With the digital universe expanding exponentially every year, data plays an important role in which direction you’re headed. according to
What is a data lake?
In simple words, a data lake is a central data repository for storing all classes of company data, regardless of their size. Like a physical lake with multiple incoming tributaries, a data lake acts as a large container for data coming from various sources to an organization, internal or external. The data stored in a data lake can be completely unstructured (for example, text documents and images) or structured (data in the rows and columns of relational databases).
A data lake is essential so you can leverage stored data in as many ways as possible, such as integrating data from multiple sources into your business application ecosystem to derive powerful insights that can enhance your business.
Microsoft’s move to data lakes
Microsoft was the first candidate to adopt data lakes with Microsoft Azure Data Lake solution, Which provides users with unlimited data storage. It is part of the Microsoft Azure public cloud platform and is primarily designed to support big data analytics. The move is intended to help simplify and improve data storage efficiency, allowing organizations to better handle, store, and maintain data.
New decisions and challenges emerge with the emergence of data lakes
Although it is a great solution for modern data management
There are many considerations, questions, and concerns that arise when considering moving to Microsoft data lakes to achieve business insights. Here are some of the main challenges encountered in achieving analytics in the Dynamics landscape with data lakes:
- Data entities, which are essential in the standard Microsoft ecosystem to enable analytics, require knowledge of coding and a development skill set. Hence, the speed of analyzes remains low despite moving to data lakes.
- Even with the promising emergence of data lakes, consumers still have problems growing when experimenting with analytics using data lakes.
- As storage costs decrease, total cost of ownership increases, as additional tools for data transformation, cleaning, and governance are required to achieve comprehensive analytics. Furthermore, tools like Synapse, Purview, and Azure Data Pipelines should be a part of your technology stack.
How can Data Modeling Studio (DMS) help?
So how does To-increase
Whether you’re navigating data lakes or not, Data Modeling Studio will help you accelerate and improve your journey toward end-to-end analytics. Data Modeling Studio capabilities integrated within Dynamics Finance & Supply Chain Management include data transformation, modeling, quality checks, and extraction, making it an all-in-one tool with zero code. This not only reduces your cost of ownership but also significantly simplifies your application landscape. In addition to the data onboarding capabilities of Data Modeling Studio, To-Ziya also offers pre-built, up-and-running analytics solutions specific to department or industry needs to accelerate your speed to access insights across
data modeling studio Powerful and reliable for preparing and exporting data in bulk. Tables with more than 170 million records are exported seamlessly, enabling real-time business insights. Moreover, you can export basic analytics information such as census, metadata, etc. automatically with the click of a button within the product. In the standard Microsoft landscape with data entities, getting this information is a long and tedious process.
To-Ziya is constantly creating and maintaining its software with new features and updates released regularly to help users stay ahead of the curve. For example, an upcoming new feature, powerful graphical modeling capabilities, can enable business users to easily design analytics. Likewise, as the solution matures, Data Modeling Studio will also be able to support the export of its structures to the data lake to ensure maximum benefit from the data lakes.
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