Abstract: Embodiments are described for a data processing tool configured to cease operations of a plurality of database readers when detecting a congestion condition in the data processing tool. In some embodiments, the data processing tool comprises a memory, one or more processors, and a plurality of database readers. The one or more processors, coupled to the memory and the plurality of database readers are configured to determine a congestion condition in at least one data pipeline of a plurality of data pipelines of the data processing tool. Each data pipeline of the plurality of data pipelines connects a database reader and a transformer of the data processing tool, a transformer and a database writer of the data processing tool, or two transformers of the data processing tool. The one or more processors are further configured to refrain from reading data from one or more databases responsive to the congestion condition.

Abstract: Disclosed herein are system, method, and computer program product embodiments for allowing a software application subject to restrictions on table or column names work with a database management system (DBMS). An embodiment operates by determining that a table name or one or more column names of a table used in the DBMS violate one or more predefined rules for the software application. In response to the determining, the embodiment then creates a view for the table such that a view name of the view or the one or more column names of the view satisfy the one or more predefined rules for the software application.

Abstract: Disclosed herein are system, method, and computer program product embodiments for performing deterministic execution of background jobs in a load-balanced system. An embodiment operates by receiving, at a work server in a load-balanced system, job submission code from a client connected to the work server, wherein the job submission code performs a background job for the client. The embodiment then executes, at the work server, the job submission code. The execution of the job submission code obtains a name of the work server executing the job submission code, maps the name of the work server to a logical server name, and submits the background job for background processing using a job processing function that executes the background job on the logical server name.

Abstract: A computer implemented system and method of performing parallel load operations during an extract, transform, load (ETL) operation. The system generates a single transaction having multiple processes, where each process loads into one target table. The processes are executed in parallel, and the system performs verification on the single transaction.

Abstract: Some embodiments include a system, method, and non-transitory medium, with the method including receiving, from a user, values for a set of usage-related parameters for one or more cloud services; determining, based on historical time series data for the one or more services provided by at least one cloud service provider, a predicted trend of future values for the set of usage-related parameters for the one or more cloud services for the at least one cloud service provider; evaluating the predicted trend of future values for the set of usage-related parameters for the one or more cloud services for each of the at least one cloud service providers to generate a recommendation that best corresponds, at least within a specified threshold, to the values for the set of usage-related parameters received from the user; and storing an indication of the generated recommendation in a data storage device.

Abstract: Embodiments leverage local data available from an on-premises Extract, Transfer, and Load (ETL) job, in order to efficiently perform remote processing (e.g., as implemented on the cloud). Connectivity data (e.g., target setup) and ETL logic (e.g., configuring data flattening, pivot transform, and/or data quality transform) is stored locally in a non-transitory computer readable storage medium. In response to receiving data transformed on-premises, the transformed data, connectivity data, and ETL logic are forwarded to a remote location for processing. Some embodiments may also forward the transformed data on to its original target on-premises (e.g., via a local database loader). Particular embodiments may provide hidden, Representational State Transfer (REST)-based loader(s) that duplicate output of the local ETL job. Embodiments conserve developer effort by allowing preparatory local ETL data that is already available on-premises, to be read and used for processing in a remote cloud intelligence system.

Abstract: Disclosed herein are system, method, and computer program product embodiments for managing data storage devices. In some embodiments, a server receives a request to store data in a first storage device. The server determines the storage space remaining in the first data storage device based on historical data associated with the first data storage device. The server further determines that the first data storage device will exceed its storage capacity based on a size of the data and the storage space remaining in the first data storage device. The server transfers a set of data stored in the first data storage device to a second storage device. Transferring the set of data causes the storage space remaining in the first storage device to be greater than or equal to the size of the data. The server stores the data in the first data storage device.

Abstract: Technologies described herein can be used to generate and distribute neural network models and executable code using feedback data received from one or more client computing devices. A neural network model can be generated by a server computer. Executable code can also be generated by the server that, when executed by a client computing device, causes the client device to generate a prediction using the neural network model. The server can transmit the model and code to one or more client computing devices. The server can receive feedback data from the client device(s) based on predictions generated by the client device(s) using the neural network model and the executable code. The server can generate an updated version of the neural network model and/or an updated version of the executable code base on the feedback data, and can transmit the updated model and/or the updated code to the client device(s).

