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Creating a Scalable Data Infrastructure

Scaling infrastructure to keep up with the pace of data growth.

Discover and avoid the challenges from scaling a data infrastructure from tens of events a second to millions of events a second. Learn what it takes to ingest data reliably and in a compliant way.

7 Data Trends To Act On Now 

It is estimated that by the year 2020, global revenue generated from big data will clock $203 million thanks to a looming explosion of data. The big question is this, will your business partake in this awesome opportunity? The whole drive behind the growing emphasis on machine learning, flexible infrastructure, and data analysis is the fact that data can now be gathered, stored and analyzed  at lower costs. 

The onus rests on your company to use the data you currently have to your advantage while taking your data to newer heights and learn how you can gain from implementing machine learning. You must also learn about data humanization and how to harness the power of data. 

According to the IDC, by the year 2025, the world will have an annual production of 180 Zettabytes of data with most of it coming from IoT. Which begs the question, what could be driving these growth levels? Let’s take a look at the data trends responsible. 

Machine learning 

To understand the inputs and context of historical data and in turn inform analysis for real-time data, big data is often leveraged and used to gain more understanding. Doing this will give more businesses the power to make decisions before events occur as opposed to waiting for events to occur before scrambling to make decisions. Although Gartner predicts that by 2020, 40% of data tasks will be automated, we can be assured that machine learning will not be taking over the roles of data scientists just yet. 

Specialization of job roles 

Because digital transformation is turning most organizations into tech-based businesses, the importance of data continues to grow and as such organizations have to implement two wide overarching ways of:

  • Implementing  tools that provide specific solutions to discrete stages of analytics delivery 
  •  Employing skilled data experts that are able to execute these stages and use the required tools 
  • Specialization leads to an increase in demand. It is projected that by 2020, job openings for analytics and data professionals will grow from 364,000 to 2,720,000. 

Log analytics 

Leveraging real-time data or streaming depends a lot on the quality of your data pipeline. Organizations that wish to do this must use a holistic and acute monitoring and alerting system that is able to handle data integrity and security all through their data systems. 

Increase the valuation of data assets 

Among the top data initiatives are being able to decrease your company’s expenses through better operational efficiency. Data streaming platforms like Amazon Kinesis and Kafka make it possible for organizations to surface analytics a lot faster so they can connect with real-time data more efficiently. This allows them also speed up their data monetization processes as well. 

Elasticity and fluidity in analytics infrastructure 

When organizations can dynamically evaluate their load on server node while also leveraging their cloud storage to anticipate some performance issues, this gives them a greater advantage as well. Think of how you can make your infrastructure better and do it. 

Built-in data prep and governance in analytics tools 

Analytics and Business Intelligence (BI) tools like Tableau keep introducing enhanced features like a data certification that makes it easier for skilled professionals to achieve business desirables a lot faster. This also means already existing complex Extract, Transform and Load (ETL) data processes get an added layer of abstraction that makes documentation of business users that are non-technical, and metadata more accessible. These business users are then able to tackle some analytical duties. 

Data humanism 

The process of making the nature of data more personal and unique especially through data visualizations is what data humanism is all about. Once an organization is able to nuance data assets and embrace its complexity, it will be able to humanize it and focus on collecting data of higher quality instead of collecting in more quantities. 


All evidence shows that we are currently in a data boom because we are now able to gather, store and analyze data at a much more reduced cost. Companies that position themselves to harness the power of data and take advantage of these growing trends will definitely have a place amongst the creators of the future.

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Creating a Scalable Data Infrastructure

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Data Science In Practice

In this lesson, we’re going to introduce you to 5 common applications of data science that answer the question of what data science is, how companies use it to make operations and products better, and that the data science lifecycle looks like. The main goal of this lesson is to do away with the notion that data science is some sort of obscure black magic while introducing you to real-life examples of how it is applied.

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