- 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
Nov 2
2015
[DB Seminar] Fall 2015: Prashanth Menon
- Speaker:
- Prashanth Menon
Modern write-intensive key-value stores have emerged as the prevailing data storage system for many big applications. However, these systems often sacrifice their read performance to cope with high data ingestion rates. Solid-state drives (SSD) can lend their help, but their limited capacity and their peculiar characteristics make their exclusive use uneconomical. Hence, hybrid storage environments with both SSDs and hard-disk... Read More
Oct 26
2015
[DB Seminar] Fall 2015: Round table
Seeing as I've not been able to get a speaker for today, I think we can default to a round table discussion. I think it would be nice to briefly discuss what we are all working on now, after the WWW/SDM deadlines. We can additionally discuss the focus of some of the works we submitted to these conferences, if there is interest in that. Next week,... Read More
Oct 22
2015
The Journey from Faster to Better (Igor Canadi + Mark Callaghan)
- Speakers:
- Igor Canadi , Mark Callaghan
- System:
- RocksDB
- Video:
- YouTube
RocksDB has been adapted to a wide range of workloads. Early adopters needed a DBMS for low-latency & high-throughput workloads. Using RocksDB as an embedded DBMS eliminates network latency per request. When using RocksDB with a fast SSD we are able to get more IO throughput compared to other open-source DBMS that we use in production. We continue to get... Read More
Oct 20
2015
Charlie Swanson (MongoDB)
- Speaker:
- Charlie Swanson
- System:
- MongoDB
From "How many documents are in my collection?" to "What state has the highest percentage of people living in its most populous city?", there are many questions MongoDB can answer about your data. In this talk, we'll see what sorts of questions can be asked, and how MongoDB finds the most efficient way to answer them. Determining the best way... Read More
Oct 19
2015
[DB Seminar] Fall 2015: Alex Beutel
- Speaker:
- Alex Beutel
Which seems more suspicious: 5,000 tweets from 200 users on 5 IP addresses, or 10,000 tweets from 500 users on 500 IP addresses but all with the same trending topic and all in 10 minutes? The literature has many methods that try to find dense blocks in matrices, and, recently, tensors, but no method gives a principled way to score... Read More
Oct 12
2015
DB Seminar [Fall 2015]: more WIN discussion
We'll continue last week's theme of discussing more cool talks from WIN. Other WIN attendees -- please bring some notes about the talks you liked/didn't like or thought-provoking questions from the workshop. Read More
Oct 8
2015
The Lightning Memory-Mapped Database (Howard Chu)
- Speaker:
- Howard Chu
- System:
- LMDB
- Video:
- YouTube
The Lightning Memory-Mapped Database (LMDB) was introduced at LDAPCon 2011 and has been enjoying tremendous success in the intervening time. LMDB was written for the OpenLDAP Project and has proved to be the world's smallest, fastest, and most reliable transactional embedded data store. It has cemented OpenLDAP's position as world's fastest directory server, and its adoption outside the OpenLDAP Project... Read More
Oct 5
2015
DB Seminar [Fall 2015]: Kijung Shin
- Speaker:
- Kijung Shin
Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes long, various preprocessing methods, most of which are related... Read More
Sep 28
2015
DB Seminar [Fall 2015]: Gene Davis (Splice Machine)
- Speaker:
- Gene Davis
- System:
- Splice Machine
In this talk, Gene will discuss Splice Machine, a full RDBMS built on top of Hadoop. He will describe what makes Splice Machine work and what their team is working on now to make it even more performant on mixed database workloads. Read More
Sep 17
2015
SQLite, A Database for the Edge of the Network (Richard Hipp)
- Speaker:
- D. Richard Hipp
- System:
- SQLite
- Video:
- YouTube
SQLite is a full-featured embedded SQL database engine, and the world's most widely deployed and used database engine. The design and implementation of SQLite is different from most other RDBMSes, since SQLite strives to solve a different problem. This talk describes the problem that SQLite solves and overviews the design choices and algorithms that SQLite uses in its unique role. Read More