Abstract: A computer implemented system and method of performing parallel load operations during an extract, transform, load (ETL) operation. The system generates a single transaction having multiple processes, where each process loads into one target table. The processes are executed in parallel, and the system performs verification on the single transaction.

Abstract: Embodiments increase the efficiency of uploading data to a remote system. An expected volume of data for upload, is calculated prior to the actual load event. This calculation allows proactive allocation of resources (e.g., processing, memory, network, disk, others) available in the remote system, in order to accommodate the load being transferred. In certain embodiments, the calculation can be based upon historical data for previous load events. According to particular embodiments, the calculation can be based upon an estimated output data volume (such as parsing the output of EXPLAIN statement for PostgreSQL) or other information provided by data source(s) or an Extract-Transform-Load (ETL) tool. Some embodiments may perform the transfer by first staging data for upload in temporary table(s) on the side of the ETL tool, prior to actually loading to the remote system. In such embodiments, record count(s) can provide a precise value of expected data load volume.

Abstract: A computer implemented system and method of performing parallel load operations during an extract, transform, load (ETL) operation. The system generates a single transaction having multiple processes, where each process loads into one target table. The processes are executed in parallel, and the system performs verification on the single transaction.

Abstract: A computer implemented system and method of performing parallel load operations during an extract, transform, load (ETL) operation. The system generates a single transaction having multiple processes, where each process loads into one target table. The processes are executed in parallel, and the system performs verification on the single transaction.

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: Embodiments of the present disclosure pertain to accessing computing resources. In one embodiment, the present disclosure includes a computer implemented method comprising storing at least one credential for accessing a first system, storing a plurality of user credentials for a plurality of users having access to a second system, linking the plurality of user credentials for the plurality of users having access to the second system to the at least one credential for accessing the first system, receiving a first user credential for a first user from the second system over a first connection, authenticating the first user credential, wherein the first user credential is authenticated when the first user credential matches one of the stored plurality of user credentials, and establishing a second connection between the first system and the second system using the at least one credential when the first user credential is authenticated.

Abstract: Embodiments of the present disclosure pertain to a personal assistant controller. In one embodiment, the present disclosure includes a computer implemented method comprising receiving a voice audio signal in the personal assistant controller, converting the voice audio signal into a target command corresponding to one of a plurality of personal assistants, wherein different personal assistants comprise different target command protocols for executing different operations on different network enabled appliances, and sending the target command for execution by a backend system corresponding to the one of the plurality of personal assistants, and in accordance therewith, performing an operation on the backend system.

Abstract: Technologies described herein can be used to generate and distribute neural network models and executable code using feedback data received from one or more client computing devices. A neural network model can be generated by a server computer. Executable code can also be generated by the server that, when executed by a client computing device, causes the client device to generate a prediction using the neural network model. The server can transmit the model and code to one or more client computing devices. The server can receive feedback data from the client device(s) based on predictions generated by the client device(s) using the neural network model and the executable code. The server can generate an updated version of the neural network model and/or an updated version of the executable code base on the feedback data, and can transmit the updated model and/or the updated code to the client device(s).

Abstract: Disclosed herein are system, method, and computer program product embodiments for a an ETL (extract-transform-load) system. An embodiment operates by receiving, at a processor, a message including a request to move data from a source database to a target database. The data is retrieved from the source database. One or more operations to perform on the data that convert the data from a source format associated with the source database to a target format associated with the target database are determined from the message. The one or more operations are executed on the data. The data is stored on the target database in the target format.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a transaction from a transaction source. Based on a set of rules configured for processing transactions, the program further determines a blockchain from a plurality of blockchains stored in memory of the device. The program also records the transaction to the determined blockchain in the memory of the device.

Abstract: Disclosed herein are system, method, and computer program product embodiments for a secured cloud storage system. An embodiment operates by receiving a compressed file comprising data that has been compressed using a compression algorithm. The compressed data is divided into a plurality of separate files. A password for each of the separate files is determined. Each of the separate files is encrypted with its corresponding password. The encrypted files are stored across a plurality of servers.