- 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
Apr 2
2018
[DB Seminar] Spring 2018: Aaron Harlap
- Speaker:
- Aaron Harlap
PipeDream is a new distributed training system for deep neural networks (DNNs) that partitions ranges of DNN layers among machines, and aggressively pipelines computation and communication. Today’s pervasive use of data-parallel training performs well for DNNs of up to 10–20 million model parameters, but inter-machine communication dominates for models that are even 10x larger (e.g., up to 85% of time... Read More
Mar 26
2018
[DB Seminar] Spring 2018: Alok Pareek (Striim)
- Speaker:
- Alok Pareek
- System:
- Striim
In this seminar - Alok will present Striim, a distributed Streaming platform, and talk about the platform's motivation, distributed architecture, use cases, and open challenges. Read More
Mar 19
2018
[DB Seminar] Spring 2018: Stephen Walkauskas (Vertica)
- Speaker:
- Stephen Walkauskas
- System:
- Vertica
In the beginning there was a DBMS, a flexible piece of software that could be used for OLTP and OLAP workloads. When transaction throughput increased and data sizes grew the database needed to be split into two, each instance optimized for a particular workload. And so it has been ever since and the distance between the two systems has increased,... Read More
Mar 19
2018
Sanjay Krishnan (Berkeley)
- Speaker:
- Sanjay Krishnan
A statistical model is only as good as its training data. Systematic errors can arise when data are integrated from untrustworthy sources, collected in mixed formats, or contain inconsistent references of the same real-world entities. This talk describes the classical relational database topic of "data cleaning", i.e., the process of transforming the data to remove such issues, from a modern... Read More
Feb 26
2018
[DB Seminar] Spring 2018: Ajit Mylavarapu [Oracle]
- Speaker:
- Ajit Mylavarapu
- System:
- Oracle
Analytic workloads in data management systems are dominated by joins, aggregations, scan and filtering costs. In-Memory columnar databases have significantly optimized scans using compressed data formats and SIMD vectorization techniques, but have made little impact to the rest of the query execution plan. The Oracle Database In-Memory (DBIM) Option introduced new SQL execution operators that accelerate a wide range of... Read More
Feb 19
2018
Feb 12
2018
[DB Seminar] Spring 2018: Ziqiang Feng
- Speaker:
- Ziqiang Feng
High-resolution, continuously-recording, and nearly-ubiquitous cameras provide great value for retrospective video analysis tasks in areas such as crime investigations and scientific research. This kind of video analysis task is often both interactive and exploratory in nature, where multiple queries are tried, aborted, refined, and re-executed in an iterative fashion. This exploratory video analysis usage model presents some unique challenges and... Read More
Feb 5
2018
[DB Seminar] Spring 2018: Joy Arulraj
- Speaker:
- Joy Arulraj
We are at an exciting point in the evolution of memory technology. Device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports fast reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design... Read More
Jan 29
2018
[DB Seminar] Spring 2018: Ziqi Wang
- Speaker:
- Ziqi Wang
Abstract: Lock-free data structures have long been rumored to provide better performance and scalability than their lock-based counterparts. On the other hand, however, there are state-of-the-art in-memory indices using various fine-grained synchronization techniques that outperform the classical lock coupling implementation. In this talk, we investigate into a concrete, optimized implementation of a lock-free B+Tree, the Bw-Tree[1], and then conduct an... Read More
Jan 22
2018
[DB Seminar] Spring 2018: Joy Arulraj
- Speaker:
- Joy Arulraj
For the first time since Von Neumann's architecture of the 1940s, device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports low-latency byte-addressable reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design... Read More