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A time series database (TSDB) is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s). In some fields, time series may be called profiles, curves, traces or trends. Several early time series databases are associated with industrial applications which could efficiently store measured values from sensory equipment (also referred to as data historians), but now are used in support of a much wider range of applications.
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Apache ZooKeeper is an open-source server for highly reliable distributed coordination of cloud applications. It is a project of the Apache Software Foundation.
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. Since most data warehousing applications work with SQL-based querying languages, Hive aids portability of SQL-based applications to Hadoop. While initially developed by Facebook, Apache Hive is used and developed by other companies such as Netflix and the Financial Industry Regulatory Authority (FINRA). Amazon maintains a software fork of Apache Hive included in Amazon Elastic MapReduce on Amazon Web Services.
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Apache CarbonData is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Daniel Abadi is the Darnell-Kanal Professor of Computer Science at University of Maryland, College Park and was named an ACM Fellow in 2020. His primary area of research is database systems. He is known for his contributions to distributed databases, column-store databases, deterministic databases, graph databases, and stream databases. Specifically, he developed the storage and query execution engines of the C-Store prototype, which was commercialized by Vertica and later acquired by Hewlett-Packard in 2011. His HadoopDB research on fault tolerant scalable analytical database systems was commercialized by Hadapt and acquired by Teradata in 2014.
Trino is an open-source distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. Trino can query datalakes that contain open column-oriented data file formats like ORC or Parquet residing on different storage systems like HDFS, AWS S3, Google Cloud Storage, or Azure Blob Storage using the Hive and Iceberg table formats. Trino also has the ability to run federated queries that query tables in different data sources such as MySQL, PostgreSQL, Cassandra, Kafka, MongoDB and Elasticsearch. Trino is released under the Apache License.
References
- ↑ "BigQuery under the hood" . Retrieved 2023-05-25.
- ↑ "Apache Drill - Architecture Introduction" . Retrieved 2017-10-08.
- ↑ "Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real" . Retrieved 2017-10-08.
- ↑ "Recognizing A New Tier" . Retrieved 2018-05-01.
- ↑ "VLDB Test of Time Award". www.vldb.org. Retrieved 2021-01-20.
- Melnik, Sergey; Gubarev, Andrey; Long, Jing Jing; Romer, Geoffrey; Shivakumar, Shiva; Tolton, Matt; Vassilakis, Theo (2010). "Dremel: Interactive Analysis of Web-Scale Datasets". Proc. of the 36th Int'l Conf on Very Large Data Bases: 330–339.
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