The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

Predicting personalized sequential behavior is a key task for recommender systems. In order
to predict user actions such as the next product to purchase, movie to watch, or place to visit …

Many organizations are shifting to a data management paradigm called the" Lakehouse,"
which implements the functionality of structured data warehouses on top of unstructured …

This article provides a comprehensive overview of the broad area of semantic search on text
and knowledge bases. In a nutshell, semantic search is “search with meaning”. This …

Graph analysis performs many random reads and writes, thus, these workloads are typically
performed in memory. Traditionally, analyzing large graphs requires a cluster of machines …

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse solution that
makes it simple and cost-effective to efficiently analyze large volumes of data using existing …

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in Rand Python, dataframes face performance …

The increasing demand for extracting value out of ever-growing data poses an ongoing
challenge to system designers, a task only made trickier by the end of Dennard scaling. As …

Modern state-of-the-art database systems are designed around a single data storage layout.
This is a fixed decision that drives the whole architectural design of a database system, ie …

We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based
key-value stores. The core intuition is that we can sacrifice filter probe time because it is not …