What is Data Lake
Everybody understands the concept of databases, but what is a data lake? To put it simply, a data lake is an innovative solution for storing and managing huge collections of data.
What is Data Lake – How Does it Work?
The main feature of a data lake is its high scalability. It can store petabytes of data, in different forms—structured, unstructured, or semi-structured. The data stored in a data lake comes from every conceivable source, including Internet of Things devices and social media platforms.
Data lakes, which can be used alongside data warehouses, provide the basic framework for machine learning. At the same time, they allow you to implement and use real-time analytics.
What is Data Lake – How Can It Be Used?
You can use cloud data lakes or you can build one on-premises, provided that you have the computing power and storage capacity necessary for such a huge undertaking. There’s also a hybrid solution, which is a mix of cloud and on-premises data lake.
The second challenge is how you manage all the data stored. You will need data governance solutions to ensure data integration and security.
Accessing your data requires the use of an enterprise-grade SQL engine that allows parallel processing and advanced data queries. This is mainly done by using AI and machine learning programs, capable of analyzing and sorting the data stored in your lake.
What is Data Lake – Practical Uses
Data lakes are the optimal data management solution for all the industries that need to store and analyze huge amounts of information.
Communication Services Providers
Communication providers are one of the biggest clients for data lake technology. Having access to a data lake allows them to continuously monitor their network and improve the quality of their services. This is essential as the world is transitioning to 5G networks.
That, coupled with the proliferation of Internet of Things (IoT) devices, gives communication services providers an opportunity to come up with new services, enabling the creation of smart cities, self-driving cars, and interconnected factories.
Managing huge amounts of information from various sources allows financial services providers to better target their customers by taking into account a multitude of factors.
Also, by using data lakes and data management programs, financial services providers can improve their claims management and mitigate fraud risks.
The need for novel data management solutions was obvious even before the COVID-19 pandemic. At present, the challenges healthcare systems all over the world are confronted with are even greater.
Healthcare operators need to store and process accurately vast amounts of data in record times. They also need to ensure data security and make sure the systems they use comply with industry standards.
Moving forward to data lakes allows healthcare providers to improve patient care and experience. Modern data management solutions are essential for remote diagnosis, patient monitoring, and telesurgery.
Using Data Lakes in an AI-focused Information Architecture
When you look at data lakes, you need to understand how they can help your business now and the role they will play in the future.
AI and ML rely on building data analysis models. In order to do that, they need access to data combined from a variety of sources, and only a data lake has the capacity for that.
The second trend that supports the case for data lakes is the ever-increasing amount of information produced every single day. You have, for instance, more and more devices connected to the IoT. These alone produce tons of data that needs to be relayed back to the manufacturer.
And let’s not even talk about social media platforms. Every minute, over 500 hours of new content is uploaded on YouTube.
All this information needs to be stored and managed if it’s to be of any use, and data lakes are the best option at the moment.
What is Data Lake – Additional Resources
- Amazon AWS – AWS offers a data lake solution that automatically configures the core AWS services necessary to easily tag, search, share, transform, analyze, and govern specific subsets of data across a company or with other external users.
- Google Cloud – Google Cloud’s data lake powers any analysis on any type of data. This empowers your teams to securely and cost-effectively ingest, store, and analyze large volumes of diverse, full-fidelity data.
- IBM – Simplify with a cloud data lake deployment or use IBM compute and storage to build out an on-premises data lake.
- Microsoft Azure – Azure Data Lake includes all of the capabilities required to make it easy for developers, data scientists and analysts to store data of any size and shape and at any speed, and do all types of processing and analytics across platforms and languages.