There are two major techniques to process information when mentioning big data. They are: Real time data integration and Batch-based data integration. Besides, understanding which data integration approach is best for specific cases is critical to processing big data in the most efficient and cost-effective manner. With this reason, in this article, ArrowHiTech will give everything you need to know about Real time data integration solutions. And, you will also know which is better between real time data integration and Batch-based data integration. Hence, let’s explore with us right now!
What is real time data integration?
First and foremost, you need to know the definition of data integration. In fact, data integration is the process of data integration entails acquiring and merging information/ data from many sources. Businesses will utilize data integration to combine data from multiple sources into a single and consolidated picture.
In terms of “real time data integration” refers to the processing and transfer of data from one system to another in real time. It’s extremely important to remember that defining actual time is more difficult than we imagine. Because moving, transforming, and processing data takes time, real-time data integration is rarely completely immediate. Therefore, delays of fractions of a second happen commonly.
Plus, companies that wish to perform real-time analytics need data warehouses that are completely up-to-date. Moreover, they want complete control over their supply chain processes and real-time data integration. Besides, many businesses can operate their operations using batch processing technologies and system integration. When batch processing is utilized for data integration, the solution collects, stores, and processes data in batches rather than individually. If your work operations do not necessitate immediate data transfers, batch processing is the ideal choice.
What’s more, understanding by the definition of real-time integration, each piece of data is processed and shared as soon as it is created. Then, you should think about it carefully because such integration solutions might cause burdens to the source systems.
This is the reason why you can wish to have a look at some of the best practices in this field. It can be difficult to choose between real-time integration, near-real-time integration, batch processing, and other triggered web service solutions. In short, the best option will be mainly dependent on your business procedures and requirements.
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Limitations of real time data integration solution
In many case, real time data integration can be difficult to implement because following reasons:
- Firstly, it may be necessary for firms to upgrade their IT infrastructure. As a result, it could be prohibitively expensive.
- Secondly, this integration will be more hard and complex if data quality and integrity are inadequate.
- Thirdly, the amount of information gathered can be enormous, especially in the age of big data. Moreover, present necessitates extensive data management that only a few companies are capable of at this time.
While the potential of real-time data integration is obvious, putting it into practice is still a challenge. Nowadays, data flows are extremely crucial, which grab a lot of people’s attention. In fact, the continuous flow of data from an increasing number of data sources is streaming data. Data collecting, data processing, and data integration are not made any easier by such advancements in data collection.
The appearance of new applications everyday carry with them potentially useful data for businesses. The problem doesn’t stop at dealing with the growing amount of data. More crucially, you must understand which data is useful. And, especially, how to handle it so that it becomes actually valuable.
Reason why real time data integration’s demand increase
In reality, a huge number of applications and external systems used by businesses to collect data has exploded in recent decades. Today, big data as well as data warehouse management has become a key component of any business.
Besides, as you may know, businesses collect data from a variety of solutions and applications. Therefore, to have a single unified perspective of the data, it can be hard. Plus, companies end up with data silos that don’t add much value to their processes unless these data sources are integrated.
What’s more, data management has become and will play a critical part in any organization. Besides, many firms ask a data integrator for help to assist them integrate these systems in this situation.
Not only that, customer information is an important component of this data collection process. And, nowadays, many firms utilize Salesforce as their preferred CRM system. However, CRM isn’t the only place where you may get information about your customers. Instead, most businesses utilize an Enterprise Resource Planning system to collect and process information about their customers’ orders and invoices.
In addition, the several systems will eventually store customer data. As a result, the only way for businesses to genuinely gain a 360-degree perspective of their consumers is to integrate Salesforce with their ERP. There are a variety of data integration systems available to help with this problem.
Following that, many integration platforms take advantage of web services and are cloud-based. Some companies specialize in batch data processing, while others focus on real-time data processing. However, the question of whether real time is genuinely necessary remains unanswered. Furthermore, real time data integration is rarely required by most firms wishing to combine Salesforce with an ERP solution or any CRM solution. Any Microsoft Dynamics ERP solution is the same way.
Real time data integration or Batch Data Integration? Which is better and more appropriate?
What is Batch data integration?
As its name suggests, batch data is the process that gathers a series of data and stores it until a certain amount of data has been gathered. And then processing all of that data as a group. In fact, it’s not the same as processing each piece of data as it comes in.
Moreover, batch processing has long been the preferred method of data integration. Processing data in batches rather than working with each small piece of data separately was often more efficient with previous technologies. As a result, the number of discrete I/O events that must occur is reduced. In addition, by compressing data in batches, it can also assist preserve network bandwidth.
What’s more, batch data integration is best for circumstances where you can wait a little longer for data analytics findings. For example, thanks to the batch-based process, you can keep track of all the documents stored on your company’s network. In particular, it’s probably fine if the index isn’t refreshed every time there is an addition, deletion or modification of documents in this scenario. Plus, by gathering data on document changes and processing it in batches, it would be acceptable to rebuild the index every hour, or even simply once a day. Alternatively, batch data integration is also useful for data that is being preserved. And, it will be accessed on a regular basis for historical purposes rather than being used to make instantaneous choices.
Which one is better?
Firstly, real time data integration produces faster results, so it’s best for you to process data in real time wherever possible. Even if real time integration isn’t strictly required for a specific workload, processing in real time can’t harm. And, it could also come in handy if your needs change in the future and you really require real-time information.
Moreover, the key reason you might decide against doing real-time processing is that it can be more expensive in terms of resource consumption. Modern solutions, on the other hand, make it simple to accomplish real-time data integration without overburdening your infrastructure.
To sum up, you should integrate data in real time whenever possible. Besides, if you’re taking advantage of batch processing, you can look into real-time integration alternatives is a good idea. Because you never know what your future requirements may be.
In fact, real time data integration isn’t always the greatest option for a company. This is because it is usually costly, and it isn’t always faster than the “near” real-time integration. More importantly, real time will never be quicker than the data processing speed of the system. However, we can’t deny the benefits that this integration brings. Above is a comprehensive overview of Real time data integration solutions, in case you have any question, let’s CONTACT US for more details.