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RCFile
Within computing database management systems, the RCFile (Record Columnar File) is a data placement structure that determines how to store relational tables on computer clusters. It is designed for systems using the MapReduce framework. The RCFile structure includes a data storage format, data compression approach, and optimization techniques for data reading. It is able to meet all the four requirements of data placement: (1) fast data loading, (2) fast query processing, (3) highly efficient storage space utilization, and (4) a strong adaptivity to dynamic data access patterns. RCFile is the result of research and collaborative efforts from Facebook, The Ohio State University, and the Institute of Computing Technology at the Chinese Academy of Sciences. Summary Data storage format For example, a table in a database consists of 4 columns (c1 to c4): To serialize the table, RCFile partitions this table first horizontally and then vertically, instead of only partitioning the table ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Apache Hive
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. Features Apache Hive supports analys ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Apache Parquet
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC, the other columnar-storage file formats in Hadoop, and is compatible with most of the data processing frameworks around Hadoop. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. History The open-source project to build Apache Parquet began as a joint effort between Twitter and Cloudera. Parquet was designed as an improvement on the Trevni columnar storage format created by Doug Cutting, the creator of Hadoop. The first version, Apache Parquet1.0, was released in July 2013. Since April 27, 2015, Apache Parquet has been a top-level Apache Software Foundation (ASF)-sponsored project. Features Apache Parquet is implemented using the record-shredding and assembly algorithm, which accommodates the complex data structures that can be used to store data. The values in each column ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Column-oriented DBMS
A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns (by eliminating the need to read columns that are not relevant), and more options for data compression. However, they are typically less efficient for inserting new data. Practical use of a column store versus a row store differs little in the relational DBMS world. Both columnar and row databases can use traditional database query languages like SQL to load data and perform queries. Both row and columnar databases can become the backbone in a system to serve data for common extract, transform, load (ETL) and tools. Description Background A relational database management system provides data that represents a two-dimensional table of columns and rows. For example, a database might have this table: This simple table includes an employee identifier (EmpId), name f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computing
Computing is any goal-oriented activity requiring, benefiting from, or creating computing machinery. It includes the study and experimentation of algorithmic processes, and development of both hardware and software. Computing has scientific, engineering, mathematical, technological and social aspects. Major computing disciplines include computer engineering, computer science, cybersecurity, data science, information systems, information technology and software engineering. The term "computing" is also synonymous with counting and calculating. In earlier times, it was used in reference to the action performed by mechanical computing machines, and before that, to human computers. History The history of computing is longer than the history of computing hardware and includes the history of methods intended for pen and paper (or for chalk and slate) with or without the aid of tables. Computing is intimately tied to the representation of numbers, though mathematical conc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pig (programming Language)
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java, Python, JavaScript, Ruby or Groovy and then call directly from the language. History Apache Pig was originally developed at Yahoo Research around 2006 for researchers to have an ad hoc way of creating and executing MapReduce jobs on very large data sets. In 2007, it was moved into the Apache Software Foundation. Naming Regarding the naming of the Pig programming language, the name was chosen arbitrarily and stuck because it was memorable, easy to spell, and for novelty. Exampl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Column (data Store)
A column of a distributed data store is a NoSQL object of the lowest level in a keyspace. It is a tuple (a key–value pair) consisting of three elements: * Unique name: Used to reference the column * Value: The content of the column. It can have different types, like AsciiType, LongType, TimeUUIDType, UTF8Type among others. * Timestamp: The system timestamp used to determine the valid content. Usage A column is used as a store for the value and has a timestamp that is used to differentiate the valid content from stale ones. According to the CAP theorem, distributed data stores cannot guarantee consistency, as availability and partition tolerance are more important issues. Therefore, the data store or the application programmer will use the timestamp to find out which of the stored values in the backup nodes are up-to-date. Some data stores, like Riak, may use the more sophisticated vector clock instead of the timestamp to resolve stale information. Differences from a relatio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cloudera
Cloudera, Inc. is an American software company providing enterprise data management systems that make significant use of Apache Hadoop. As of January 31, 2021, the company had approximately 1,800 customers. History Cloudera, Inc. was formed on June 27, 2008, by Christophe Bisciglia (from Google), Amr Awadallah (from Yahoo!), Jeff Hammerbacher (from Facebook), and Mike Olson (from Oracle). Awadallah oversaw a business unit performing data analysis using Hadoop while at Yahoo!; Hammerbacher used Hadoop to develop some of Facebook's data analytics applications; and Olson formerly served as the CEO of Sleepycat Software, the company that created Berkeley DB. The four were joined in 2009 by Doug Cutting, a co-founder of Hadoop. In March 2009, Cloudera released a commercial distribution of Hadoop, in conjunction with a $5 million investment led by Accel Partners. This was followed by a $25 million funding round in October 2010, a $40M funding round in November 2011, and a $160M fundi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hortonworks
Hortonworks was a data software company based in Santa Clara, California that developed and supported open-source software (primarily around Apache Hadoop) designed to manage big data and associated processing. Hortonworks software was used to build enterprise data services and applications such as IoT (connected cars, for example), single view of X (such as customer, risk, patient), and advanced analytics and machine learning (such as next best action and realtime cybersecurity). Hortonworks had three interoperable product lines: * Hortonworks Data Platform (HDP): based on Apache Hadoop, Apache Hive, Apache Spark * Hortonworks DataFlow (HDF): based on Apache NiFi, Apache Storm, Apache Kafka * Hortonworks DataPlane services (DPS): based on Apache Atlas and Cloudbreak and a pluggable architecture into which partners such as IBM can add their services. In January 2019, Hortonworks completed its merger with Cloudera. History Hortonworks was formed in June 2011 as an independen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Apache ORC
The Apache () are a group of culturally related Native American tribes in the Southwestern United States, which include the Chiricahua, Jicarilla, Lipan, Mescalero, Mimbreño, Ndendahe (Bedonkohe or Mogollon and Nednhi or Carrizaleño and Janero), Salinero, Plains (Kataka or Semat or "Kiowa-Apache") and Western Apache ( Aravaipa, Pinaleño, Coyotero, Tonto). Distant cousins of the Apache are the Navajo, with whom they share the Southern Athabaskan languages. There are Apache communities in Oklahoma and Texas, and reservations in Arizona and New Mexico. Apache people have moved throughout the United States and elsewhere, including urban centers. The Apache Nations are politically autonomous, speak several different languages, and have distinct cultures. Historically, the Apache homelands have consisted of high mountains, sheltered and watered valleys, deep canyons, deserts, and the southern Great Plains, including areas in what is now Eastern Arizona, Northern Mexico (Sono ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
