Quality data analytics in
Learn why we consider Azure as one of the most important solutions for building modern data platforms.
Azure Data Lake
Making your data more reliable data and more available
With the exponential increase in data, many organisations are faced with the ever-increasing challenge of storing, processing and making sense of their data. Cloud-based data lakes help address these problems by providing organisations with the capability to capture any type of data, whether structured or unstructured and make this data available for use for a range of applications. This includes your traditional reporting from structured data warehouses to big data analytics.
Not all data lakes however are created equal. When deciding which data lake platform to use, you need to consider your end-user requirements as well as your IT capabilities.
At Data Agility, we use Microsoft’s Azure Data Lake to help ensure our clients get access to the data they need when they need it. With its unlimited scalability and support for a range of distributed computing technologies, Azure Data Lake supports disparate data sets of any size being brought together and analysed to create meaningful insights.
Definition of a data lake
A data lake is a centralised repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics — from dashboards and visualisations to big data processing, real-time analytics, and machine learning to guide better decisions.
Azure Data Lake provides data scientists and analysts with new capabilities
Azure’s data lake storage is purposely built for big data analytics. Moving away from traditional data architectures, Azure Data Lakes enable data to be stored in a fast and secure fashion while enabling you to take intelligent actions.
Let’s look at the top 5 reasons why we use Azure Data Lake when building a modern data platform.
Any size, shape and speed
Azure Data Lake eliminates data silos and allows you to capture data of any size, type and speed – all in one single storage platform. Built to the open Hadoop Distributed File System (HDFS) standard, Azure Data Lake enables you to run massively parallel analytics workloads at consistent high performance. It supports you to extract maximum value from your unstructured, semi-structured or structured data.
Azure Data Lake also gives you the ability to run analytics in the language of your choice such as U-SQL, R, Python, or .NET. Whether it’s business intelligence, image processing, machine learning or predictive analytics, Azure Data Lake is able to handle massive amounts of data for diverse workloads.
Works with existing IT investments
Another feature that we like about Azure Data Lake is its ability to seamlessly integrate with our clients’ existing IT investments. Azure Data Lake provides REST based APIs supporting any number of existing technologies. It also provides seamless integration with other Azure technologies such as Data Factory, Functions, SQL Database, Azure Synapse Analytics and Power BI making data capture, preparation, processing and analytics simple and efficient.
No limits to the size of data
Working with various organisations over the years, we’ve seen how data is being produced on an ever increasing scale. Due to limitations of on-premise infrastructure this is restricting the way businesses can operate.
Azure Data Lake allows you to overcome these limitations by providing storage that is infinitely scalable and is able to store virtually any number of files at any size. Having a scalable storage solution at your disposal ensures that it is able to grow as you do and meet the capacity requirements of your organisation well into the future.
Improved performance and reduced costs
Modern data platforms are not automated “set and forget” solutions. They require continuous optimisations to constantly achieve the best possible outcomes from your data. Azure Data Lake makes optimisation more efficient through its deep integration with Visual Studio, Eclipse and IntelliJ.
This means as data engineers, you can use familiar tools to run, debug and tune your code to improve performance and reduce costs. Azure Data Lake also supports visualisation of your U-SQL, Apache Spark, Apache Hive and Apache Storm jobs so you can better identify performance bottlenecks.
Unlike on-premise data lakes, Azure Data Lake allows you to instantly and independently scale your storage and compute according to your business needs. This ‘pay for what you use’ model ensures that maximum value can be extracted from your IT budget, allowing you to refocus your IT workforce away from managing infrastructure towards analysing your data and providing value to the business.
In 2019, we worked with Environmental Protection Authority (EPA) Victoria to help them better collect and manage high volumes of data coming from a variety of sources. Based on Azure, we built a data analytics platform that now enables them to swiftly and accurately report significant environmental information.
The key results were:
- Improved time for data availability. Real-time data processing is validated and aggregated for use within 3 minutes of it being received.
- Fully redundant system, ensuring information is always available for the community.
- Direct access to air quality data through modern tools providing easier access to data across the Applied Sciences division.
- A data platform that is more flexible, scalable, easier to support and maintain and is cost-efficient.
“Data Agility enabled us to deliver a modern data platform that’s highly reliable but also significantly more scalable. Using the latest cloud technology, we can bring in data from multiple sources and apply the latest machine learning algorithms to it.“
Chris Moon, Chief Information Officer, Information Technology, EPA
Build your modern data platform with Data Agility
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Get in touch with us today and learn how you can make the most out of the Azure platform and improve your data analytics.