Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments of the present technology relate to data monitoring systems to generate multi-layered histograms. In some examples, the data monitoring system comprises a computing device that stores an executable modeling component. The modeling component, in response to execution, reads a data record associated with a data pipeline and models the data record as a histogram. The histogram comprises histogram buckets that categorize data values of the data record. The modeling component scans the histogram buckets and determines when a proportion of the data values assigned to one of the histogram buckets exceeds a threshold value. When the threshold value is triggered, the modeling component models the data values assigned to the exceeding histogram bucket as a subsidiary histogram. The subsidiary histogram comprises subsidiary histogram buckets that categorize the data values assigned to that histogram bucket.

Abstract: 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.

Abstract: Various embodiments comprise systems and methods to determine output attributes of a data pipeline. In some examples, a data pipeline monitoring system retrieves historical data generated by a data pipeline and determines generation dates for the historical outputs. The system identifies one or more attributes of the historical data outputs. The system generates an output model that indicates expected output attributes based on the identified attributes of the historical outputs. The system generates an error threshold based on the model and applies the error threshold to outputs generated by the data pipeline. The system generates alerts when the outputs trigger the error threshold.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments comprise systems and methods to determine entropy in strings generated by a data pipeline. In some examples, data monitoring circuitry monitors a data pipeline that ingests input data, processes the input data, and responsively generates and transfers a data string that comprises character groups. The data monitoring circuitry receives the data string, identifies character groups in the data string, identifies group types for the character groups, and assigns numbers to the character groups based on the group types. The data monitoring circuitry determines a probability distribution for the numbers, calculates entropy for the data string based on probability distribution, and generates an entropy histogram based on the entropy. The data monitoring circuitry compares the entropy histogram of the data string to another entropy histogram for another data string, determines a change in entropy, and reports the change in entropy.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.