With the digital universe expanding exponentially every year, data plays a significant role in the direction it heads. According to
What is a Data Lake?
In simple words, a data lake is a central repository of data to store all categories of corporate data, regardless of its size. Like an actual lake with multiple tributaries coming in, a data lake acts as a large container for data coming from various sources into an organization, internal or external. Data stored in a data lake can be completely unstructured (e.g., text documents, images) or structured (data in rows and columns of relational databases).
A data lake is essential so that you can make use of the stored data in as many ways as possible, such as combining data from multiple sources in your business applications ecosystem to derive powerful insights that can boost your business.
Microsoft’s Move to Data Lakes
Microsoft has been the front runner to adopt data lakes with its Microsoft Azure Data Lake solution, which offers users unlimited data storage capacity. It is a part of the Microsoft Azure public cloud platform and is primarily designed to support big data analytics. This move aims to help streamline and improve data storage efficiency, allowing organizations to process, store, and maintain data better.
New Decisions and Challenges That Emerge With the Rise of Data Lakes
Although a great solution to manage data in a modern
There are various considerations, questions, and concerns that surface when contemplating a move to Microsoft data lakes to realize business insights. Some of the key challenges faced to achieve analytics in the Dynamics landscape with data lakes are as follows:
- Data entities, which are essential in the standard Microsoft ecosystem to enable analytics, require coding knowledge and a development skillset. Hence, the speed to analytics continues to remain low despite the move to data lakes.
- Even with the promising advent of data lakes, consumers still face teething problems when piloting analytics with data lakes.
- While storage costs reduce, the overall cost of ownership increases, as additional tools are needed for data transformation, cleaning, and governance to realize end-to-end analytics. Moreover, tools such as Synapse, Purview, and Azure Data Pipelines need to be a part of your technology stack.
How Can Our Data Modeling Studio (DMS) Help?
So how does To-Increase’s
Whether you are making the move to data lakes or not, Data Modeling Studio will help speed up and optimize your journey towards end-to-end analytics. Embedded inside Dynamics Finance & Supply Chain Management, Data Modeling Studio’s capabilities include data transformation, modeling, quality checks, and extraction, making it an all-in-one tool with zero code. This not only reduces the cost of ownership but also simplifies your application landscape remarkably. In addition to data preparation capabilities of Data Modeling Studio, To-Increase also offers pre-built, plug-and-play analytics solutions specific to departments or industry needs to accelerate your speed to insights via
Data Modeling Studio is robust and reliable for high-volume data preparation and exports. Tables containing more than 170 million records are exported seamlessly, enabling real-time business insights. Furthermore, you can export essential analytics information such as enumerations, metadata, etc., automatically with a click of a button within the product. In the standard Microsoft landscape with data entities, getting such information is a long and tedious process.
To-Increase creates and maintains its software consistently with new features and updates released regularly to help users stay ahead of the curve. For instance, an upcoming new feature, powerful graphical modeling capabilities, can empower business users to design analytics easily. Similarly, as the solution matures, Data Modeling Studio will also be able to support the export of its constructs to data lake to ensure you make the best out of data lakes.