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  2. Extract, load, transform - Wikipedia

    en.wikipedia.org/wiki/Extract,_load,_transform

    [1] [2] 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. [3] [4]

  3. Amazon Redshift - Wikipedia

    en.wikipedia.org/wiki/Amazon_Redshift

    Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. [1] It is built on top of technology from the massive parallel processing (MPP) data warehouse company ParAccel (later acquired by Actian ), [ 2 ] to handle large scale data sets and database migrations .

  4. Amazon Kinesis - Wikipedia

    en.wikipedia.org/wiki/Amazon_Kinesis

    Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. [2]

  5. Shard (database architecture) - Wikipedia

    en.wikipedia.org/wiki/Shard_(database_architecture)

    A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Each shard may be held on a separate database server instance, to spread load. Some data in a database remains present in all shards, [a] but some appears only in a single shard. Each shard acts as the single source for this subset of data.

  6. zstd - Wikipedia

    en.wikipedia.org/wiki/Zstd

    It was also used to create a proof-of-concept OpenZFS compression method [8] which was integrated in 2020. [21] The AWS Redshift and RocksDB databases include support for field compression using Zstandard. [22] In March 2018, Canonical tested [23] the use of zstd as a deb package compression method by default for the Ubuntu Linux distribution.

  7. Amazon SageMaker - Wikipedia

    en.wikipedia.org/wiki/Amazon_SageMaker

    Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, [9] AWS Batch for offline batch processing, [9] [10] or Amazon Kinesis for real-time processing.

  8. Presto (SQL query engine) - Wikipedia

    en.wikipedia.org/wiki/Presto_(SQL_query_engine)

    Presto (including PrestoDB, and PrestoSQL which was re-branded to Trino) is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, [1] and allows use of multiple data sources within a query.

  9. AWS CloudFormation - Wikipedia

    en.wikipedia.org/wiki/AWS_CloudFormation

    AWS CloudFormation is a service provided by Amazon Web Services (AWS) that enables users to model and manage infrastructure resources in an automated and secure manner. [1] Using CloudFormation, developers can define and provision AWS infrastructure resources using a JSON - or YAML -formatted infrastructure as code template.