- Aerospike
- Akamas
- AlloyDB
- Antithesis
- APOLLO
- Aurora DSQL
- Berkeley DB
- BlazingDB
- Brytlyt
- Chaos Mesh
- Chronon
- ClickHouse
- Confluent
- CouchDB
- CrocodileDB
- Databricks
- Datometry
- DB9
- Debezium
- Dolt
- Dremio
- DSQL
- DVMS
- EraDB
- eXtremeDB
- Fauna
- Featureform
- Firebolt
- Fluree
- FoundationDB
- Gel
- Google Spanner
- Greenplum
- HarperDB
- HorizonDB
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Microsoft SQL Server
- Modin
- MongoDB
- MotherDuck
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- Oxla
- ParadeDB
- Pinot
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- Sentry
- Sirius
- SLOG
- Snowflake
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TeraData
- TiDB
- TileDB
- Tokutek
- TopK
- turbopuffer
- Umbra
- Vertica
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- AirFlow
- Alibaba
- Anna
- ApertureDB
- Arrow
- Azure Cosmos DB
- BigQuery
- Bodo
- Cassandra
- Chroma
- Citus
- CockroachDB
- Convex
- CrateDB
- Daft
- DataFusion
- Datomic
- dbt
- Delta Lake
- Doris
- Druid
- DuckDB
- EdgeDB
- Exon
- FASTER
- FeatureBase
- Feldera
- Floe
- Fluss
- Gaia
- GlareDB
- GoogleSQL
- GreptimeDB
- Heron
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Milvus
- MonetDB
- Mooncake
- Multigres
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- OxQL
- Pinecone
- Pixeltable
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- SingleStore
- sled
- Smooth
- SpacetimeDB
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TerminusDB
- TigerBeetle
- TimescaleDB
- TonicDB
- Trino
- Turso
- Velox
- VillageSQL
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- Aerospike
- Alibaba
- Antithesis
- Arrow
- Berkeley DB
- Bodo
- Chaos Mesh
- Citus
- Confluent
- CrateDB
- Databricks
- Datomic
- Debezium
- Doris
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Milvus
- MongoDB
- Multigres
- Neon
- NuoDB
- Oracle
- OxQL
- Pinot
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- Sirius
- Smooth
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TiDB
- TimescaleDB
- TopK
- Turso
- Vertica
- VoltDB
- WiredTiger
- YugabyteDB
- AirFlow
- AlloyDB
- ApertureDB
- Aurora DSQL
- BigQuery
- Brytlyt
- Chroma
- ClickHouse
- Convex
- CrocodileDB
- DataFusion
- DB9
- Delta Lake
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Floe
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- Modin
- Mooncake
- MySQL
- NoisePage
- OceanBase
- OtterTune
- ParadeDB
- Pixeltable
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- Sentry
- sled
- Snowflake
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TeraData
- TigerBeetle
- Tokutek
- Trino
- Umbra
- VillageSQL
- Vortex
- XTDB
- Akamas
- Anna
- APOLLO
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Chronon
- CockroachDB
- CouchDB
- Daft
- Datometry
- dbt
- Dolt
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- HorizonDB
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Microsoft SQL Server
- MonetDB
- MotherDuck
- Napa
- Noria
- OpenDAL
- Oxla
- Pinecone
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- SingleStore
- SLOG
- SpacetimeDB
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TerminusDB
- TileDB
- TonicDB
- turbopuffer
- Velox
- Vitesse
- Weaviate
- Yellowbrick
- Aerospike
- AlloyDB
- APOLLO
- Berkeley DB
- Brytlyt
- Chronon
- Confluent
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluree
- Gel
- Greenplum
- HorizonDB
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- Modin
- MotherDuck
- Neon
- OceanBase
- Oxla
- Pinot
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- Sirius
- Snowflake
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TiDB
- Tokutek
- turbopuffer
- Vertica
- Vortex
- Yellowbrick
- AirFlow
- Anna
- Arrow
- BigQuery
- Cassandra
- Citus
- Convex
- Daft
- Datomic
- Delta Lake
- Druid
- EdgeDB
- FASTER
- Feldera
- Fluss
- GlareDB
- GreptimeDB
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MonetDB
- Multigres
- NoisePage
- OpenDAL
- OxQL
- Pixeltable
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- sled
- SpacetimeDB
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TigerBeetle
- TonicDB
- Turso
- VillageSQL
- Weaviate
- YugabyteDB
- Akamas
- Antithesis
- Aurora DSQL
- BlazingDB
- Chaos Mesh
- ClickHouse
- CouchDB
- Databricks
- DB9
- Dolt
- DSQL
- EraDB
- Fauna
- Firebolt
- FoundationDB
- Google Spanner
- HarperDB
- Iceberg
- kdb
- LeanStore
- MapD
- Microsoft SQL Server
- MongoDB
- MySQL
- Noria
- Oracle
- ParadeDB
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- Sentry
- SLOG
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TeraData
- TileDB
- TopK
- Umbra
- Vitesse
- WiredTiger
- Alibaba
- ApertureDB
- Azure Cosmos DB
- Bodo
- Chroma
- CockroachDB
- CrateDB
- DataFusion
- dbt
- Doris
- DuckDB
