What is a Data Pipeline (and How Does it Affect Business Intelligence)?

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Data pipeline? They are not a new concept. For decades, they have been used by many business organizations, consciously or unconsciously. However, usage in earlier years was significantly different from today’s use cases, largely due to constant technological innovation.

With the continued exponential growth of business data every year, and the increased adoption of data science, data pipelines are becoming crucial components for business success (in addition to their non-business uses).

What is a Data Pipeline?

In layman’s language, a data pipeline is a set of data processing units that alter or transform raw data (structured and unstructured) from a variety of sources into a meaningful format for business intelligence uses such as exploratory data analytics, data visualizations, machine learning tasks, fast data serving layers, data warehousing, and real-time data querying, among others.

A good example is Amazon’s AWS Data Pipeline.

As the name suggests, data pipelines act as the “piping” for business intelligence dashboards or data science projects. A wide variety of places can serve as data sources, for example, files, SQL and NoSQL databases, APIs, and others.

However, such data is usually unstructured, meaning that they cannot be utilized immediately. It is then the job of professionals such as data scientists and data engineers to structure this data so that it can be meaningful to businesses.

As soon as the data has been efficiently filtered, merged, and summarized, they can then be stored and surfaced for use. Effectively organized data pipelines form the foundation for an array of data projects, including exploratory data analyses and others mentioned above.

The Rising Popularity of Data Pipeline for Business Intelligence

The need to utilize data pipelines in business intelligence has increased exponentially in recent years. Here are the main reasons for this growing need:

  • Businesses are facing increased numbers of data sources, and business owners need to generate meaningful insights from these data by connecting all their data sources.
  • Like the data sources, the volume of data being produced is also getting bigger than was the case a few years ago. Large volumes of data are becoming a lot more challenging for businesses to process and analyze with traditional methods and software.
  • Increased analytical savvy on the part of business owners which is leading to the development of highly advanced or sophisticated business intelligence tools and dashboards.
  • In these days of intense online competition, it has become very essential for any serious online business to regularly improve its sales funnels in order to survive and meet its goals.
  • With very large volumes of data being produced these days, delivering data to end users at the appropriate speed levels has become a tough challenge.

Data Pipeline: Different Types

Data pipelines are broadly classified into two types:

  • Batch processing
  • Streaming data

Batch Processing

In batch processing, data is loaded into a repository in “batches” at pre-determined time intervals, usually during off-peak business hours. Off-peak scheduling helps ensure that workloads are not affected since batch processing tasks typically use large volumes of data capable of stretching system resources.

Batch processing tasks are made up of a workflow of sequenced commands. Under this arrangement, the output of one command becomes the input of the subsequent command. For instance, one command may kick off data ingestion, while a subsequent command may initiate filtration of particular columns, and the next command may deal with aggregation.

The series of commands continues until a complete transformation of the data is achieved, after which such data is then written into the data repository.

Batch processing is usually best when there is no urgent or continuous need to analyze a specific dataset, for instance, in monthly accounting. It is more associated with the “extract, transform, and load” (ETL) data integration process.

Note that it is not mandatory that all data pipelines follow this ETL sequence. Notably, the more recent ELT (“extract, load, and transform”) pipeline process has become more popular with the emergence of cloud-native tools. ELT is more suitable than ETL when a business is faced with large, unstructured data sets and when time is of the essence.

Streaming Data

In contrast to batch processing, streaming data is best when there is a need for data to be continuously analyzed and updated. In other words, rather than loading data in batches, streaming pipelines transport data continuously in real time from the source to the target.

Because data events here are processed shortly after they occur, streaming processing systems exhibit lower latency than batch systems but are not regarded as reliable as the latter since messages can be dropped unintentionally or can stay in the queue for long.

One way message brokers help to address this concern is by a consumer acknowledging or confirming the processing of the message to the broker so that it can be removed from the queue.

However, streaming pipelines have some benefits as well. For example, they allow users to analyze or report in real time on all kinds of datasets without the need to spend time extracting, transforming, and loading more data. Streaming pipelines also attract lower costs and require lower maintenance than batch-based pipelines.

