Many database optimization problems, eg, slow SQL diagnosis, database testing, optimizer
tuning, require a large volume of SQL queries. Due to privacy issues, it is hard to obtain real …

This work studies the problem of constructing a representative workload from a given input
analytical query workload where the former serves as an approximation with guarantees of …

Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-
data services. At the same time, it is incredibly hard for users to allocate resources per query …

Optimizing resource allocation for analytical workloads is vital for reducing operational costs
in modern cloud-oriented query processing services. At the same time, it is incredibly hard …

Today cloud companies offer fully managed Spark services. This has made it easy to
onboard new customers but has also increased the volume of users and their workload …

Selecting the optimal cloud target to migrate SQL estates from on-premises to the cloud
remains a challenge. Current solutions are not only time-consuming and error-prone …

Modern DBMS engines can achieve unprecedented transaction processing speeds thanks
to the invention of clever data structures, concurrency schemes, and improvements in CPU …

Data processing engines increasingly leverage distributed file systems for scalable, cost-
effective storage. While the Apache Parquet columnar format has become a popular choice …

This paper aims to initiate a discussion around benchmarking data management systems
with machine-learned components. Traditional benchmarks such as TPC or YCSB are …

Database management systems~(DBMS) are crucial architectural components of any
modern distributed software system. Yet, ensuring a smooth, high-performant operation of a …