- Exon
- FeatureBase
- Floe
- Gaia
- GoogleSQL
- Heron
- Impala
- Kinetica
- Litestream
- MariaDB
- Milvus
- Mooncake
- Napa
- NuoDB
- OtterTune
- Pinecone
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- SingleStore
- Smooth
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TerminusDB
- TimescaleDB
- Trino
- Velox
- VoltDB
- XTDB
Aug 29
2016
[DB Seminar] Fall 2016: First Meeting + PKU/CMU Interns
- Speakers:
- Bohan Zhang, Yuemei Zhang
This is the first meeting of the Database Group for Fall 2016. We will do the usual meet & greet, followed by two talks from PKU/CMU summer interns. Read More
May 10
2016
Robson Cordeiro (University of Sao Paulo)
- Speaker:
- Dr. Robson Cordeiro
Given a data stream with many attributes and high frequency of events, how to cluster similar events? Can it be done in real time? For example, how to cluster decades of frequent measurements of tens of climatic attributes to aid real time alert systems in forecasting extreme climatic events, such as floods and hurricanes? The task of clustering data with... Read More
May 9
2016
[DB Seminar] Spring 2016: Lin Ma
- Speaker:
- Lin Ma
In-memory database management systems (DBMSs) outperform disk-oriented systems for on-line transaction processing (OLTP) workloads. But this improved performance is only achievable when the database is smaller than the amount of physical memory available in the system. To overcome this limitation, some in-memory DBMSs can move cold data out of volatile DRAM to secondary storage. Such data appears as if it... Read More
May 3
2016
[PDL Visit Day 2016] Siying Dong (Facebook)
- Speaker:
- Siying Dong
- System:
- RocksDB
RocksDB is an embedded persistent key-value store for low-latency and high-throughput workload. It has been adapted to a wide range of workloads, including RocksDB as an embedded DBMS and as storage engines of other DBMS systems. Our benchmarks show RocksDB can achieve 126K random reads per second on flash and 7 million random reads per second on memory. RocksDB also... Read More
May 3
2016
[PDL Visit Day 2016] Thomas Baby (Oracle)
- Speaker:
- Thomas Baby
- System:
- Oracle
The IT industry today is undergoing a revolutionary change in how customers deploy and configure their compute resources. Driven by the demand to reduce costs, both in capital and operation expense, these customers are turning to CLOUD or HYBRID-CLOUD solutions. These customers span the spectrum from very small startup businesses to Fortune 500 companies across regions and industries. Oracle Corporation... Read More
May 3
2016
[PDL Visit Day 2016] Shasank Chavan (Oracle)
- Speaker:
- Shasank Chavan
- System:
- Oracle
The Database In-Memory (DBIM) Option by Oracle is an industry-first dual format in-memory database that maintains transactional consistent data in both row and columnar formats. This unique architecture enables analytic and OLTP workloads to coexist simultaneously, bringing together the best of both worlds. DBIM is the fastest growing database option since its release in 2014, achieving great success with customer... Read More
May 2
2016
[DB Seminar] Spring 2016: Huanchen Zhang
- Speaker:
- Huanchen Zhang
Using indexes for query execution is crucial for achieving high performance in modern on-line transaction processing databases. For a main-memory database, however, these indexes consume a large fraction of the total memory available and are thus a major source of storage overhead of in-memory databases. To reduce this overhead, we propose using a two-stage index: The first stage ingests all... Read More
Apr 28
2016
Murat Demirbas (University at Buffalo)
- Speaker:
- Murat Demirbas
Work on theory of distributed systems abstract away from the physical-clock time and use the notion of logical clocks for ordering events in asynchronous distributed systems. Practice of distributed systems, on the other hand, employ loosely synchronized clocks using NTP in a best-effort manner without any guarantees. Recently, we introduced a third option: hybrid clocks. Hybrid clocks combine the best... Read More
Apr 25
2016
[DB Seminar] Spring 2016: Miguel Araujo
- Speaker:
- Miguel Araujo
Miguel will give a practice talk on his thesis proposal. Abstract: The identification of anomalies and communities of nodes in real-world graphs has applications in widespread domains, from the automatic categorization of wikipedia articles or websites to bank fraud detection. While recent and ongoing research is supplying tools for the analysis of simple unlabeled data, it is still a challenge to find patterns and anomalies in large labeled... Read More
Apr 18
2016
[DB Seminar] Spring 2016: Dana Van Aken
- Speaker:
- Dana Van Aken
Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that... Read More