What is data sharing and why it's important?​

Data sharing is a behavior that we share data to other parties which maybe other departments in the same company, or customers outside the company, etc. The reason why we agree to share data is that we finally realize data is a valuable asset especially to business, since it can make the business process more smooth and enable better decision making results!

When thinking about how to share data with others, there are some common requirements coming up to our minds, such as what are the formats of data being stored, what kind of storage should persist the data, and how to share data with other parties. However, there are also other directions we need to consider and I would like to discuss data privacy with you in this article.

Why data privacy is important to data sharing?​

In terms of sharing data with others, there are certainly some scenraios that we can treat everyone we share with equally.

TLDR: VulcanSQL, a free and open-source data API framework built specifically for data applications, empowers data professionals to generate and distribute data APIs quickly and effortlessly. It takes your SQL templates and transforms them into data APIs, with no backend expertise necessary.

Preface​

Normally, in order to retrieve the data we need from a data source, we have to write SQL statements. However, this process could be time-consuming especially when the data consumers have different requirements in short time. Now, to make this process easier and more flexible to data consumers, VulcanSQL has integrated HuggingFace inference capabilities. This allows us to reduce the need for changing SQL templates by simply allowing data consumers to ask questions and getting the results they need.

Hello, data folks.

Are you building a data application using your data warehouse, be it customer-facing analytics, externally shared APIs, or in-house tools like an admin panel, and finding yourself entangled with latency and cost problems? If so, you've come to the right spot!

What Are the Challenges ?​

When it comes to building data applications on top of your data warehouse, several obstacles can get in the way. Here are the primary obstacles you might come across:

1. High Query Latency: Customer-facing applications require speedy responses, often within milliseconds. Traditional data warehouses, however, are optimized for analytical workloads, which might lead to slow query responses that could negatively impact your application's user experience.

2. Security Concerns: In a world where data breaches are all too common, implementing an application-specific security layer is vital. This is especially important for multi-tenant environments where each user must access only their own data.

3. Cost Considerations: The scalability of your applications to serve a large number of concurrent users can bring about cost challenges. As the user base grows, the associated cost of managing a data warehouse might skyrocket. Striking a balance between scalability and cost can be quite a struggle.