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Extract, Load, Transform
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format. This enables faster loading times. However, ELT requires sufficient processing power within the data processing engine to carry out the transformation on demand, to return the results in a timely manner. Since the data is not processed on entry to the data lake, the query and schema do not need to be defined a priori (although often the schema will be available during load since many data sources are extracts from databases or similar structured data systems and hence have an associated schema). ELT is a data pipeline model. [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Extract, Transform, Load
In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed (cleaned, sanitized, scrubbed) and loaded into an output data container. The data can be collated from one or more sources and it can also be outputted to one or more destinations. ETL processing is typically executed using software applications but it can also be done manually by system operators. ETL software typically automates the entire process and can be run manually or on reoccurring schedules either as single jobs or aggregated into a batch of jobs. A properly designed ETL system extracts data from source systems and enforces data type and data validity standards and ensures it conforms structurally to the requirements of the output. Some ETL systems can also deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions. The ETL process became a popular concept in the 1970s and is often used in d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Lake
A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data ( CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video). A data lake can be established "on premises" (within an organization's data centers) or "in the cloud" (using cloud services from vendors such as Amazon, Microsoft, or Google). Background James Dixon, then chief technology officer at Pentaho, coined the term by 2011 to contrast it with data mart, which is a smaller repository of interesting attributes derived from raw data. In promoting data lakes, he argued that data marts h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data
In the pursuit of knowledge, data (; ) is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and in virtually every other form of human organizational activity. Examples of data sets include price indices (such as consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures which can be used in such a manner in order to capture the useful information out of it. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Processing
Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of ''information processing'', which is the modification (processing) of information in any manner detectable by an observer.Data processing is distinct from ''word processing'', which is manipulation of text specifically rather than data generally. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing functions Data processing may involve various processes, including: * Validation – Ensuring that supplied data is correct and relevant. * Sorting – "arranging items in some sequence and/or in different sets." * Summarization(statistical) or (automatic) – reducing detailed data to its main points. * Aggregation – combining multiple pieces of data. * Analysis – the "collection, organization, an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pipeline (computing)
In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines include: * Instruction pipelines, such as the classic RISC pipeline, which are used in central processing units (CPUs) and other microprocessors to allow overlapping execution of multiple instructions with the same circuitry. The circuitry is usually divided up into stages and each stage processes a specific part of one instruction at a time, passing the partial results to the next stage. Examples of stages are instruction decode, arithmetic/logic and register fetch. They are related to the technologies of superscalar execution, operand forwarding, speculative execution and out-of-order execution. * Graphics pipelines, found in mo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amazon S3
Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Amazon S3 uses the same scalable storage infrastructure that Amazon.com uses to run its e-commerce network. Amazon S3 can store any type of object, which allows uses like storage for Internet applications, backups, disaster recovery, data archives, data lakes for analytics, and hybrid cloud storage. AWS launched Amazon S3 in the United States on March 14, 2006, then in Europe in November 2007. Design Amazon S3 manages data with an object storage architecture which aims to provide scalability, high availability, and low latency with high durability. The basic storage units of Amazon S3 are objects which are organized into buckets. Each object is identified by a unique, user-assigned key. Buckets can be managed using the console provided by Amazon S3, programmatically with the AWS SDK, or the REST application programming ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amazon RDS
Amazon Relational Database Service (or Amazon RDS) is a distributed relational database service by Amazon Web Services (AWS). It is a web service running "in the cloud" designed to simplify the setup, operation, and scaling of a relational database for use in applications. Administration processes like patching the database software, backing up databases and enabling point-in-time recovery are managed automatically. Scaling storage and compute resources can be performed by a single API call to the AWS control plane on-demand. AWS does not offer an SSH connection to the underlying virtual machine as part of the managed service. Timeline Amazon RDS was first released on 22 October 2009, supporting MySQL databases. This was followed by support for Oracle Database in June 2011, Microsoft SQL Server in May 2012, PostgreSQL in November 2013, and MariaDB (a fork of MySQL) in October 2015, and an additional 80 features during 2017. In November 2014 AWS announced Amazon Aurora, a My ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google Cloud Storage
Google Cloud Storage is a RESTful online file storage web service for storing and accessing data on Google Cloud Platform infrastructure. The service combines the performance and scalability of Google's cloud with advanced security and sharing capabilities. It is an ''Infrastructure as a Service'' (IaaS), comparable to Amazon S3. Contrary to Google Drive and according to different service specifications, Google Cloud Storage appears to be more suitable for enterprises. Feasibility User activation is resourced through the API Developer Console. Google Account holders must first access the service by logging in and then agreeing to the Terms of Service, followed by enabling a billing structure. Design Google Cloud Storage stores objects (originally limited to 100 GiB, currently up to 5 TiB) in projects which are organized into buckets. All requests are authorized using Identity and Access Management policies or access control lists associated with a user or service account. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Azure Data Lake
Azure Data Lake is a scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud. History Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. Azure Data Lake Store Users can store structured, semi-structured or unstructured data produced from applications including social networks, relational data, sensors, videos, web apps, mobile or desktop devices. A single Azure Data Lake Store account can store trillions of files where a single file can be greater than a petabyte in size. Azure Data Lake Analytics Azure Data Lake Analytics is a parallel on-demand job service. The parallel processing system is based on Microsoft Dryad. Dryad can represent arbitrary Directed Acyclic Graphs (DAGs) of computation. D ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
