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What Is A Data Pipeline And What Does It Do?

If you run a state-of-the-art business or online store, you probably need a data scientist. If you produce a lot of data but do not think you need a data science expert, you are not yet familiar with this area of ​​technology. 

Data science has been in the business dictionary since 2001. Continued by William S. Cleveland introduced it as part of the statistics field. Until Google’s senior economist Hall Varian in 2009 offered a new perspective on the science.

He believed that the process of collecting data and extracting information from it would transform modern business.

What is a Data Pipeline and what does it do?

Today, data scientists are developing machine learning algorithms to solve complex business challenges.

 These algorithms help you perform the following processes:

What is a data transfer bus?

Why do we need a data bus?

Elements of a data transmission bus

To better understand how a data transfer bus prepares a large data set for analysis, you should first look at the key components of a typical data bus.

  1. Source
  1. Destination
  1. Data circulation 
  1. Processing 
  1. Workflow
  1. monitoring

How is a data transfer bus built?

Do you need a data scientist to build a data transfer bus?

There are different views in this regard. Data scientists have a good job market right now, but no one knows what evidence they need. To address this ambiguity, the Open Group (IT Industry Consortium) introduced three levels of certification for the title of Data Scientist in early 2019.

To obtain these certifications, applicants must prove their knowledge in the areas of programming languages, large data infrastructures, machine learning, and artificial intelligence.

Until recently, data scientists needed to build a data bus, but today, with solutions offered by companies like Xplenty, you can create your own data bus without the need for coding knowledge.

Do you have to provide a dedicated data gateway yourself?

Some large companies, such as Netflix, have developed their own dedicated data gateways, but building a dedicated data gateway is time-consuming and requires extensive resources. In addition, such a solution requires constant maintenance, which increases costs. The following are some of the most common challenges faced by organizations in building data transmitters within the organization:

  1. Connections 

A modern company is likely to add new data sources as it progresses. Each time a new data source is added, it must be integrated into the data transfer bus. This integration may cause problems with both the lack of proper API documentation and different protocols. For example, a company instead

REST API Use SOAP API. Also, APIs may change or crash, which means they need to be constantly monitored. As the complexity of data resources increases, you will need to devote more time and resources to maintaining APIs.

  1. Delay time 

The faster the data transfer bus can transfer data to the destination, the better the business intelligence performance. However, extracting real-time data from several different sources is not easy. There is also the problem that some databases, such as Amazon Redshift, are not optimized for real-time processing.

  1. Flexibility 

The data bus must be able to handle changes quickly. These changes can appear in the form of various types of data forms or API ups and downs. For example, changes to an API may cause unexpected situations that the data bus may not be able to handle. You need to be prepared for such scenarios to avoid disrupting the data transfer bus.

  1. Centralization 

Intra-corporate data gateways usually have a group of central IT members, including programmers responsible for building and maintaining these gateways. This raises two major concerns: The cost of hiring a dedicated engineering team can be high. This approach leads to the centralization of data processing, which is not very efficient.

Superconfigured data gateways have significantly reduced costs so that any business can create its own data gateway within minutes and start collecting business insights. Decentralization in data processing can be a great advantage to increase operational efficiency.

A case study of using a new solution to build data transmissions

Xplenty provides an intuitive and user-friendly platform for organizations to create their own data transfer bus in minutes. This data integration platform can meet the need for specialized engineering teams and solve the problem of spending a lot of time building and maintaining these systems.

This system is compatible with most data storage devices and SaaS platforms, and with the help of REST APIs, you can combine almost any data source with a data transfer bus.

 

 

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