- Aerospike
- Akamas
- AlloyDB
- ApertureDB
- Arrow
- Berkeley DB
- BlazingDB
- Brytlyt
- Chaos Mesh
- Citus
- CockroachDB
- Convex
- CrateDB
- Databricks
- Datometry
- dbt
- Delta Lake
- Dremio
- DSQL
- DVMS
- EraDB
- eXtremeDB
- Fauna
- Featureform
- Firebolt
- Fluss
- Gaia
- GlareDB
- GoogleSQL
- GreptimeDB
- Heron
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Milvus
- MonetDB
- Mooncake
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- OxQL
- Pinecone
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- SingleStore
- SLOG
- Snowflake
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TiDB
- TileDB
- Tokutek
- Umbra
- Vertica
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- AirFlow
- Alibaba
- Anna
- APOLLO
- Azure Cosmos DB
- BigQuery
- Bodo
- Cassandra
- Chroma
- ClickHouse
- Confluent
- CouchDB
- CrocodileDB
- DataFusion
- Datomic
- Debezium
- Dolt
- Druid
- DuckDB
- EdgeDB
- Exon
- FASTER
- FeatureBase
- Feldera
- Fluree
- FoundationDB
- Gel
- Google Spanner
- Greenplum
- HarperDB
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Modin
- MongoDB
- MotherDuck
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- ParadeDB
- Pinot
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- sled
- Smooth
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TerminusDB
- TigerBeetle
- TimescaleDB
- Trino
- Velox
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- Aerospike
- Alibaba
- ApertureDB
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Citus
- Confluent
- CrateDB
- DataFusion
- dbt
- Dolt
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Milvus
- MongoDB
- MySQL
- NoisePage
- OceanBase
- OtterTune
- Pinecone
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- SLOG
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TigerBeetle
- Tokutek
- Velox
- VoltDB
- WiredTiger
- YugabyteDB
- AirFlow
- AlloyDB
- APOLLO
- Berkeley DB
- Bodo
- Chaos Mesh
- ClickHouse
- Convex
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Modin
- Mooncake
- Napa
- Noria
- OpenDAL
- OxQL
- Pinot
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- SingleStore
- Smooth
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TerminusDB
- TileDB
- Trino
- Vertica
- Vortex
- XTDB
- Akamas
- Anna
- Arrow
- BigQuery
- Brytlyt
- Chroma
- CockroachDB
- CouchDB
- Databricks
- Datomic
- Delta Lake
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- MonetDB
- MotherDuck
- Neon
- NuoDB
- Oracle
- ParadeDB
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- sled
- Snowflake
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TiDB
- TimescaleDB
- Umbra
- Vitesse
- Weaviate
- Yellowbrick
- Aerospike
- AlloyDB
- Arrow
- BlazingDB
- Chaos Mesh
- CockroachDB
- CrateDB
- Datometry
- Delta Lake
- DSQL
- EraDB
- Fauna
- Firebolt
- Gaia
- GoogleSQL
- Heron
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- MonetDB
- MySQL
- Noria
- Oracle
- Pinecone
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- SLOG
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TiDB
- Tokutek
- Vertica
- Weaviate
- YugabyteDB
- AirFlow
- Anna
- Azure Cosmos DB
- Bodo
- Chroma
- Confluent
- CrocodileDB
- Datomic
- Dolt
- DuckDB
- Exon
- FeatureBase
- Fluree
- Gel
- Greenplum
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MongoDB
- Napa
- NuoDB
- OtterTune
- Pinot
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- Smooth
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TigerBeetle
- Trino
- Vitesse
- WiredTiger
- Akamas
- ApertureDB
- Berkeley DB
- Brytlyt
- Citus
- Convex
- Databricks
- dbt
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluss
- GlareDB
- GreptimeDB
- Iceberg
- kdb
- LeanStore
- MapD
- Milvus
- Mooncake
- Neon
- OceanBase
- OxQL
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- SingleStore
- Snowflake
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TileDB
- Umbra
- VoltDB
- XTDB
- Alibaba
- APOLLO
- BigQuery
- Cassandra
- ClickHouse
- CouchDB
- DataFusion
- Debezium
- Druid
- EdgeDB
- FASTER
- Feldera
- FoundationDB
- Google Spanner
- HarperDB
- Impala
- Kinetica
- Litestream
- MariaDB
- Modin
- MotherDuck
- NoisePage
- OpenDAL
- ParadeDB
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- sled
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TerminusDB
- TimescaleDB
- Velox
- Vortex
- Yellowbrick
Sep 14
2023
[Fall 2023] Snowflake Tech Talk (Bowei Chen)
- Speaker:
- Bowei Chen
- System:
- Snowflake
Snowflake internals tech talk. Read More
Sep 11
2023
[ML⇄DB 2023] Qdrant: Vector Search Engine Internals (Andrey Vasnetsov)
- Speaker:
- Andrey Vasnetsov
- System:
- Qdrant
- Video:
- YouTube
This talk is part of the ML⇄DB Seminar Series. Zoom Link: https://cmu.zoom.us/j/91461275681 (Passcode 177332) Read More
Aug 31
2023
[Fall 2023] New Semester Research Meeting
Carnegie Mellon University's renowned Database Group is thrilled to announce the kickoff of its Fall 2023 semester with a highly anticipated meeting on Thursday, August 31st. This gathering promises to be an extraordinary occasion as the members embark on a vainglorious retrospective, celebrating the group's remarkable research achievements over the years, as well as to begin discussions on new projects.... Read More
Aug 23
2023
On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments (Matt Butrovich)
- Speaker:
- Matt Butrovich
The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel- space) limit query processing responsiveness. To avoid these overheads, user-space applications prioritizing performance over simplicity can... Read More
Jul 13
2023
[Summer 2023] Towards a Hardware-software Approach for High-performance Databases (Jignesh Patel)
- Speaker:
- Jignesh Patel
In various industries, in-database analytics are crucial for decision-making. Yet, the growing amount of data presents challenges as traditional methods become excessively time-consuming and costly. Moore's Law and Dennard scaling, which previously aided data-intensive analytics, are reaching their physical limits. A new approach is needed to handle analytics workloads. The speed gap between processing units and memory creates bottlenecks for... Read More
May 5
2023
[Spring 2023] 15-721 Final Project Presentations
Carnegie Mellon University is thrilled to announce the upcoming final project presentations for the Advanced Database Systems course for the Spring 2023 semester. This eagerly awaited event showcases the hard work and innovation of the university's talented students as they present their cutting-edge projects to an audience of esteemed guests and fellow students. The presentations promise to deliver captivating insights... Read More
Mar 30
2023
Return of the Database Machines? Towards a Hardware-Software Approach for High-performance Databases (Jignesh Patel)
- Speaker:
- Jignesh Patel
Analytic database applications have an insatiable appetite for higher performance. In the past, a large part of this appetite was met by leveraging the gift of Moore’s Law. However, the slowing down of Moore’s Law now requires a new approach. Fortunately, the hardware landscape is currently undergoing a Cambrian explosion of new architectures. In this talk, I will describe how... Read More
Dec 13
2022
MS Thesis Defense: High Performance DBMS Design for Intelligent Query Scheduling (Deepayan Patra)
- Speaker:
- Deepayan Patra
Decades of research in the field of database management systems (DBMSs) have focused on improving system performance with impressive results. Modern analytical databases take advantage of innovative methods such as vectorization and compilation to improve single query performance, use supporting data structures such as indexes or views to reduce data access requirements, and support the execution of multiple queries in... Read More
Dec 13
2022
MS Thesis Defense: Extendable Rule-Based Action Generation for Self-Driving Database Systems (Mike Xu)
- Speaker:
- Mike Xu
Database management systems (DBMSs) have become more complex to meet increasingly demanding usage. To owners and operators, the need for a self-driving DBMS that can automatically tune and optimize itself without human intervention is apparent now more than ever. Such a self-driving DBMS considers a set of candidate actions to apply to reach a configuration that improves performance for a... Read More
Dec 8
2022
[15-445/645] Fall 2022 Live Call-in Q&A Lecture
- Speaker:
- Andy Pavlo
For the final lecture in CMU's Introduction to Database Systems (Fall 2022) course, we are allowing anyone to call in with their database questions. The lecture will be livestreamed via Youtube and you will be able to ask your questions to Prof. Andy Pavlo directly. Livestream: https://youtu.be/MxOKUt6LeeU Audience Call-in: https://cmu.zoom.us/j/99783788428?pwd=R2ZSd2x0SUFnRlNIak5TVk5ubmFjQT09 (Must have Zoom account) Read More