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A Comprehensive Guide to Implementing an eCommerce Data Pipeline

If you want to start an eCommerce business, you need to think about various aspects such as your products, marketing, and branding.

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A Comprehensive Guide to Implementing an eCommerce Data Pipeline

Running an online business in the eCommerce industry means that you are running a business that creates a lot of data along the way. This data helps you stay competitive and make decisions based on facts instead of guessing. And we all know that guessing is never good in the world of business.

If you want to start an eCommerce business, you need to think about various aspects such as your products, marketing, and branding. But for all of that, you need to rely on data that you will collect as a vendor every day.

This data deals with website traffic, sales records, product details, inventory details, marketing efforts, advertising numbers, customer insights, and so on. Almost all operations related to your business generate various amounts of data.

But what should you do when you get overwhelmed with the amount of data that is being generated?

Well, you need to transform all data coming from your data sources into actionable insights that could mean a lot to your business. And you can do that with a data pipeline. Take a look below and learn more about eCommerce data pipelines and how they can benefit your business.

1. What is a data pipeline?

A data pipeline is essentially a set of tools and activities used to move data from one process with its method of data storage and processing to another, where it can be stored and handled differently.

In the eCommerce realm, a data pipeline should be seen as an automated process of various actions used to extract and handle data from different sources into a format used for further analysis.

Take a look at various places where your business data can be gathered:

A data pipeline allows a user to extract and move data from all disparate apps and platforms into one central place. It transforms it into a usable format for reporting across sources.

Therefore, all businesses that rely on multichannel insights need to recognize how a data pipeline can help them improve their processes. Remember, before one can extract valuable insights from the gathered data, they first need to have a way to collect and organize it.

2. ETL pipeline vs data pipeline

ETL pipeline vs data pipeline

A lot of businesses that rely on this kind of technology also talk about ETL pipelines. Moreover, many have ditched the traditional pipelines and got ETL ones.

So, what is an ETL pipeline? And how is it separate from a traditional data pipeline?

An ETL data pipeline can be described as a set of processes that involve the removal of data from a source, its change, and then loading into the target ETL database or data warehouse for data analysis or any other need.

The target destination can be a data warehouse, data mart, or database. It is essential to note that having a pipeline like this requires users to know how to use ETL software tools. Some benefits of these tools include facilitating the performance, providing operational resilience, and providing a visual flow.

ETL stands for extraction, transformation, and loading. You can tell by its name that the ETL process is used in data integration, data warehousing, and data transformation (from disparate sources).

The primary purpose behind an ETL pipeline is to collect the correct data, prepare it for reporting, and save it for quick and easy access and analysis. Along with the right ETL software tools, such a pipeline helps businesses free up their time and focus on more critical business tasks.

On the other hand, a traditional data pipeline refers to the sets of steps involved in moving data from the source structure to the target system. This kind of technology consists of copying data, moving it from an onsite location into the cloud, and ordering it or combining it with various other data sources.

A data pipeline is a broader term that includes ETL pipeline as a subset and has a set of proceeding tools that transfer data from one process to another. Depending on the tools, the data may or may not be transformed.

3. Are there any other kinds of data pipelines?

Keep in mind that there are quite a few data pipelines that you could make use of. Let’s go through the most prominent ones that have already worked for many other businesses.

  • Open-source vs proprietary. If you want a cheap solution that is already available to the general public, seeking open-source tools is the right way to go. However, you should ensure that you have the right experts at the office and the needed resources to expand and modify the functionalities of these tools according to your business needs.
  • On-premise vs cloud-native. Businesses still use on-premise solutions that require warehousing space. On the contrary, a cloud-native solution is a pipeline using cloud-based tools, which are cheaper and involve fewer resources.
  • Batch vs real-time. Most companies usually go for a batch processing data pipeline to integrate data at specific time intervals (weekly or daily, for instance).

This is different from a real-time pipeline solution where a user can process data in real-time. This kind of pipeline is suitable for businesses that quickly process real-time financial or economic data, location data, or communication data.

4. How to determine what data pipeline solution you need?

How to determine what data pipeline solution you need

You might want to consider a few different factors if you want to determine your business needs’ exact type of data pipeline. Think through the whole business intelligence and analytics process at your company and ask yourself these questions:

  • How often do we need data to be updated and refreshed?
  • What kind of internal resources do we have to maintain a data pipeline?
  • What is the end goal for our data?
  • What types of data do we have access to?
  • How should it be extracted, arranged, and maintained?

Keep in mind that you really can build a data pipeline all on your own. But connecting your various data sources and building a sustainable and scalable workflow from zero can be quite a feat.

If you want to do this and consider this option, think about what the process would look like and what it would take. A data pipeline consists of many individual components, so you will have quite a bit of thinking to do:

  • What insights are you interested in?
  • What data sources do you currently have access to/are using?
  • Are you ready to gather additional solutions to help you with data storage reporting?

If all of this seems overwhelming, do not worry. Most businesses out there don’t have the expertise (or resources) to devise a data pipeline independently. However, if you connect the right expert to your data storage, you might achieve this goal. It will not happen quickly, but it will be worth it.

