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Showing posts from March, 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

The Missing Piece in Learning-based Query Optimization

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Machine Learning (ML) has not only become omnipresent in our everyday lives (with self-driving cars, digital personal assistants, chatbots etc.) but has also started spreading to our core technological systems, such as databases and operating systems. In the area of databases, there is a large amount of works aiming at optimizing data management components, from index building, knob tuning to query optimization. Just in query optimization, ML is used in the place of many optimizer components, such as cardinality estimation, cost model, and join enumeration. In this blog post, we focus on the case of using an ML in the place of a cost model and go from the traditional cost-based query optimization to the newly proposed ML-based query optimization.   ML-based query optimization Generally speaking, given a user query, a query optimizer finds the best way to execute this query (i.e., execution plan) so that the runtime is minimized. For example, Databloom Blossom's query optimi

Challenges and opportunities towards AI solutions adoption

Artificial intelligence solutions have been revolutionizing the industry continuously in the last decades. The benefits delivered by these technologies are numerous and diverse; among others you can find: capacity to improve work efficiency, capacity to analyze big datasets, automate infrastructure for easy escalation, enhance customer experience, etc. Nowadays companies are challenging themselves to obtain benefits from these technologies, even enabling whole organizational transformations, boosting the capacity of the companies from its core. The mere existence of these opportunities implies risk. A company's competitors can and will eventually exploit the capabilities of AI solutions, gaining significant competitive advantages. This fact brings pressure to implement AI strategies with architectural deficiencies based on general misconceptions among business people and AI specialists .  This blog will explore two important problems to consider when integrating AI solutions with

A decade after ‘data is the new soil’, it’s now the time for Blossom

When the Internet started, people were hard-set to provide a name for using the Internet. You have probably heard about "surfing / drowning / diving on the internet" . Those three concepts referred to the capacity we had to explore and use the Internet. Each step forward on the Internet means being hit by tons of data; at that moment, we also start coining metaphors for data.  Using metaphors provides an easy way to compare two concepts or objects, one abstract and complex, and the other familiar; they need to share some characteristics because the familiar concept will provide a way to understand the abstract. Nevertheless, the abstract concept will have many familiar representations hiding some attributes as it becomes more complex.  When people started talking about “ Data is the new oil ” in 2006, they were referring to data as the source of a kind of energy that moves the world into a new area. And, if so, data also creates byproducts for those who work it, too. Those b