Quantum Corporation

Data Culpa Inc.

Cobalt Iron, Inc.

Lassofx, Inc.

Quatum Corporation

J Mitchell Haile

Gregory L Wade

Barry Herman

Bill Kan

Robert Merrill Marett

Richard Raymond Spurlock

Michael T Rowan

Display screen or portion thereof with a graphical user interface

Various embodiments comprise systems and methods for operating a data monitoring system to branch data models. In some examples, a data monitoring system maintains a series of models for a data stream. The data monitoring system adds a reference pointer to a position in the series of models. The data monitoring system generates a set of branch models for the data stream and appends the set of branch models to the series of models at the reference pointer. The data monitoring system compares ones of the set of branch models with corresponding ones of the series of models and generates test results based on the comparison. The data monitoring system reports the test results.

Branching data monitoring watchpoints to enable continuous integration and continuous delivery of data

Various embodiments comprise systems and methods to maintain data consistency in a data pipeline. In some examples, a computing system comprises data monitoring circuitry that monitors the operations of the data pipeline. The data pipeline receives input data, processes the input data, and generates output data. The data monitoring circuitry receives and processes the output data sets to identify changes between the output data sets. The data monitoring circuitry generates a consistency score based on the changes that indicates a similarity level between the output data sets. The data monitoring circuitry determines when the consistency score exceeds a threshold value. When the consistency score exceeds the threshold value, the data monitoring circuitry generates and transfers an alert that indicates ones of the output data sets that exceeded the threshold value.

Consistency monitoring of data in a data pipeline

A data pipeline monitoring system configured to monitor operations of a data pipeline. In the data pipeline monitoring system, data processing circuitry receives a training data set, processes the training data set, and responsively generates a data standard that indicates a preferred data format. The data pipeline receives an input data set, processes the input data set, responsively generates the output data set, and transfers the output data set to the data processing circuitry. The data processing circuitry receives an output data set from the data pipeline. The data processing circuitry determines similarities between the output data set and the data standard. The data processing circuitry scores the output data set based on the similarity between the output data set and the data standard and reports the score.

System and method for automatic data consistency checking using automatically defined rules

Various embodiments comprise systems and methods to sample data outputs of a data pipeline. In some examples, data monitoring circuitry monitors the data pipeline wherein the data pipeline receives an input data set, processes the input data set, and responsively generates and transfers an output data set. The data monitoring circuitry ingests the output data set, determines an amount of available computing resources, and selects an amount of the values from the output data set based on the amount of available computing resources. The data monitoring circuitry generates a quality score for the selected values based on a data quality, generates a confidence score based on the amount of the selected values, and reports the quality score and the confidence score.

Elastic data sampling in a data pipeline

Various embodiments comprise systems and methods to monitor operations of a data pipeline. In some examples, a data pipeline receives data inputs, processes the data inputs, and responsively generates and transfers data outputs. Data monitoring circuitry monitors the operations of the data pipeline circuitry, identifies an input change between an initial one of the data inputs and a subsequent one of the data inputs, and identifies an output change between an initial one of the data outputs and a subsequent one of the data outputs. The data monitoring circuitry correlates the input change to the output change, determines a quality threshold for the output change based on the correlation, and determines when the output change falls below the quality threshold. When the output change falls below the quality threshold, the data monitoring circuitry generates and transfers an alert that indicates the input change and the output change.

Data lineage in a data pipeline

Various embodiments comprise systems and methods to indicate when errors occur in a data pipeline. In some examples, data monitoring circuitry monitors the operations of a data pipeline. The data monitoring circuitry ingests an output data set generated by the pipeline, compares the output data set to an expected output, identifies differences between the output data set and the expected output, and determines when the magnitude of the difference exceeds an error threshold. When the error threshold is exceeded, the data monitoring circuitry generates a graphical representation of the output data set, a graphical representation of the expected pipeline output, and an animated transition from the graphical representation of the expected pipeline output to the graphical representation of the output data. The data pipeline monitoring circuitry transfers an alert that comprises the graphical representation of the expected pipeline output, the graphical representation of the output data, and the animated transition.

Visual alert generation in a data pipeline environment

Various embodiments include a data monitoring system that monitors the operations of a data pipeline. The data monitoring system receives a call from the data pipeline to ingest unprocessed data. The data monitoring system generates metadata based on the unprocessed data and responsively computes expected data outputs. The data monitoring system receives a call from the data pipeline to ingest processed data that comprises actual data outputs generated by the data pipeline. The data monitoring system generates output metadata based on the processed data. The data monitoring system compares the metadata for the expected data outputs with the output metadata for the actual data outputs and determines when the expected data outputs do not align with the actual data outputs. When the expected data outputs do not align with the actual data outputs, the data monitoring system generates and transfers an alert signifying the non-alignment.

Systems and methods for data quality monitoring

Systems and methods for data quality monitoring are provided. Various embodiments include a data monitoring system that integrates into a data pipeline. The data monitoring system may receive a call from the data pipeline to analyze data inputs entering the data pipeline. The monitoring system can generate metadata describing the data inputs and compare the generated metadata with previously generated metadata to determine if the data inputs are historically consistent. The data monitoring system may return a consistency measure to the data pipeline. In further embodiments, the data monitoring system can generate metadata describing data outputs from the data pipeline and compare the output metadata to previously generated output metadata. In further embodiments, the data monitoring system may operate as a read only entity in a database. The monitoring system may monitor for changes in the database and determine when adverse changes occur in the database.

A replica control system includes software to control replication in virtual environments. The replica control system identifies a plurality of data blocks within an underlying storage volume in response to a request to update a replica of a target storage volume, identifies changed data blocks of the plurality of data blocks within the underlying storage volume, and identifies a subset of the changed data blocks with which to update the replica of the target storage volume based on a characteristic of the changed data blocks.

Data replica control

Techniques for facilitating electronic discovery (eDiscovery) data storage in a backup environment are disclosed. In one particular embodiment, the technique(s) may be realized as a method of operating a computing system to facilitate electronic discovery (eDiscovery) data storage in a backup environment. The method may comprise storing, using electronic storage, a backup data set associated with an organization, processing, using at least one computer processor, the backup data set to extract metadata associated with data items in the backup data set, processing the metadata to identify a subset of the data items that are associated with eDiscovery, and generating an index of the metadata that identifies the subset of the data items in the electronic storage that are associated with the eDiscovery.

Techniques for leveraging a backup data set for eDiscovery data storage

Embodiments disclosed herein provide systems and method for storing metadata to unused portions of a virtual disk file. In a particular embodiment, a method provides selecting a virtual disk file stored on a data storage volume and identifying unused portions of the virtual disk file. The method further provides writing metadata for the virtual disk file in the unused portions of the virtual disk file.

Metadata storage in unused portions of a virtual disk file

Techniques for operating a computing system to facilitate visualization of a backup environment are disclosed. In one particular embodiment, the techniques may be realized as a method of operating a computing system to facilitate visualization of a backup environment. The method may comprise performing a backup service for an organization and rendering a graphical user interface that presents a plurality of views of the backup environment for the organization on a sub-organizational basis.

Techniques for presenting views of a backup environment for an organization on a sub-organizational basis

Embodiments disclosed herein provide systems and methods for performing garbage collection in virtual environments. In a particular embodiment, a method provides performing a garbage collection process to identify at least a portion of a secondary storage volume located within a primary storage volume. The method further provides triggering a data block release process on at least a portion of the primary storage volume corresponding to the at least a portion of the secondary storage volume.

Growing community of inventors

Somerville, MA, United States of America

J Mitchell Haile