Costs of storing and processing data are much reduced when they are cloud-based and under an ELT process. Not only is the ELT process largely low maintenance, but the transformation process is usually automated and cloud-based.

How A Data Pipeline Affects Business Intelligence

The advent of cloud computing means that various sections of a typical modern business (e.g., HR, production, accounting, marketing, sales, etc.) now use a suite of diverse software applications for different purposes.

This usually results in data being fragmented across different business tools, resulting in data silos. One challenge with data silos is that they can make life difficult when it comes to leveraging even the simplest of business insights.

Even when data is successfully gathered from a multiplicity of sources and entered into a Google or an Excel sheet for analysis and interpretation, there are still other problems, such as data redundancy.

In addition, the time and energy needed to manually gather these data is inversely proportional to the complexity of the business’s IT infrastructure. Adding other variables into the mix (such as data from real-time sources, e.g., streaming data) makes the whole task even more herculean.

By aggregating data from multiple sources into one dedicated medium, data pipelines ensure that data is quickly analyzed, interpreted, and utilized (or otherwise) by businesses. This is, in addition to providing consistent data quality, an absolute necessity for reliable business insights.

Some Business Intelligence Use Cases of Data Pipelines

Here are some of the most common use cases of data pipelines for business intelligence:

Exploratory Data Analysis (EDA)

Data scientists use exploratory data analysis to analyze and investigate data sets as well as summarize their main characteristics, often with the assistance of data visualization methods.

EDA helps users determine an optimal way of manipulating data sources in order to obtain the right results. In summary, with EDA, data scientists can easily unravel patterns, detect inconsistencies, scrutinize assumptions, or test hypotheses, among others.

Advanced Search Systems

Engines like Elasticsearch are good places to store the results of processed data. This will make such data readily available and searchable for a company’s other applications. Businesses that intend to access large volumes of complex data in milliseconds can also deploy Elasticsearch as a speed layer.

Data Analytic Tools and Data Visualizations Dashboards

Businesses can use a tool like batch processing to carry out complex business intelligence queries on a combination of data in order to generate analytical dashboards.

Data visualizations depict data through widely used graphics techniques such as infographics, plots, charts, and even animations. These visual depictions of information communicate complex data patterns and relationships and provide easy-to-understand data-driven insights.

Real-Time Queries

Data pipelines enable business intelligence teams to perform real-time queries on data for very quick decision-making. However, this task can be fairly complicated for businesses that deal with big data and may require them to use software designed to process large volumes of data.

Data Warehouses

Processed data output can be stored in data warehouses from where a business intelligence team can perform queries on it. A data warehouse such as Amazon Redshift can be a good data pipeline store.

Machine Learning

The result of the processed data can be linked to a machine learning resource such as Spark MLlib, a scalable machine learning library that boasts common learning algorithms and utilities, including classification, regression, clustering, and collaborative filtering.

How Can Yurbi Help?

In conclusion, understanding the significance of a robust data pipeline and its impact on Business Intelligence is essential. Within the realm of BI technology, Yurbi stands out as a valuable component that securely connects to data from various sources, enabling seamless sharing with internal and external users, all at an affordable cost.

That’s why you need Yurbi to do all the work for you. How?

By leveraging Yurbi‘s capabilities, businesses can generate real-time dashboards and reports, ensuring that decision-makers have access to up-to-date and factual information. This transforms business data into a valuable resource that empowers teams to make informed choices and drive growth.

One of the key advantages of Yurbi is its competitive yet affordable pricing, specifically tailored for small and medium-sized businesses. This allows organizations to unlock their data potential with lower initial costs and ongoing expenses compared to larger competitors such as Power BI or Tableau.

To experience the benefits of Yurbi firsthand, we invite you to schedule a meeting with our team or try our free live demo session. Discover how Yurbi can accelerate your data analysis, enhance business intelligence, and propel your organization toward success. Don’t miss out on this opportunity to revolutionize your data pipeline and unlock the true potential of your business.

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