Azure Data Lake Storage

Stored Terabytes of Healthcare data in a cost-optimized solution on-cloud using Azure Data Lake Storage Gen2 in containerized fashion. We utilized Azure Data Lake Storage containers as a Destination in our Data Engineering Streasmets Pipelines. Loaded Data became available further to multiple downstream applications in an automated and faster way using Azure Data Factory. Also turned out a better, cost-optimized, and faster solution than HDFS for our different business use cases like the migration of huge data from RDBMS to Data Lake.

We had all of our storage located within a single datacenter, which caused an issue should it go down. Azure Data Lake Storage allowed us to move some of the storage there, keeping that piece online and active if we lost communication to the main datacenter. It's nice, but not the most reliable.

Overall it is easy to learn and would be useful for any home care service. Another thing that I like about it is that there is a phone call system where they help you with all the questions you may have. Audio and video calls are possible, with PC screen sharing its other systems Allows saving documents for some members or sharing them in general channels

In our environment, we use Azure Data Lake Storage as a basis for data analytics workflows. Since we want to move away from single storage solutions we chose Azure Data lake as it integrates seamlessly with our current Azure environment. We are able to process large amounts of data and visualize and present it accordingly while ensuring availability and security.

We need to store large amount of data that flow daily from our processes as well from external APIs, and we need to keep them for long period of times to perform historical analysis for our clients. Azure Data Lake Storage helps us achieve this goal by providing a secure, fast and large data store for our needs.

As part of Analytics and BI COE within my company Azure Data Lake Storage has been a key ETL and data integration platforms to perform proper processing of huge amount of structured and semi structured data. Azure Data Lake is used as a test ground for creating use cases around integration of Azure Storage, MS SQL Server, Azure Synapse Analytics and for dynamic data using Azure Streaming Analytics

We use the Azure Data Lake Storage unlimited capacity for storing real big IoT data. The major problem with the real-time data is the scalability and when the business demands more resources the database e.g. increasing number of connected sensors, the Azure Data Lake Storage is highly scalable and helps us to manage such big data.

Azure Data Lake Storage is a huge storage repository service that can be used to store information in the public cloud by Microsoft.
Our business scope is to work on a large data analytic project where we have to extract a large amount of structured and unstructured data for the data analysis and transformation.
Since we are also hosted our business application on Azure Cloud, the Azure Data Lake Storage is very helpful to use as it can be integrate with other Azure services and we do our analysis on the real-time at one place. Azure Data Lake Storage is built on the Hadoop file system which means it can process massive pentabytes of data in an efficient way.
It helps in streamline the overall efficiency of our requirement and business outcomes.
Except for some query performance improvements, we have faced no issues so far.

Azure Data Lake is being utilized in a number of ways for our company, most of all tracking employee meal plans, and other analytical sales data. This is the best solution for our use case, and has worked extremely well. We love that it also integrates with Power BI, which our sales team and marketing folks use heavily.

Azure data lake storage performs very fast and it's cost effective. We can store the data in multi structure format and unstructured format. Azure data lake storage is easy to use and transform data using pipelines. The another good thing is It's support One Drive for Enterprise, SharePoint Online and Exchange online.

If you need big data analytics for your structured, semi-structured, or unstructured data you can use Azure Data Lake Storage. It provides us with unlimited storage. So we can use it to store any type of data of any size.

We use Azure Data Lake Storage as a repository for our larger data analytics. Azure Data Lake has enabled us to capture data of various sizes (from small to large) and types, relatively fast for us to do our analytics. It integrated without issue into our existing data warehouses. Was able to set it up with minimal help.

Microsoft Azure Data Lake Storage is the best among all available Hadoop data Software in the market. It has the best end-to-end security for our data. It is used by our company to analyze our large data. It is highly cost-efficient and also highly scalable with gives us a visualization of our dataset and makes it easy to understand the data properly.

We use Azure Data Lake Storage for our big data analytics tasks across a variety of clients and data sets that are larger than the average sets.

We used Azure Data Lake Storage to provide our enterprise with a medium to ingest and build our data warehouse. We went with the Azure storage because of our existing Microsoft footprint as well as the cost of the solution. After our initial discovery, we found that the tool was very easy to implement and we've been on it for 2 generations.

The product is pretty good. Asked in online communities to get more information from vendors on demonstrating capabilities, possible use cases, and how others are using it but not had good results. There are many components to their AZURE analytics solution, just takes time to understand how all the pieces work together.