5. Final thoughts

Take another look at the most critical parts of this guide and evaluate your business needs. Only then can you be able to come up with the right solution for your business.

Again, if you can’t make up your mind and struggle between ETL and traditional data pipelines, know that ETL data pipelines are used for extraction, transformation, and loading, while data pipeline tools may or may not include change!

Keep this in mind since it can make a difference even though it is just one functionality.

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Artificial Intelligence (AI)

Why AIOps Needs Big Data and its Importance in Business

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT

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The Role of AI in digital marketing Artificial Intelligence (AI) Marketing, Business

Introduction

The current IT environment has evolved to a point where old, manual methods were not sufficient to keep up with today’s needs. Increasing complexity, the need for quick solutions, and the massive size of data in IT operations require AIOps to function smoothly.

1. What is AIOps?

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT operations and produce results in real-time. It is an essential tool for monitoring and managing IT Operations.

If issues in digital services are not quickly detected and resolved, business operations will be negatively affected. Customers will not have a satisfying experience. To avoid this situation, AIOps must be implemented.

AIOps does an algorithmic analysis of all the data and helps the IT Operations and DevOps (Development Operations) teams to identify and resolve issues with high speed. AIOps prevents outages, reduces downtime, and provides seamless services. AIOps can give better insights as all the information is centrally stored in one place.

2. Aspects of IT Operations monitoring using AIOps:

a. Data Selection

The modern IT environment generates massive amounts of heterogeneous data. For example, event records, metrics, logs, and other types of data from different sources like applications, networks, storage, cloud instances, etc. This data is always high in volume, and the majority of it is redundant. AIOps use entropy algorithms to remove noise and duplication.

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b. Pattern discovery

Selecting meaningful data and grouping them by correlation and identification of the relationship between them by using various criteria. These groups of data can be further analyzed to discover a particular pattern.

c. Inference

Recurring problems are analyzed, and root causes are found. Identifying such issues makes resolving them more comfortable and quicker.

d. Collaboration

AIOps tools help in reporting to required operators for collaboration without any mishaps, even when these operators are in different departments or different geographical locations.

e. Automation

Automation is the heart of the AIOps. When the business’s infrastructure continues to grow and multiply, AIOps helps automate all the business processes. It helps in storing data centrally, auto-discovering, and mapping the infrastructure, updating databases (CMDB), automating redundant tasks and processes. Thus, leading to agile and efficient IT and business operations.

3. What is Big Data?

Big Data is a high volume of structured and unstructured data that is generated by businesses at high speed at varying veracity.

It systematically extracts meaningful insights from this data to make better decisions and strategic business moves. With the advent of digital storage in the year 2000, data creation increased as digital storage was cheaper than analogue storage. DVDs made data sharing easier.

As institutions like universities, hospitals, businesses started using technology, the amount of data created went through the roof. This resulted in two problems.

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a. The rigidity of relational data structures

This was solved by using Data lakes. The data lake is a centralized repository which allows data storage of all the structured and unstructured data (usually files or object blobs) at any scale and makes it available for analysis.

b. Processing queries in the relational database has scaling issues.

When queries were processed in a single queue, it was time-consuming. The use of Massive Parallel Processing (MPP) resolved this technical issue.

Hadoop 1.0 is an open-source software framework. It was implemented using data lakes and MPP. Apache Hadoop facilitated the use of big data in all organizations. Hospitals, Scientists, and businesses used big data to analyze large sets of data and derive valuable insights quickly.

Hadoop 1.0 had few drawbacks. Optimization of data was complicated. The organization had to employ data scientists to get the required insights.

The introduction of Hadoop 2.0 resolved those issues and further commoditized big data. Hadoop 2.0 also enabled the use of AIOps.

4. The necessity of big data for AIOps

AIOps can function only with big data as older datasets are small and inefficient.

Hadoop 2.0 had a YARN feature that supported data streaming. It also enabled interactive query support. It allowed the integration of third-party applications.

This means that analytics could be improved, but only if it was re-architectured. Organizations without data science resources still had difficulty in optimizing and using Hadoop for better data analytics.

The requirement for more purpose-built and easy use solutions brought companies like Logstash, Elastic, and Kibana to the market. They replaced Hadoop in a few use cases.

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5. What does this mean for your business?

This is important for both Core IT Operations and Service Management because they both rely on an interactive query and streaming data technology.

The Digital Transformation of organizations elevated the need for IT solutions. IT had to deal with increasing complexity, massive data size, and speed.

Transition by upgrading or re-architecture method to support Big Data was also tricky due to purpose-built applications, and the data remained in silos.

AIOps makes Artificial Intelligence take over manual analysis. Data from all the silos form the dataset. Interactive solutions are designed from both technical and usability perspectives.

Conclusion

IT operations need to work on diverse data, analyze real-time streaming data, identify and automate workflows, derive meaningful insights, and support historical analysis. All this requires businesses to build a Big Data backend on the AIOps platform.

AIOps initiative must not be built with a traditional, relational database. AIOps improves the functionality of IT operations. Hence, we can say that AIOps needs Big Data to function efficiently. Also, businesses and corporations who need to store large amounts of data will need AIOps to function correctly, automate tasks, obtain insights, and work efficiently as per the trending demands from end-users.

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