connect – a framework to stream data into and out of Apache Kafka®. You can use several built-in connectors to stream data to or from commonly used systems such as relational databases or HDFS. In order to efficiently discuss the inner workings of Kafka Connect, it is helpful to establish a few major concepts. As an open source framework for connecting Kafka (or, in our case – OSS) with external sources Kafka Connect facilitates integration with things like object stores, databases, key-value stores, etc. Kafka Connect integration is extremely powerful and can be used in any microservice architecture on the Oracle Cloud.
Streamlining data from a database (MySQL) into Apache Kafka® offers significant benefits as they come with a variety of built-in data connectors (for ingestion), pre-defined transformations, and built-in job scheduler for automating the process. Such tools make data integration easier, simpler, and quicker, while reducing the dependency on your IT team.
Distributed systems must have a network that connects all components (machines, hardware, or software) together so they can transfer messages to communicate with each other.
Distributed systems were created out of necessity as services and applications needed to scale and new machines needed to be added and managed. In the design of distributed systems, the major trade-off to consider is complexity vs performance.
To understand this, let’s look at types of distributed architectures, pros, and cons.
The ultimate goal of a distributed system is to enable the scalability, performance and high availability of applications.
Major benefits include:
Every engineering decision has trade offs. Complexity is the biggest disadvantage of distributed systems. There are more machines, more messages, more data being passed between more parties which leads to issues with:
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