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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:
  •  They make fraudulent predictions more accurate.
  •  Identify the motivations and desires of consumers and buyers to a precise level. This helps to raise brand awareness, reduce financial burdens and increase marginal revenue.
  •  Predict future customer demand and help business executives spend liquidity in the right places.
  •  They help marketers personalize each customer experience based on their tastes and needs.
  • To achieve these results, data pipelines are critical pieces of the puzzle.

What is a data transfer bus?

  • The Data Pipeline is a set of steps that transmit raw data from one source to another. In the sense of business intelligence, a resource can be an exchange database, while the destination is usually a data lake or a data warehouse. The destination is where the data is analyzed to reach the business perspective. In the source-to-destination path, the data is refined to prepare it for analysis.

Why do we need a data bus?

  • Using the cloud means that a modern organization uses a set of applications to manage various tasks. The marketing team may use a combination of HubSpot and Marketo to automate marketing, the sales team may rely on Salesforce to manage the strategic plan, while the product team may use MongoDB to store customer feedback.
  •  Since each team uses its own solutions, there is a problem of data fragmentation between different tools and errors in the results stored in data silos (repositories).
  • Data warehouses can make even a simple fetch from a business perspective like the most profitable market difficult. If you try to manually fetch data from all different sources and integrate them into an Excel spreadsheet, you may encounter errors such as data redundancy. In addition, the effort required to do this manually depends on the complexity of the IT infrastructure.
  • Also, transferring data from instantaneous sources such as data streams complicates the issue. Data gateways combine data from all different sources into a common destination, allowing for rapid data analysis to achieve business insights.

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
  • There are places from which a data bus extracts data. They can include relational database management systems (RDBMS), CRMs, ERPs, social media management tools, and even IoT sensors.
  1. Destination
  • The endpoint is the data transfer bus. Where all extracted data is discharged. Often the destination for a data bus is a data lake or data warehouse. Where data is stored for analysis, but this is not always the case. For example, data can be sent to data visualization tools for analysis.
  1. Data circulation 
  • The data changes as it moves from source to destination. This data transfer is called data flow. One of the most common methods of data circulation is ETL or extraction, conversion, and loading.
  1. Processing 
  • These steps include extracting data from sources, converting, and transferring it to a destination. In the processing stage, it is decided how the data circulation should be done. For example, what extraction process should be used to capture the data? Two common methods of extracting data from sources include batch processing and ongoing processing.
  1. Workflow
  • Workflow is about sequencing tasks in a data bus and their interdependence. It is these dependencies and sequencing that decide when to run a data bus. In a data transfer process, the upload process must first be completed before the download can begin.
  1. monitoring
  • A data transfer bus needs constant monitoring to check for accuracy and data loss. Also, the speed and efficiency of the bus should be monitored, especially when the volume of data increases.

How is a data transfer bus built?

  • To build a data transfer bus, an organization must decide how to extract data from resources and transfer it to its destination. Batch processing and streaming are two common ways to do this. The conversion process (ELT or ETL) must be decided after the data has been transferred to the intended destination. This is just the beginning of building a data transfer bus. There are several other things to consider when building a low-latency, reliable, and flexible data transfer bus.

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.