Achieve Scalability with a Modern Data Estate

08 January 2020

In the not too distant past, your organisation could manage with a certain amount of data storage, as well as a set amount of analytical capabilities. There wasn’t a massive influx of information coming into your company waiting to be analysed. Times have changed. 

Today, you need more data storage as well as greater amounts of analytical capabilities to keep up with all of the information you collect and assess. A modern data estate makes that a reality; one of its greatest strengths is its scalability.

What Is a Modern Data Estate?

A modern data estate refers to all of the digital information your organisation has. Today’s firms have quite a bit of data. 

For example, there are line-of-business applications, databases, your CRM, any information coming in from IoT applications and sensors, social media data… and the quantity of that data isn’t going to decrease anytime soon. In fact, it’s only going to grow exponentially. That’s why scalability is crucial for today’s enterprise. 

Why Scalability Matters

To give you a sense of why scalability matters so much, let’s take a look at some statistics. 

Between 2018 and 2025, the amount of digital information on the planet will skyrocket from 33 zettabytes to 175 zettabytes. That’s a compound annual growth rate of 61 per cent. While that number sounds astonishing, it’s actually a good thing. You want to be able to leverage that flow of information. That data represents valuable opportunities for your firm, and the ability to store and analyse it is vital if you are aiming to seize opportunities for growth.

How Can You Achieve a Scalable Modern Data Estate?

A modern data estate is built upon a foundation of operational databases, data warehouses, and data lakes. Each of these components must be optimised so that they can achieve the highest levels of scalability possible. 

What does optimisation look like? That will depend on the component. For example, a data lake must be able to store Big Data analytics workloads, so it must accept unstructured and structured information in a variety of formats and sizes. Moreover, its performance must also be optimised, so it can handle parallel analytics workloads, high throughput, and high IOPS. 

In addition, processes themselves need to have scalability built in. What does that mean in practice? Because the amount of information that needs to be processed can vary widely day by day, your data processing should be able to scale to meet your needs, with a level of granularity and time range that fulfills your SLAs.

Data serving, which refers to the processed information served up by the data warehouse to analytic clients and reporting tools, should also be able to scale upward. That way, it can ensure that all of the information you want analytics and reporting tools to include will be incorporated. 

Data analytics should also take place at scale. You don’t want to wait for valuable insights. If it takes too long for your analytics solutions to evaluate information, you could miss out on opportunities. Scalable analytics should also be available across your entire data estate; otherwise, you might not be aware of a critical situation that needs to be addressed right away.

Microsoft Azure: Your Key to Achieving a Scalable Modern Data Estate 

Microsoft Azure helps you achieve a scalable modern data estate with its cutting-edge solutions. Two crucial components of its scalable modern data estate offering are databases and data analytics in the cloud. 


There are four databases as part of Microsoft Azure’s services: 

  • Azure SQL database 
  • Azure Database for MySQL/PostgreSQL
  • Azure Cosmos DB
  • Azure SQL Data Warehouse

The Azure SQL database is a fully managed relational database that provisions quickly, scales on the fly, and includes built-in intelligence and security. Azure’s database for MySQL/PostgreSQL is also a fully managed, scalable MySQL/PostgreSQL relational database with high availability and out-of-the-box security. The Azure Cosmos DB is a globally distributed multi-model database, with support for NoSQL choices, with industry-leading performance and SLAs. Azure’s SQL Data Warehouse is a fully managed elastic data warehouse with security at every level of scale.

Data Analytics in the Cloud 

There are five business intelligence and analytics features offered by Microsoft Azure:

  • HDInsight
  • Data Lake Store
  • Data Lake Analytics
  • Stream Analytics 
  • Machine Learning 

HDInsight is a fully-managed cloud Hadoop and Spark service backed by 99.99 per cent SLA for your firm. Azure’s Data Lake Store is a no-limits data lake that supports massively parallel analytics. The Data Lake Analytics is a fully managed, on-demand, pay-per-job analytics service featuring enterprise-grade security, auditing, and support.

Stream Analytics is an on-demand, real-time stream processing service with the highest-level security, auditing, and support. Machine Learning is a fully-managed cloud service that allows you to build, deploy, and share predictive analytics solutions.

What Are Some Use Cases for a Scalable Modern Data Estate?

There are a few use cases for a scalable modern data estate: 

  • Process optimisation
  • Greater insights into data
  • Stronger security 

Do you want your operations to be faster and smoother? A scalable modern data estate enables process optimisation. You can now analyse information quickly, meaning that you can act on that data faster – there won’t be any more lags. 

More sophisticated analytics also means that you have greater insights into your data. You can seize opportunities you weren’t aware were even available. Because security is stronger, you won’t lose sleep at night due to threats or lack of compliance with local, industry, or international regulations. 

Enlighten Designs: Helping You Build a Scalable Modern Data Estate

Enlighten Designs has over 20 years of experience helping customers get the most out of their data. We’ve been a proud Microsoft partner since 2004. Since then, we’ve achieved Gold status in the categories of Application Development, Data Platforms, and Data Analytics. To learn more about building a scalable modern data warehouse, contact us today.

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