Future-Proofing Your Big Data Infrastructure

By Data Science Salon

Data engineers are facing a great challenge worldwide, i.e., the unprecedented growth of data.

Since 2017, the amount of data generated worldwide has increased by a staggering 270%. This incredible growth has organizations questioning their data policies and infrastructure. Companies have had to repeatedly revamp their data infrastructures to cater to the needs of this ever-growing data influx. 

To stay competitive in this 4th industrial revolution, organizations need to plan their data strategies that remain robust for years to come. This article will discuss various aspects of building a resilient data infrastructure, including its major components, challenges, and use cases.

What Are the Components of a Big Data Infrastructure?


Each infrastructure component is equally important for efficiently handling data, and if properly planned, it proves valuable in the long run. Some major components are:

  • Scalable Hardware
  • ETL/Reverse ETL Pipelines
  • Database Systems and Sources
  • Data Processing
  • Data Security
  • BI Analytics

Let's talk about these components in detail below:

1. Scalable Hardware

Hardware includes the physical servers and disk drives where the data is stored. With increasing data volume and velocity, storage space depletes rapidly. More data also means more processing power is required; hence, organizations must upgrade their storage spaces and server CPUs on demand to build a robust data infrastructure.

2. ETL/Reverse ETL Pipelines

Data isn’t only growing in size but also variation and format, i.e., structured, unstructured, and semi-structured. Flexible data integration pipelines ensure smooth and error-free data flow from business tools to the data warehouse or data lake ( the ETL process) and back to business tools (Reverse ETL process) for operationalization. 

The data ingestion processes should allow variations in data schemas. The ETL pipeline must be equipped with modern data transformation processes, and the central storage should be resilient to the size or type of incoming data. Moreover, the pipeline must ensure data integrity and data security at every stage. 

3. Database Systems and Sources

As mentioned above, data is complex. It can be: 

  • structured like relational databases with proper tables and keys
  • unstructured like images, audio, video, and text 
  • semi-structured like emails, graphs, code, and XML files

Today, an organization’s data infrastructure should be compatible with a multitude of databases and data sources which can be achieved using data connectors that allow real-time data transfer across the pipeline.

4. Data Processing

Before storage in a warehouse or data lake, raw data must undergo some preprocessing, also known as data transformation. During transformation, data is cleaned and structured to maintain its quality and integrity. 

Modern data pipelines can automate many of the transformation tasks to reduce processing time. For larger data volumes, preprocessing can become costly; hence, on-demand CPU compute becomes an integral component of data infrastructure.

5. Data Security

Data is a valuable asset, but it is usually not secure. According to the 2022 Verizon Data Breach report, ransomware-related breaches increased by 13%, and 82% of data breaches had human involvement. Proper security measures, both physical and digital, are essential to ensure your data is always protected. This includes making data pipelines compliant with GDPR, CCPA, HIPPA, and SOC2 protocols.

6. BI Analytics

A data engineer can easily query data stored within a database, but it is inconvenient if non-data team members need access. Reporting tools such as Apache Superset, MS PowerBI, and Tableau provide users with a rich interface to create and visualize data analytics on a dashboard. BI tools streamline operations within an organization and provide valuable insight from the data to all members.

How to Build a Big Data Infrastructure?—in 4 Steps

Building a data pipeline must be built with carefully calculated steps. These steps are as follows:

Step 1: Planning

Diving into a data project without proper research could lead to a train wreck; hence, it is crucial to brainstorm requirements and formulate a plan. To build a lasting data ecosystem in your organization, you need to consider the type of data you have, its inflow, and how it will scale. Lack of planned execution and due consideration could lead to financial losses.

Step 2: Team Building

Selecting the right engineers and analysts is vital for building a sustainable data ecosystem. You need members who can build this infrastructure from the ground up, keeping the data requirements and future prospects in mind. A proper hierarchy of data managers, engineers, scientists, annotators, analysts, and specialized executives like CDOs/CIOs is required to ensure that data is in safe hands.

Step 3: Hardware and Software Requirements

Hardware setup might be the most crucial step in building your data infrastructure. For hardware, you need to consider all prospects of the data pipeline, including:

    • Type of Data: Your setup will vary depending on whether your data is structured, unstructured, or both.
    • Space Complexity: You need to decide how much disk space is required depending on how much data is currently being generated and how much it will grow in the future. 
    • Processing Power: Preprocessing large volumes of data will require a lot of CPU power. You need to buy servers with better processors (GPUs/TPUs). This will also come in handy if you utilize BI tools to build interactive dashboards on top of your databases.
  • Server Deployment Model: You can have on-premises or cloud-based servers. For on-premise setup, you will require an experienced IT and security team. If you opt for cloud servers, you must choose a cloud provider depending on your budget and the required features (security, scaling, optimization, etc.).
  • Suitable Software Selection: SaaS software and tools are an essential part of data infrastructure. However, there are numerous options available. The managers must evaluate and compare SaaS software according to their business and data requirements. 

Step 4: Future-Proofing

The whole point of planning the infrastructure is to end up with something sustainable for years. Everything discussed above must be carried out by keeping future advancements in mind. For instance:

  • Planning should be done by industry veterans who are aware of the present and future data trends. The plan should be regulated and governed by industry-standard data policies. 
  • For hardware selection, ensure you don’t pick outdated machines, or they will wear out when processing requirements increase. 
  • When building a team, hire candidates who demonstrate good analytical skills and are open to learning new technologies.
  • Finally, document the whole process to make it more transparent and accountable.

Why does Big Data Infrastructure Fail?—The Challenges  

According to Statista, the total data generated between 2010 and 2015 was 50.5 zettabytes, whereas data generated in 2020 alone was 64.2 zettabytes. Planning for such an ever-growing and ever-changing entity is not a simple task. 

Due to the sheer data volume and changing trends, many of the prospects related to data cannot be predicted reliably. There is no safe estimate for the required hardware specifications an organization might need to manage its data. This is the reason many companies start struggling very early on.

Another critical factor in data infrastructure failure is that many organizations are reluctant to switch to newer technologies, and sooner or later, their legacy infrastructure becomes challenging to maintain.

Use Cases of Modern Data Infrastructure

Besides keeping up with the competition, a modern data infrastructure has many perks. Modern technology is created with user experience in mind, and such improvements are financially beneficial. Let’s discuss these perks below.

Modernize Databases

Present-day databases come integrated with a user-friendly interface, optimization tools, and state-of-the-art security features preventing unauthorized access to your data. These capabilities increase the performance of your team and the overall system.

Modernize Legacy Systems

Legacy systems often have compatibility issues with modern software. Upgrading your system allows you to implement new technology and enhance your processes. Faster processors and memory modules mean lesser time spent on data transformation. Newer processors allow parallel and distributed computing capabilities that further increase overall system efficiency.

Modernize Analytics

BI tools are very intelligent and extract valuable insights from your data that can be used to understand your target audience and grow your business. Moreover, modern analytical tools offer a drag-and-drop interface for creating striking and interactive visualizations. Due to this ease of use, non-data team members require very little practice to utilize the advanced features of these tools.

Wrapping up

IT is an evolving field, and adaptability is vital for organizations to survive in this space. Part of this adaptability is constructing a modern and robust big data infrastructure that can withstand changes, allows scalability, and carries out processes seamlessly. 

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