Microsoft recently introduced Azure Synapse Analytics, which offers an integrated experience across data ingestion, storage, and querying in Apache Spark and T-SQL over data in the lake, including files and warehouse tables. In this paper, we present our experiences with designing and implementing Hyperspace, the indexing subsystem underlying Synapse. Hyperspace enables users to build multiple types of secondary indexes on their data, maintain them through a multi-user concurrency model, and leverage them automatically---without any change to their application code---for query/workload acceleration. Many requirements of Hyperspace are based on feedback from several enterprise customers. We present the details of Hyperspace's underlying design, the user-facing APIs, its concurrency control protocol for index access, its index-aware query processing techniques, and its maintenance mechanisms for handling index updates. Evaluations over standard industry benchmarks and real customer workloads show that Hyperspace can accelerate query execution by up to 10x and in certain real-world workloads, even up to two orders of magnitude.

Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relational engines. The system collects close to a quadrillion events indexing approximately 16 trillion search keys per day from hundreds of thousands of machines across tens of data centers around the world. Helios use cases within Microsoft include debugging/diagnostics in both public and government clouds, workload characterization, cluster health monitoring, deriving business insights and performing impact analysis of incidents in other large-scale systems such as Azure Data Lake and Cosmos. Helios also serves as a reference blueprint for other large-scale systems within Microsoft. We present the simple data model behind Helios, which offers great flexibility and control over costs, and enables the system to asynchronously index massive streams of data. We also present our experiences in building and operating Helios over the last five years at Microsoft.

In large enterprises, data discovery is a common problem faced by users who need to find relevant information in relational databases. In this scenario, schema annotation is a useful tool to enrich a database schema with descriptive keywords. In this paper, we demonstrate Barcelos, a system that automatically annotates corporate databases. Unlike existing annotation approaches that use Web oriented knowledge bases, Barcelos mines enterprise spreadsheets to find candidate annotations. Our experimental evaluation shows that Barcelos produces high quality annotations; the top-5 have an average precision of 87%.

An enterprise information worker is often aware of a few example tuples (but not the entire result) that should be present in the output of the query. We study the problem of discovering the minimal project join query that contains the given example tuples in its output. Efficient discovery of such queries is challenging. We propose novel algorithms to solve this problem. Our experiments on real-life datasets show that the proposed solution is significantly more efficient compared with na\"{i}ve adaptations of known techniques.

Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.