Abstract: The techniques and/or systems described herein implement erasure coding to generate various chunks for a data collection (e.g., data chunks and at least one encoding chunk). The chunks are then distributed and stored within an individual group (e.g., a pod) of storage units, where a pod of storage units is determined based on characteristics that affect an amount of time it takes to recover a data collection or to restore lost data.

Abstract: In some examples, an erasure code can be implemented to provide for fault-tolerant storage of data. Maximally recoverable cloud codes, resilient cloud codes, and robust product codes are examples of different erasure codes that can be implemented to encode and store data. Implementing different erasure codes and different parameters within each erasure code can involve trade-offs between reliability, redundancy, and locality. In some examples, an erasure code can specify placement of the encoded data on machines that are organized into racks.

Abstract: The techniques and/or systems described herein implement erasure coding to generate various chunks for a data collection (e.g., data chunks and at least one encoding chunk). The chunks are then distributed and stored within an individual group (e.g., a pod) of storage units, where a pod of storage units is determined based on characteristics that affect an amount of time it takes to recover a data collection or to restore lost data.

Abstract: In some examples, an erasure code can be implemented to provide for fault-tolerant storage of data. Maximally recoverable cloud codes, resilient cloud codes, and robust product codes are examples of different erasure codes that can be implemented to encode and store data. Implementing different erasure codes and different parameters within each erasure code can involve trade-offs between reliability, redundancy, and locality. In some examples, an erasure code can specify placement of the encoded data on machines that are organized into racks.

Abstract: Embodiments of the present invention relate to systems, methods and computer storage media for providing Structured Computations Optimized for Parallel Execution (SCOPE) that facilitate analysis of a large-scale dataset utilizing row data of those data sets. SCOPE includes, among other features, an extract command for extracting data bytes from a data stream and structuring the data bytes as data rows having strictly defined columns. SCOPE also includes a process command and a reduce command that identify data rows as inputs. The reduce command also identifies a reduce key that facilitates the reduction based on the reduce key. SCOPE additionally includes a combine command that identifies two data row sets that are to be combined based on an identified joint condition. Additionally, SCOPE includes a select command that leverages SQL and C# languages to create an expressive script that is capable of analyzing large-scale data sets in a parallel computing environment.