Azure data lakes is a secured cloud platform that is highly scalable, cost-effective, providing data storage and analytical service. It allows developers, businesses and organizations, scientists, and other Microsoft users to store and analyze data on a massive scale while gaining insight into the complexity of the data sets. With data storage and analysis gaining more attention in today’s digitalized world, Azure data lakes have proven to be an important tool to managing data with ease. Here, Hashan Kodituwakku a successful IT professional with decades of experience in IT services explore more on what data lakes entails. Keep reading!
Azure data lake service is divided into parts and this include:
Data lake storage
As said earlier, data lake is a storage solution for structured or unstructured data. This means it can be a safe haven for data to be kept and be readily available for future use. All data that an enterprise generates no matter the account size, file size, amount of data and sources can be stored. With this cloud storage, anything is welcomed from structured data like databases, to semi structured in the form of JSON, XML to raw unstructured data such as emails, pdfs, pictorial presentations. As well as data from IoT devices, social channels including texts and images.
Importance of data lake storage
Hashan Kodituwakku says as organisations become data driven to effectively perform business operations, the need for a sound data lake becomes increasingly important. With this powerful platform, organisations can unlock insights, facilitate exploration and drive innovation across multiple sources. Data analyst, and scientistS are the professionals actively involved in the platform as they require a tool to get their job done perfectly. Data lake:
● Provide high performance analytics workload while eliminating data silos with the use of a single data platform.
● Provide cost effective solutions from unlimited sources than data warehouse premium storage.
● It is secure, and highly scalable. There is no limit to the size of data that can be stored.
● It can seamlessly integrate with other IT infrastructure.
● Provides high scale capacity and throughput.
Data lake analytics
Data lake analytics allows you to simplify big data that has been stored in order to easily develop and run some of the largest scale processing. You can choose between on-demand service or a pay-per-model in which users only pay for what they use be it storage, analysis or compute. This is a cost-effective solution as you only pay whatever you use or consume and you don’t have to spend on what you don’t need. Hence, you uncover value from all your structured and unstructured data in order to gain insights required to facilitate business operations. This is carried out with no need for no hardware, licenses or service-specific support.
For more advanced analytics and processing of big data in the lake, organisations can also make use of a set of tools and technologies to broaden their level of expertise. This includes Azure data factory, Azure Synapse Analytics, Power BI and Azure data warehouse.
‘With Azure HDInsight, Spark and Azure Synapse Analytics, you can optimise data automatically, build a report and run real-time analytics to your data to maximise performance and provide better, actionable insights,’ Hashan Kodituwakku explains. Not to ignore the fact that the data lake is in Azure. This allows you to integrate all data generated from social networks and IoT devices without hassle.
Another important benefit of data lake is that it is developer friendly. Finding the right tools to fine-tune your big data can be overwhelming. But data lake is making life easy with easy integration with other powerful solutions such as Azure DevOps, Eclipse, and Visual Studio to make it easy to develop, debug and optimise big data.
Conclusion
As a result of the cost effectiveness and scalability, Azure data lakes has been gaining ground and is being effectively used to solve the challenges of big data. And with more advanced data analysis tools, you can take your data analysis to another level in order to make informed decisions.
