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Showing posts from July, 2022

The Data Mesh - should you adapt?

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In actuality, not every firm may be a good fit for the implementation of a Data Mesh.  Larger enterprises that experience uncertainty and change in their operations and environment are the primary target audience for Data Mesh.  A Data Mesh is definitely an unnecessary expense if your organization's data requirements are modest and remain constant over time. What is a "Data Mesh"? As it focuses on delivering useful and safe data products, Data Mesh is a strategic approach to modern data management and a strategy to support an organization's journey toward digital transformation. Data Mesh's major goal is to advance beyond the established centralized data management techniques of using data warehouses and data lakes. By giving data producers and data consumers the ability to access and handle data without having to go through the hassle of involving the data lake or data warehouse team, Data Mesh highlights the concept of organizational agility. Data Mesh's dec

Regulation-Compliant Federated Data Processing

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Federated data processing has been a standard model for virtual integration of disparate data sources, where each source upholds a certain amount of autonomy. While early federated technologies resulted from mergers, acquisitions, and specialized corporate applications, recent demand for decentralized data storage and computation in information marketplaces and for Geo-distributed data analytics has made federated data services an indispensable component in the data systems market.  At the same time, growing concerns with data privacy propelled by regulations across the world has brought federated data processing under the purview of regulatory bodies.  This series of blog post will discuss challenges in building regulation-compliant federated data processing systems and our initiatives at Databloom that strive towards making compliance as a first-class citizen in our Blossom data platform .   Federated Data Processing Running analytics in a federated environment require distribu

Internationalization: The challenges of building multilingual web applications

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At Databloom, we value diversity. We are a multicultural company with team members from different parts of the world, where we speak a wide variety of languages, such as English, French, German, Greek, Hindi, Korean, and Spanish. Data science teams are also so diverse! For that reason, in Databloom's Blossom Studio , we plan to introduce internationalization and localization features to make our application multilingual. In that context, we want to discuss several aspects that we found relevant when trying to implement a multilingual application. In addition, we also want to share some resources that we found helpful when applying some of these concepts into practice. 1. Translation Methods We have two main options: machine automatic translations , where an external service performs the translation for us (e.g., Google translator API , Amazon translate ), and human translations , where we manually provide the translated texts. Generally speaking, it is helpful to use online transla